Pandemic Darlings The pandemic economy, in original documents
Home Court filings Bofa Ca Unemployment In re: Bank of America California Unemployment Benefits Litigation — S.D. Cal., No. 21-md-02992 Exhibit PX10 — In re Bank of America California Unemployment Benefits Litigation (Dkt. 634-11, S.D. Cal. No. 3:21-md-02992)

Court filing

Exhibit PX10 — In re Bank of America California Unemployment Benefits Litigation (Dkt. 634-11, S.D. Cal. No. 3:21-md-02992)

Filed January 9, 2026 in In re Bank of America California Unemployment Benefits Litigation; one of 1415 filings from this case.

Record facts

CourtU.S. District Court for the Southern District of California
Filed2026-01-09

U.S. District Court for the Southern District of California · No. 3:21-md-02992-GPC-MSB · Doc. 634-11 · 2026-01-09 · Docket on CourtListener

Full text

PX 10 
 
 
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UNITED STATES DISTRICT COURT 
SOUTHERN DISTRICT OF CALIFORNIA 
IN RE BANK OF AMERICA  
CALIFORNIA UNEMPLOYMENT 
BENEFITS LITIGATION 
Case No. 3:21-md-02992-GPC-MSB 
EXPERT REBUTTAL REPORT OF NATALIE LOEBNER 
April 4, 2025 
REDACTED PUBLIC VERSION
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i 
TABLE OF CONTENTS 
I. 
ASSIGNMENT .......................................................................................................... 1 
II. 
INFORMATION CONSIDERED ........................................................................... 1 
III. 
HOURLY RATE ....................................................................................................... 1 
IV. 
QUALIFICATIONS .................................................................................................. 2 
V. 
SUMMARY OF FACTUAL BACKGROUND ...................................................... 5 
VI. 
SUMMARY OF OPINIONS .................................................................................... 7 
VII. 
THE BANK FAILED TO CONSIDER LIKELY SCENARIOS WHERE CFF-1 
CLAIMS WOULD HAVE BEEN LEGITIMATE, THUS IMPLEMENTING 
OVERLY BROAD AUTOMATED CLAIM DENIALS. ...................................... 9 
A. IN DEVELOPING AND IMPLEMENTING CFF-1 AUTOMATION, THE BANK FAILED TO 
CONSIDER WELL-KNOWN SITUATIONS SUCH AS CARD SKIMMING—A PERVASIVE 
CRIME WHERE A CARDHOLDER’S PIN IS COMPROMISED, ENABLING 
UNAUTHORIZED CASH WITHDRAWALS FROM AN ATM—WHERE A 
CLAIMANT/CARDHOLDER WOULD BE A TRUE VICTIM. ........................................ 12 
B. ALTHOUGH THERE MAY BE A CORRELATION BETWEEN FRAUD AND ATM CASH 
WITHDRAWALS, IT WAS UNREASONABLE FOR THE BANK TO USE THIS 
CORRELATION ALONE IN DEVELOPING CFF-1. ................................................... 16 
C. THE BANK ACTED UNREASONABLY IN FAILING TO INCORPORATE ANY OF ITS 
VARIOUS AVAILABLE TOOLS AND METHODS USED IN OTHER CONTEXTS TO 
INFORM CFF-1 AND ASSESS WHETHER A CARDHOLDER-CLAIMANT WAS IN FACT A 
LEGITIMATE VICTIM. .......................................................................................... 20 
VIII. 
PESCE AND CRONAN’S OPINIONS ABOUT THE REASONABLENESS 
OF THE BANK’S AUTOMATED FRAUD DETECTION SYSTEMS IS 
FLAWED AS THE RECORD REFLECTS THE BANK MAINTAINED A 
FLAWED APPROACH TO DEPLOYING AND IMPLEMENTING THE CFF 
AUTOMATION. .................................................................................................... 24 
A. THE BANK’S HEAVY RELIANCE ON RECONSIDERATION RATES TO MEASURE CFF 
AUTOMATION’S EFFECTIVENESS WAS A FLAWED APPROACH AND  
UNREASONABLE................................................................................................. 26 
B. THE BANK’S RELIANCE ON RECONSIDERATION RATES WAS UNREASONABLE 
BECAUSE IT FAILED TO CONSIDER CFF’S ACTUAL IMPACT ON THOSE RATES. ..... 30 
C. THE BANK’S FAILURE TO EMPLOY ADDITIONAL GENERALLY ACCEPTED 
PROACTIVE STRATEGIES, SUCH AS SAMPLING, SURVEYS, OR INTENTIONAL 
FEEDBACK TO MEASURE CFF AUTOMATIONS’ EFFECTIVENESS WAS 
UNREASONABLE................................................................................................. 33 
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D.
THE BANK’S INACCURATE DOCUMENTATION REGARDING THE CFF-1
AUTOMATION FURTHER HIGHLIGHTS THAT THE BANK’S OVERALL
APPROACH TO IMPLEMENTING CFF WAS NOT REASONABLE. ............................. 38 
IX.
THE BANK DID NOT REASONABLY RESPOND TO THE REVERSING
OF CARDHOLDERS’ PERMANENT CREDITS BASED ON CFF-1. .............. 42 
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1 
 
I. 
ASSIGNMENT 
1. 
I have been retained by Plaintiffs’ counsel in connection with In re 
Bank of America California Unemployment Benefits Litigation, Case No. 3-12-md-
02992-LAB-MSB. I was asked to address certain opinions and assumptions 
presented in the Expert Report of Russell Cronan, dated March 4, 2025 (“Cronan 
Expert Report”), and the Expert Report of Teresa A. Pesce, dated March 4, 2025 
(“Pesce Expert Report”), regarding: 
A. The reasonableness of Bank of America, N.A.’s (“Bank’s”) use of Claim 
Fraud Filter Indicator 1 (“CFF-1”) to summarily deny customer-initiated 
unauthorized transaction claims, and  
B. The reasonableness of the Bank’s retroactive application of CFF-1 to 
overturn its approvals of previously investigated claims. 
II. INFORMATION CONSIDERED  
2. 
In addition to my own knowledge, training and experience, I relied on 
case-related materials provided to me by Plaintiffs’ counsel.  These materials are 
listed in Appendix A of this report. I reserve the right to supplement or amend my 
report should new information be provided to me.  
III. HOURLY RATE 
3. 
Plaintiffs’ counsel retained me at the hourly rate of $900 per hour, 
which is not contingent on my opinions or the outcome of this case.  
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IV. QUALIFICATIONS 
4. 
I am the principal of Loebner Consulting, LLC, a limited liability 
company based in Virginia that I founded in 2023. Through Loebner Consulting, I 
primarily offer subject-matter expertise to the federal government in complex 
financial investigations. My work is focused on software development, automation 
implementation, and leveraging data analytics to improve investigative processes. 
This work includes coordinating with teams that develop and implement rules-
based systems and/or data models to identify unlawful conduct in pursuing 
criminal or civil investigations. Additionally, I advise private sector clients 
supporting financial crimes investigations through software development and/or 
data analytical services.  
5. 
Prior to founding my own company, I offered similar consultation 
services through my previous employer, Eastport Analytics, Inc. I have over 
five years of experience consulting with federal law enforcement and government 
agencies.  
6. 
Apart from my consulting work, I have extensive experience in 
financial investigations (criminal, civil fraud, and compliance), gained in part 
through my time as a Special Assistant United States Attorney (SAUSA) and as a 
Trial Attorney for the Department of Justice (DOJ) Tax Division. For example, in 
2013, as a SAUSA in the Southern District of Alabama, I led an interagency 
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financial crimes task force shortly after the issuance of the Tax Division Directive 
144 (issued September 18, 2012), which addressed Stolen Identity Refund Fraud 
(SIRF)1 and increased exploitation of prepaid bank cards. As part of this effort, I 
coordinated with over 20 state and federal agencies and liaised with various U.S.-
based financial institutions.  From 2015 to 2018, I served as an Electronically 
Stored Information Coordinator for the DOJ Tax Division, supporting efforts to 
improve discovery requests issued to financial institutions with the aim of 
obtaining more compliant and useful responses from the institutions’ core systems. 
I also provided training to DOJ attorneys on evaluating transactional data obtained 
from relational databases.  
7.
Private sector companies and law enforcement agencies, foreign and
domestic, have relied upon my expertise—including my knowledge and experience 
concerning the exploitation of payment systems and eCommerce platforms by 
criminal actors—to better understand and investigate financial crimes. Based on 
my expertise, in the past two years I have made numerous presentations as a 
thought leader in my field, including at the Joint Chiefs of Global Tax 
Enforcement (the J5 Initiative), which is dedicated to combatting transnational tax 
1 U.S. Attorneys’ Manual | 36. Tax Division Directive 144 (September 18, 2012)—Temporary 
Delegation of Authority to Authorize Grand Jury Investigations, Criminal Complaints, and 
Seizure Warrants for Certain Offenses Arising from Stolen Identity Refund Fraud | United States 
Department of Justice, https://www.justice.gov/archives/usam/tax-resource-manual-36-tax-
directive-no-144  
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crime through increased enforcement collaboration, as well as to the Association of 
Certified Anti-Money Laundering Specialists (ACAMS) on organized retail crime 
and improving subpoenas to financial institutions. I have also led formal trainings 
with agents of the Internal Revenue Service (IRS), Federal Bureau of Investigation 
(FBI), Homeland Security Investigations (HSI), and the DOJ.  
8. 
I advise several non-profit organizations focused on combatting 
financial crime and advocating for financial crime victims in a volunteer capacity, 
including organizations such as the Aspen Institute, the Knoble, Operation 
Shamrock, and the Survivor Inclusion Initiative.  
9. 
I hold a Bachelor of Arts from Whitman College in Pure Mathematics 
as well as Rhetoric and Film Studies. As part of my mathematics degree, I studied 
and became familiar with computer coding, data modeling, and machine 
learning—skills I have since applied in my work with governmental and private 
entities combating financial crimes. 
10. 
I received both a J.D. and LLM in Taxation from the University of 
Washington School of Law. 
11. 
My resume, attached as Appendix B, has additional details and dates 
about my previous positions and professional experience. 
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V. SUMMARY OF FACTUAL BACKGROUND2 
12. 
I understand that the Bank issued prepaid debit cards funded by the 
State of California’s Employment Development Department (“EDD”) prior to and 
during the COVID-19 pandemic. I further understand that the Bank was 
responsible by contract and statute to respond to unauthorized-transaction claims 
asserted by such cardholders, including claims that funds had been stolen from 
them through unauthorized use at an Automated Teller Machine (ATM). 
13. 
It is my understanding that the Bank implemented a Claim Fraud 
Filter (“CFF”) that, beginning on September 28, 2020, was applied to 
automatically deny cardholder unauthorized-transaction claims.  
14. 
I understand the Bank’s CFF had three different indicators of potential 
fraud: 
A. 
Indicator 1 (“CFF-1”). An EDD cardholder makes an unauthorized 
transaction claim where at least one transaction is a PIN-enabled 
ATM cash withdrawal. No other facts were considered by the Bank 
before automatically denying claims that triggered CFF-1. 
B. 
Indicator 2 (“CFF-2”). 
 
. 
 
2 This factual background is based on my review of documents and dispositions listed in 
Appendix A and other documents cited herein and is informed by my experience in working in 
financial fraud and criminal cases and investigations.  
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C. 
Indicator 3 (“CFF-3”). 
 
 
: 
(1)  
; 
(2)  
; 
(3)  
; 
(4)  
; or 
(5)  
 
cardholders share the same address (physical or email).3 
15. 
I understand that an EDD cardholder’s unauthorized-transaction claim 
could trigger more than one indicator but that the relevant population for this case 
are those cardholders whose claims triggered CFF-1 only. 
16. 
I understand that from September 28, 2020, through June 8, 2021, all 
unauthorized-transaction claims that triggered CFF-1 only were summarily denied; 
that prior to March 18, 2021, the associated accounts were automatically frozen; 
and that from March 18, 2021, through June 8, 2021, the associated accounts were 
automatically blocked.  
17. 
I understand that the Bank automatically rescinded permanent credits 
for EDD cardholder unauthorized-transaction claims that triggered CFF-1 only that 
were initiated on or after April 1, 2020, and before September 28, 2020.  
 
3 BANA_EDD_MDL_00090640 
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VI. SUMMARY OF OPINIONS 
18. 
I disagree with Cronan’s and Pesce’s opinion that the Bank’s use of 
CFF-1 to summarily deny an EDD cardholder claim based solely on the fact that at 
least one of the unauthorized transactions was a PIN-enabled ATM cash 
withdrawal (i.e., based on the Bank’s implementation of its automated CFF-1) was 
reasonable for the following reasons: 
A. In automatically denying unauthorized-transaction claims based solely on 
CFF-1, the Bank failed to account for well-known, common scenarios in 
which an EDD cardholder’s unauthorized-transaction claim would have 
been legitimate. By not considering these scenarios, the Bank failed to 
consider customer/cardholder victims as required by their regulators. 
B. The Bank was unreasonable in its characterization that any cash 
withdrawal at an ATM was inherently suspect and basing the 
development of CFF-1 on that unreasonable premise was flawed.  
C. The Bank was unreasonable in its failure to consider or implement other 
tools it had available to address card skimming.  
19. 
I disagree with Cronan’s and Pesce’s opinion that the deployment and 
methods to measure CFF-1’s effectiveness were reasonable. The Bank did not have 
or implement an appropriate plan to measure the actual effectiveness of CFF-1 for 
the following reasons: 
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A. The Bank’s 
 to 
measure the effectiveness of CFF-1 auto-denials was flawed because the 
metric was not reasonably representative of the impacted cardholder 
population. 
B. The Bank’s stated 
 was woefully 
inadequate because the Bank did not include sufficient details to review 
the metric during initial months CFF-1 was deployed.  
C. The Bank unreasonably failed to leverage any proactive metric, like 
sampling or surveys, to ensure CFF-1 was effective and reasonably 
tailored given the size of the impacted cardholder population.  
D. The Bank’s documentation on its implementation of CFF-1 did not 
consistently and clearly describe the automation and decisioning process, 
and, therefore, failed to adhere to the documentation requirements for 
large banks. 
20. 
The Bank’s retroactive denial of previously approved and investigated 
unauthorized-transactions claims by EDD cardholders based solely on CFF-1 was 
not reasonable. The Bank applied an overly broad rule, failed to except previously 
decisioned cardholders from denials, and responded unreasonably when the Bank 
learned previously investigated cardholders had their permanent credits reversed.  
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21. 
It is my opinion that the Bank’s above-described failures and 
oversights, when viewed collectively, demonstrate that the Bank’s design, 
implementation, and methods to measure CFF-1’s effectiveness were woefully 
inadequate and an extraordinary departure from well-established standards.  
VII. CRONAN’S AND PESCE’S OPINION THAT THE BANK’S USE OF 
CFF-1 AS A MECHANISM FOR AUTOMATICALLY DENYING 
UNAUTHORIZED-TRANSACTION CLAIMS WAS REASONABLE IS 
WRONG. 
22. 
Cronan opines that a reasonable investigation can “include automated 
features” to “aid or support an investigation.”4  Cronan goes on to say that he is 
“not aware of any specific prescriptive elements about of the nature of the 
investigation to be conducted, including any specific prescriptions about the use of 
automated features in fraud strategies.”5 Pesce similarly opines that “the Bank 
acted reasonably in devising an automated fraud detection solution” which she 
describes as “
.”6 While automated features are not uncommon 
fraud strategies, the strategies banks implement must be reasonably tailored and 
effective. Banks must take care at every stage—developing, deploying, and 
measuring the effectiveness—of any automation they implement. For the reasons 
 
4 Cronan Expert Report ¶ 30. (“Cronan Rep”) 
5 Cronan  Rep. ¶33. 
6 Pesce Expert Report ¶ 56 (“Pesce Rep.”); see also id. ¶ 53 (opining that “the fraud filter was a 
reasonable attempt to expeditiously address a growing fraud problem”). 
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stated below, with respect to CFF-1, it is my opinion that the Bank was not only 
unreasonable at each stage but contrary to industry standards when considered 
collectively.  
23. 
Pesce and Cronan present a fundamentally flawed assessment of the 
reasonableness of the Bank’s use of CFF-1. They cite the Office of the Currency 
Comptroller of the Currency’s (“OCC”) Bulletin 2019-377 in an effort to support 
their opinions, but they fail to consider some of that Bulletin’s more critical 
guidance when reaching their ultimate conclusions. Notably, the OCC in its 
bulletin highlights the different types of fraud (internal, external, third-party) and 
victims of fraud (a customer/cardholder, a bank, or a client) that banks must 
consider and account for when developing their fraud strategies.8 Despite its 
awareness of this reality,9 the Bank crafted a rule in the form of CFF-1 that 
categorically and unreasonably precluded the Bank from concluding that 
cardholders who complained about having money stolen from them through an 
unauthorized ATM transaction were telling the truth and that they were in fact 
 
7 Office of the Comptroller of the Currency, OCC Bulletin 2019-37: Operational Risk: Fraud 
Management Principals (July 24, 2019), https://www.occ.treas.gov/news-
issuances/bulletins/2019/bulletin-2019-37.html  
8 Id.  
9 It is my understanding that the Bank incorporated the OCC’s guidance in the form of internal 
standards instructing employees of their responsibility to protect cardholders from the three 
forms of fraud (e.g., internal, external, and third-party. See William Martin Deposition (Martin 
Tr.)150:1–153:24. 
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legitimate victims. In my opinion, this is not reasonable, especially because the 
Bank’s regulator’s guidance expressly calls out only three types of victims the 
Bank needed to consider, and the Bank only considered two when developing 
CFF-1. 
24. 
Pesce’s and Cronan’s contention that the Bank’s creation and 
implementation of CFF-1 was reasonable and appropriate rests upon a flawed data 
analysis that improperly relies on, and overemphasizes, a potential correlation 
between fraud and ATM cash withdrawals.10 Further, their reports fail to explain 
why the Bank acted reasonably in failing to incorporate any of the available anti-
fraud tools and methods that it used in other contexts to assess whether cardholder 
claimants were in fact victims of fraud.  
25. 
The development of CFF-1 failed to consider well known scenarios 
where cardholders could be victims, a lopsided development for an automation to 
decision unauthorized-transaction claims. CFF-1’s deployment was hurried, and 
the Bank failed to leverage well known strategies to prevent errors and confusion. 
Measurement of the effectiveness of the automation was woefully inadequate, 
providing the Bank with insufficient visibility into the impact and effectiveness of 
the automation.  
 
10 Cronan Rep., ¶48, 49, Pesce Rep., ¶56. 
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26. 
For the reasons stated below, CFF-1 was not a reasonable use of an 
automation as it fell woefully short of meeting industry standards to be reasonably 
tailored to minimize collateral damage to innocent cardholders which the Bank 
knew about. In my opinion, the unreasonable approach by the Bank at each stage 
of the CFF-1 development, implementation, and effectiveness measurement 
demonstrates this was a flawed automation from start to finish. 
A. In developing and implementing CFF-1 automation, the Bank failed 
to consider well-known situations such as card skimming—a 
pervasive crime where a cardholder’s PIN is compromised, enabling 
unauthorized cash withdrawals from an ATM—where a claimant 
cardholder would be a true victim. 
27. 
Cronan and Pesce state in their reports that in their experience, banks 
use automations to support a wide variety of functions, including making otherwise 
manual efforts more streamlined, often by reducing or eliminating employee 
judgment and discretion from the decision-making process.11 In my experience, it 
is highly problematic to rely on automations that are not reasonably tailored for the 
specific use case. Assessing whether CFF-1—or any rule—is reasonably tailored to 
achieve its desired objective requires assessing that rule’s underlying 
presumptions. By categorically denying any unauthorized-transaction claim based 
on a PIN-enabled ATM cash withdrawal, CFF-1 treats such claims as inherently 
 
11 Cronan Rep., ¶16, Pesce Rep., ¶¶23, 24, and 55.  
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fraudulent. Cronan and Pesce both claim that this inherent conclusion of fraudulent 
conduct is reasonable. Their opinion, however, ignores that many such claims are 
valid and that the relevant claimant-cardholders are in fact victims of fraud. The 
awareness of card skimming and other legitimate claims related to unauthorized 
ATM transactions is demonstrated, for example, by the inclusion of 
 
 
 
12 
28. 
One of the more obvious and notorious examples of a valid 
unauthorized-transaction claim in which EDD funds were stolen from a cardholder 
at an ATM involves card-skimming. Based on my experience supporting financial 
crime investigations, I am familiar with card skimming operations. Card skimming 
occurs when criminals compromise payment terminals or ATMs by installing card-
reading devices. The cardholder’s PIN can be compromised using different 
techniques, including pinhole cameras or keypad overlays. Criminals use 
information captured by a skimming device to generate a duplicate or cloned card 
to make unauthorized purchases or to make ATM withdrawals using the 
cardholder’s compromised PIN. Card skimming has become so frequent an 
occurrence that the FBI, U.S. Secret Service, and other law enforcement agencies 
 
12 BANA_EDD_MDL-00004996 (
 
 
).  
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and regulators have issued public warnings and brought numerous card skimming 
cases.13 These compromises have been known to occur at ATMs, gas stations, and 
other locations with publicly accessible terminals accepting debit cards with PIN 
input.14 PIN inputs are generally required at non-ATM terminals where the card or 
terminal is not EVM chip-enabled.  
29. 
Based on my experience engaging with financial institutions, card 
skimming is a risk with which banks issuing debit and prepaid cards are familiar, 
both before and during the COVID-19 pandemic. Financial institutions reduce this 
risk by implementing processes and procedures to detect and mitigate the risk of 
card skimming. Common measures to identify and mitigate card skimming risks 
include: (1) monitoring transactions to detect point-of-sale (POS) terminal usage 
 
13 The FBI Scams and Safety webpage, https://www.fbi.gov/how-we-can-help-you/scams-and-
safety/common-frauds-and-scams/skimming, cautions the public on the various techniques used 
to compromise cardholder PINs. Similarly, the U.S. Secret Service webpage, 
https://www.secretservice.gov/investigations/skimming, states the Service investigates hundreds 
of cases a year and advises that PIN compromise occurs at a variety of common merchants like 
pharmacies, gas stations, and grocery stores. The webpage also highlights the recommendation to 
use chip enabled cards to reduce the exposure of the cardholder’s PIN as merchant locations. In 
2018, the Federal Trade Commission (FTC) issued a consumer alert related to card skimming, 
https://consumer.ftc.gov/consumer-alerts/2018/08/watch-out-card-skimming-gas-pump. The 
Office of the Comptroller of the Currency (OCC) identifies advances in card skimming 
technology in its Fiscal Year 2018 Bank Supervision Operating Plan, https://www.occ.gov/news-
issuances/news-releases/2017/nr-occ-2017-113a.pdf, and 2019 Fall Semiannual Risk 
Perspective, https://www.occ.gov/publications-and-resources/publications/semiannual-risk-
perspective/files/pub-semiannual-risk-perspective-fall-2019.pdf.   
14 United States Secret Service, U.S. Secret Service Serves up Cold Dish of Justice to Gas Pump 
Skimmers, U.S. Secret Service Media (November 22, 2018), 
https://www.secretservice.gov/press/releases/2018/11/us-secret-service-serves-cold-dish-justice-
gas-pump-skimmers (Press release regarding a nationwide initiative called Operation Deep 
Impact to combat the targeting of gas stations noting the identification of nearly 200 skimmers.) 
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that connects numerous cards to suspicious ATM withdrawal or purchase patterns; 
(2) physically inspecting ATMs to identify skimming devices; (3) proactively 
flagging transactions associated with compromised ATMs and POS terminals; and 
(4) leveraging skimming indicators placed on transactions to streamline transaction 
holds and cardholder engagement.15 
30. 
It is my opinion that the Bank was aware of and treated card 
skimming as a risk before and during the Bank’s deployment of CFF-1. My 
opinion is supported by records I reviewed in this case in which Bank employees 
. Further, the Bank was publicly highlighted in 
Department of Justice press releases during the relevant timeframe as either 
assisting law enforcement with card skimming investigations or otherwise 
acknowledging having customers as card-skimming victims.16 Nonetheless, the 
 
15 BANA_EDD_MDL-00057505 (Email from 
 
). 
16 A sampling of DOJ press releases illustrative of the prevalence of the risk before and during 
the Class Period: Twelve Individuals Charged in ATM Skimming Conspiracy (November 16, 
2016), https://www.justice.gov/archives/opa/pr/twelve-individuals-charged-atm-skimming-
conspiracy (a 2016 case where skimming devices were found at Bank of America locations.); 
Romanian Citizen Pleads Guilty in ATM Skimming Conspiracy (March 29, 2017), 
https://www.justice.gov/archives/opa/pr/romanian-citizen-pleads-guilty-atm-skimming-
conspiracy (a 2017 case identifying both Bank of America and PNC Bank customers as victims); 
Romanian National Sentenced for Multi-State ATM Card Skimming Scheme (June 3, 2019), 
https://www.justice.gov/archives/opa/pr/romanian-national-sentenced-multi-state-atm-card-
skimming-scheme (a 2019 case noting keypad overlays as one of the ways PINs were 
compromised); Seven Members and Associates of Large-Scale Gas Pump Skimming Device 
Organization Charged with Racketeering and Money Laundering Conspiracies (May 8, 2024), 
https://www.justice.gov/archives/opa/pr/seven-members-and-associates-large-scale-gas-pump-
 
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Bank failed to account for this scenario when implementing CFF-1 in its 
unauthorized-transaction claim process.  
31. 
Card-skimming is one of the more notable examples in which a 
cardholder-claimant would be the victim rather than the fraudster and is a situation 
of which the Bank was well aware. In my opinion, a reasonable automated process 
would have accounted for this real and well-known scenario; however, CFF-1 did 
not take this into account. 
B. 
Even if there was a potential correlation between fraud and an 
ATM cash withdrawal, it was unreasonable for the Bank to use 
this correlation alone in developing CFF-1. 
32. 
The Bank’s reliance on its CFF-1 as a stand-alone basis for denying 
unauthorized-transaction claims rests on the incorrect assumption that all 
unauthorized-transaction claims based on ATM/PIN cash transactions are 
inherently false and fraudulent. Both Cronan and Pesce assert that the Bank 
conducted a thoughtful analysis to determine “
” indicative of fraud by 
identifying a strong correlation between PIN-enabled ATM cash activity and 
fraudulent unauthorized-transaction claim behavior, and that the Bank used that 
analysis in developing and implementing its CFF-1.17  Pesce supports her 
 
skimming-device-organization-charged  (a 2024 case cover conduct from 2014 to 2024 involving 
large scale gas station skimming operations).  
17 Pesce Rep. ¶56 and Fn. 129; Cronan Rep.¶49. 
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conclusions in part with a statement from the Letson Declaration “
 
 
.”18  
33. 
While it likely is true that some claims by cardholders that benefits 
funds were stolen from them through an unauthorized ATM transaction were 
fraudulent, the question in this case as I understand it is whether that factor alone 
(i.e., CFF-1’s exclusive reliance on the fact that funds were allegedly stolen 
through a PIN-enabled ATM transaction as the basis for decision), is alone a 
reasonably sufficient indicator that the cardholder’s asserted claims was fraudulent. 
Even if there were some correlation between ATM use and cardholder fraud, it 
does not follow that this feature/characteristic definitively identifies invalid 
fraudulent claims. The potential correlation to fraud does not necessarily mean the 
claimant is the fraudster and in my opinion, based on experience in working with 
the government and institutions in developing fraud strategies, it is an insufficient 
basis for CFF-1 which by design would have denied all legitimate ATM claims.  
34. 
In an attempt to defend the contrary view, Pesce asserts that it would 
have been unusual for EDD debit card holders to convert their benefits 
distributions to cash at an ATM given the shift to cashless payments.19 In offering 
 
18 Pesce Rep. Fn. 129; Cronan Rep. Fn.45.  
19 Id. at ¶52 
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18 
that opinion, Pesce cites certain studies relating to national trends toward cashless 
payments. In my opinion, these studies do not support Pesce’s ultimate conclusion. 
However, these studies show an overall reduction in the use of cash in 2019 and 
2020 generally. They do not stand for the proposition that the use of cash by the UI 
benefits recipients at issue here is unusual or that the shift to cashless payments 
was so great that the use of cash should be categorically viewed as unusual. In fact, 
a subsequent study that specifically explored the Covid-19 timeframe notes that 
consumers use of cash increased in 2021 and that there was a higher correlation in 
the use of cash for households making under $150,000 and approximately a three 
times greater correlation for household making under $25,000.20 This contradicts 
Pesce’s claim that UI benefits recipients would not have converted their benefits 
distributions to cash. 
35. 
Moreover, based on my review of the documents produced in this 
case, Bank employees with knowledge of the California EDD cardholder 
population noted their specific awareness that 
 
. For example, a September 14, 
2020, and internal Bank email from 
 
 
 
20 Emily Cubides and Shaun O’Brian, The Federal Reserve, “2022 Findings from the Diary of 
Consumer Payment Choice” (May 5, 2022),  2022-Findings-from-the-Diary-of-Consumer-
Payment-Choice-FINAL.pdf 
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19 
21
36.
The contemporaneous evidence I reviewed does not reasonably show
that the mere fact that an unauthorized-transaction claim was based on an ATM 
cash transaction indicated a fraudulent claim. The Bank would have needed to 
identify additional information, obtained through a manual investigation or through 
other channels available to it, to have a reasonable basis to summarily deny 
unauthorized ATM transaction claims. The Bank’s underlying premise is unsound 
because it ignores scenarios in which the cardholder would be the victim (e.g., a 
victim of card skimming). 22 The automatic presumption of fraud by the claimant is 
21 BANA_EDD_MDL-00630750.  
22 BANA_EDD_MDL-00169902 (September 19, 2020,
; BANA_EDD_MDL-00125058 and 
00125059 (September 24, 2020 email and slide deck
); BANA_EDD_MDL-00170045 and 
0017046 (September 24, 2020 email and slide deck
); 
BANA_EDD_MDL-00087760, BANA_EDD_MDL-00087763 (September 25, 2020 email from 
 regarding GFC’s proposal for a 
); 
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20 
particularly unreasonable given the Bank’s awareness of card skimming at the time 
of its CFF-1 development and implementation,23 and the tools available to the 
Bank to identify card-skimming.  
C. The Bank failed to incorporate any of its various available tools and 
methods used in other contexts to inform CFF-1 denials and assess 
whether a cardholder-claimant was in fact a legitimate victim.   
37. 
As Cronan acknowledges, large banks, such as Bank of America, have 
long implemented automated processes to identify fraud. This acknowledgment 
aligns with my experience as well. Cronan’s report, however, omits any discussion 
of whether automated processes included the capability to identify card skimming. 
Regardless, the records I reviewed revealed the Bank did not incorporate any of the 
available card skimming detection processes in developing its CFF-1 automation 
policy. As I noted above, based on my experience, there are several means 
available to the Bank to detect and identify card skimming. None were 
 
BANA_EDD_MDL-00087766 and 00087767 (September 25, 2020 email and slide deck 
 
 
 BANA_EDD_MDL-00087779 and 00087780 
(September 25, 2020, email and slide deck
 
 
23 BANA_EDD_MDL-00455617 (Email string initiated by
 
 
and noting 
; 
BANA_EDD_MDL-00228914 – 15 (April 24, 2020 email 
 
. 
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21 
incorporated into CFF-1. A financial institution of the Bank’s size and 
sophistication should have been able to devise a process to identify card skimming 
claims and should have had typologies and detection rules in place to identify this 
type of activity prior to the Covid-19 pandemic.  
38. 
The Bank’s records reveal that several Bank employees raised 
concerns about using CFF-1 to auto-deny claims24 and noted that CFF-1 
 
 
25 
 
 
24 BANA_EDD_MDL-00107267 (December 29, 2020, email from
 
 
 
; BANA_EDD_MDL-00107267 at -00107268 (January 11, 
2021, email from 
 
). 
25 See e.g., BANA_EDD_MDL-00070242 (Slide deck entitled 
 
 dated September 2, 2020, 
 
 
 
 
 
; see 
also BANA_EDD_MDL-00102789 (September 2, 2020, email from
 
 
 
 BANA_EDD_MDL-00874835 and BANA_EDD_MDL-00885638 
(September 13, 2020 email from 
 
 
  
 
); BANA_EDD_MDL-
00087718 and BANA_EDD_MDL-87719 (September 15, 2020, email from 
 
 
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22 
 
 
, as compared to the single-factor, 
automatic-denial approach incorporated into CFF-1, demonstrates that the Bank 
could have adopted a more tailored approach to address concerns about fraudulent 
unauthorized ATM transaction claims. Nonetheless, the Bank did not adopt any 
such nuanced considerations in CFF-1 and failed to consider any heightened risk 
factors to tie the activity to benefits fraud as with 
 or tie the activity to 
legitimate claims like card skimming for approval. 26    
39. 
Pesce highlights that the Bank also applied CFF-1 to cases in which 
EDD had verified the claimant-cardholder’s identity. Pesce contends that the 
Bank’s reliance on EDD’s verification of the cardholder’s identity was sufficient 
control to eliminate false positives, i.e., unauthorized ATM transaction claims that 
 
 
 
;  
BANA_EDD_MDL-00076931 and BANA_EDD_MDL-00076932 (September 22, 2020 email 
 
 
 
BANA_EDD_MDL-00125177 (
 
 
.  
26 Pesce Rep. ¶41. Stating “accounts bore strong indicia that they belonged to fraudulent benefits 
recipients (e.g., because of their 
).” 
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23 
the Bank automatically denied based on CFF-1 even though the claimant was an 
actual victim of ATM theft.27 As a preliminary matter, EDD’s verification of 
cardholder identity would have taken place after legitimate cardholders’ claims 
were already auto-denied; thus, that procedure was not sufficient to constitute a 
reasonable control. The auto-denial would have occurred prior to the Bank taking 
any steps to determine whether there were transactions that may have occurred at a 
known compromised ATM or POS terminal or presented flags regarding a new 
compromise.  
40.
Further, unlike EDD, the Bank had exclusive access to the
transactional data necessary to determine whether a cardholder was a victim of 
card skimming or otherwise a victim of another type of fraud (i.e., prior contested 
transactions, use of a compromised ATM, etc...). 28 Failure to leverage this 
transactional data and instead relying on a system that requires cardholders to 
exhaust efforts with the State before being permitted to regain access to critical 
benefits funds cannot reasonably be expected to accurately distinguish legitimate 
claims from illegitimate ones. This practice also increases the likelihood that 
information supporting the cardholders’ assertions may become lost or inaccessible 
27  Id. at. ¶ 59 
28 BANA_EDD_MDL-00417517 (
. 
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24 
(records proving where the cardholder was at the time of the unauthorized 
transaction, photos of individual that the ATM or POS terminal may be purged 
over time). These realities further undermine any contention that CFF-1 is 
reasonably scoped and tailored. 
41. 
In my opinion, in designing CFF-1 to deny any claim based on a 
contested ATM cash withdrawal, the Bank took the most extreme approach to 
automation resulting in a blunt and overbroad filter that was not tailored to the task 
of identifying likely fraudulent claims and that foreseeably would impact many 
legitimate cardholders. 
VIII. PESCE AND CRONAN’S OPINIONS ABOUT THE 
REASONABLENESS OF THE BANK’S AUTOMATED FRAUD 
DETECTION SYSTEMS IS FLAWED AS THE RECORD REFLECTS 
THE BANK MAINTAINED A FLAWED APPROACH TO 
DEPLOYING AND IMPLEMENTING THE CFF-1 AUTOMATION. 
42. 
Both Cronan and Pesce acknowledge the Bank’s regulator’s 
expectation that banks identify, measure, monitor, and control risks. Cronan 
references the regulator’s handbook series that includes guidance on effective 
control systems for insight into how to effectively measure and monitor the 
systems that are put in place.29 The Bank’s regulator emphasizes in the handbook 
 
29 Office of the Comptroller of the Currency, Administrator of National Banks, “Comptroller’s 
Handbook: Internal Control, ” (January 2001), available at https://www.occ.gov/publications-
and-resources/publications/comptrollers-handbook/files/internal-control/index-internal-
control.html.  
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25 
that effective control systems are important to help detect possible mistakes, errors 
in judgment, or even distraction that might cut against the effectiveness of a bank’s 
system of controls.30  
43. 
Cronan and Pesce recognize that no fraud detection system or 
automation will be perfect and will have false positives.31 That said, as discussed 
above, the controls and measures underlying an automation must be reasonably 
tailored for the specific purpose they are used, which CFF-1 was not. Similarly, a 
bank must be reasonable in its approach in identifying metrics to measure an 
automation’s effectiveness and safeguards to identify and correct errors. This is 
true even when—and arguably, especially when—circumstances necessitate quick 
implementation. In my opinion, neither the Cronan nor the Pesce expert report 
provides a sufficiently comprehensive assessment of the reasonableness of the 
Bank’s approach to the CFF automation generally, much less the CFF-1 
automation.  
44. 
Additionally, although automated features may be used to “aid an 
investigation,”32 I understand that CCF-1 was used by the bank not to flag 
cardholder claims involving ATM transactions for priority review, but to 
 
30 Id. at p.2. 
31 Cronan Rep. ¶12; Pesce Rep. ¶59. 
32 Cronan Rep. ¶ 30. 
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26 
automatically deny those claims without any investigation. Accordingly, CFF-1 
was not being used to “aid an investigation” but rather a substitute for conducting 
an investigation. Given the complete replacement of a previously manual multi-
step investigation, it was not reasonable to have such a limited method to measure 
the impact and effectiveness of the CFF-1 automation.  
45. 
As I detail below, the overall approach adopted by the Bank in 
automating CFF-1 was flawed. The Bank’s reliance
 
 was flawed at the outset because it was insufficient to address the 
complexities associated with the CFF automation and the effectiveness of CFF-1 in 
particular. The Bank compounded the negative effect of this flaw by failing to take 
a nuanced and concerted approach to understanding the impact of its CFF-1 on 
. Based on my experience, the Bank was not sufficiently 
deliberate in its implementation of CFF-1 and did not make any meaningful effort 
to enhance CFF-1’s effectiveness by incorporating other metrics or other proactive 
feedback mechanisms.  
A. 
The Bank’s heavy reliance on reconsideration rates to measure 
CFF-1 automation’s effectiveness was a flawed approach and 
unreasonable.  
46. 
Based on my review of the documents, I understand that the Bank 
 
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27 
.33 “Reconsideration rate” as used in this report refers to 
the percentage of denied claimants who proactively asked for reconsideration of 
their claim denials.  Pesce contends that 
 
reconsideration rates to measure the effectiveness of its automated CFF decisional 
process was reasonable.34 Pesce’s assessment, however, fails to take into account 
certain obvious shortfalls and obstacles inherent in the metric’s features in the 
context of administering state benefits.  
47. 
At the outset, the process of reconsideration involves protracted 
delays. Based on my review of the records provided, it 
 
the Bank to decision a reconsideration request once the cardholder was able to 
speak to a call center employee.35 The significant delay was driven and possibly 
further increased by several factors, all known to the Bank. Prior to March 18, 
2020, EDD cardholders had to reverify their identity with the state prior to 
requesting reconsideration by the Bank. Cardholders were explicitly instructed to 
take this step in the denial letters issued by the Bank, which required them to 
 
33 Letson Deposition, at 184:2-20. (
 
). 
34 Pesce Rep. ¶58 (“In my opinion, the lack of prepaid cardholders seeking reconsideration 
would have provided the Bank with a good faith basis to believe the CFF was capturing 
fraudsters.”). 
35 Renee Johnson Deposition at.190:6 – 192:11 (
 
.); BANA_EDD_MDL-00417271-2 
(email noting that the 
.) 
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28 
contact the state of California to reverify their identity. Moreover, the phone lines 
at the Bank had significant waiting times. Given the Bank’s extensive experience 
managing call centers, the Bank would have, or reasonably should have, known 
that these delays would cause many valid claimants to abandon their calls out of 
frustration or due to their own time constraints. The effect would be particularly 
pronounced for EDD cardholders, who tended to be low-income individuals who 
often had time constraints as well as budgetary constraints.36 Further, the reality is 
that a process requiring affirmative reconsideration requests will almost never 
capture all erroneous denials. Language barriers, family or personal distractions, 
medical issues, limited phone plans, and other common everyday problems in our 
society lead people to delay pursuing or abandon altogether valid claims. This 
phenomenon is well known, and it is reasonable to conclude the Bank would have 
been aware of it. In my opinion, these factors make it unreasonable for the Bank to 
have relied on reconsideration rates alone to measure erroneous denials. 
48. 
Pesce highlights the “low” percentage of individuals requesting 
reconsiderations in attempting to describe the reconsideration rates as a sufficient 
metric.37 However, Pesce’s characterization of the rate as a “low” number is 
 
36 Anne Holt Deposition at.  134:20-135:11 
 
, 94:11-
95:25, 139:14-140:5, 226:21-227:1; Faiz Ahmad at.  56:1-7; 69:14-17 
37 Pesce Rep. Fn.138.  
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29 
misleading. Although the percentage of individuals who requested reconsideration 
was approximately 
, this percentage represented a dramatic jump in the 
overall number of reconsideration requests after the implementation of CFF 
automation. In August and September 2020 (prior to CFF rollout), 
individuals respectively requested reconsideration as compared to 
 in 
October 2020 and 3,262 in November 2020.38 This jump represents a nearly 
 percent increase in the number of reconsideration requests in the first 
months following the Bank’s CFF implementation. In my opinion, this sharp 
increase alone would have been a justification to question the reasonableness of the 
Bank’s reliance on reconsideration rates as a sole measure of CFF’s efficacy.  
49.
While reconsideration rates might provide some information, it was
not reasonable for the Bank to 
 
 
 considering the known obstacles and the data showing a steep rise in 
reconsideration requests after CFF rollout. The Bank should have accounted for 
this and taken additional steps to improve the accuracy of its measurement. It was 
not reasonable for the Bank to 
—a purely 
38 BANA_ED_MDL-00203373 (On the “Exec Summary” tab row 22 columns AD and AE); 
BANA_EDD_MDL-00571086 (On the “Exec Summary” tab row 26 columns AH, and AI). 
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30 
reactive measure—without simultaneously taking into account any additional 
proactively obtained metrics to better assess CFF’s effectiveness.  
B. 
The Bank’s reliance on reconsideration rates was unreasonable 
because it failed to consider CFF-1’s actual impact on those rates.  
50. 
If the Bank chose to use reconsideration rates 
 
, it reasonably should follow that the Bank would utilize a 
system to monitor and track the effect CFF had on those rates. A bank 
implementing CFF or similar automation should want to know which 
reconsideration requests were driven by the new automation and which were not. 
As CFF had three indicators (CFF-1, CFF-2, and CFF-3), a bank should be 
reasonably interested in examining which reconsideration requests were tied to 
each indicator. When addressing reconsideration rates, neither Cronan’s nor 
Pesce’s report addresses how the Bank assessed the impact of CFF automations on 
those rates. In fact, Cronan and Pesce could not address it because, based on my 
review of the records provided, the Bank 
 
. In the immediate 
months after implementing CFF, the Bank 
 
 
. In my opinion, this failure/oversight 
is unreasonable. 
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31 
51. 
In the first two and half months of the CFF automation deployment, 
the Bank did not 
 
.39 Additionally, 
based on the records provided to me, the Bank was not reviewing reports that 
 
 
.40 It further appears that the Bank’s reports did not provide sufficient details 
to meaningfully measure the impact of each CFF indicator on the auto-decisioned 
population until May 2021—approximately seven months after its CFF rollout.41 
These reports reveal that in the initial months, the Bank did not have a clear 
 
39 BANA_EDD_MDL-00203372, 00203373 (CR&R Report from October 28, 2020); 
BANA_EDD_MDL-00203434, -00203435 (CR&R Report from November 4, 2020); 
BANA_EDD_MDL-00203523, -00203527 (CR&R Report from November 12, 2020); 
BANA_EDD_MDL-00203585, -00203586 (CR&R Report From November 20, 2020); 
BANA_EDD_MDL-0023610, -0023612 (CR&R Report from November 25, 2020); 
BANA_EDD_MDL-00202637, 00202639 (CR&R Report from December 4, 2020); 
BANA_EDD_MDL-00203643-00203645 (CR&R Report from December 7, 2020). 
40 BANA_EDD_MDL-00571046, -0057104 (Email and attached presentation sent on December 
16, 2020, 
 
; BANA_EDD_MDL-00646320 at -646321 (Email 
string on January 21, 2020,
 
); BANA_EDD_MDL-00510141, BANA_EDD_MDL-
00510142 at -510145(Email string with attachment starting on May 11, 2021, identifying that 
 
); 
BANA_EDD_MDL-00118652-55 (Email string starting on May 10, 2021, highlighting
 
. 
41 BANA_EDD_MDL-00510148; BANA_EDD_MDL-00118652; BANA_EDD_MDL-
00159490; BANA_MDL_EDD-00166412, -00166413; BANA_EDD_MDL-00166432, -
00166433. 
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32 
understanding of what claims were auto-decisioned, much less the basis for auto-
decisioning. The reports were patently insufficient to apprise recipients at the Bank 
on the CFF indicators’ performance.  
52. 
Without this critical information, it seems inconceivable that these 
report recipients could be reasonably informed about whether the Bank needed to 
improve or refine its CFF automation or where it should layer additional 
measurement strategies to account for information gaps. Had the Bank 
implemented appropriately detailed reporting from the outset, it would have been 
able to quickly appreciate the impact of the CFF indicators, particularly CFF-1. A 
meaningful review of the metrics would have made evident the disproportionate 
impact that CFF-1 had on the overall auto-decisioned population, both in the 
number of claims and dollar amounts. A presentation from May 2021 providing 
 
 
 
 
).42  
 
42 BANA_MDL_EDD-00159490 at-159492 (Slide deck presentation from May 2021 that states 
that 
 
; see also BANA_EDD_MDL-00159490 (A slide from 
the May 2021 presentation noting 
 
 
). 
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33 
53. 
As I noted above, the Bank’s reliance on 
 
as a measure of CFF automation’s effectiveness was fundamentally flawed and 
shows the Bank was not reasonable in its approach to capturing accurate metrics. 
In my opinion, the Bank effectively doubled down on its unreasonable approach by 
failing to ensure its reporting of 
 was meaningfully tied to the 
CFF indicators.  
C. 
The Bank’s failure to employ additional generally accepted 
proactive strategies, such as sampling, surveys, or intentional 
feedback, to measure CFF-1 automations’ effectiveness was 
unreasonable.  
54. 
As I explained above, the Bank’s failure to immediately capture and 
assess the impact of each CFF indicator on 
 was unreasonable 
and contrary to standard practices. Those analytical deficiencies are exacerbated by 
the fact that the Bank’s later reports revealed that 
 
 
. Additionally, as I noted above, CFF automation resulted in a 
dramatic upsurge in the overall number of reconsideration requests (again, a 
 
). 43 This impact could not reasonably be dismissed as 
trivial or negligible.  
 
43 BANA_EDD_MDL-00571086 (On the “Exec Summary” tab rows 5 and 26, Columns AH, 
and AI).  
  
 
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34 
55. 
The Bank was aware that CFF’s impact could not be dismissed as 
trivial given the noticeable change in operations, and the Bank reasonably should 
have taken steps to inform itself of the impact’s scope. In the initial months of the 
CFF automation, the Bank’s reports 
. But 
even if the Bank had implemented from the start its later-adopted method of 
reporting, its knowledge would have been limited to knowing how many claims 
were auto-decisioned and the basis for auto-decisioning. The Bank would not have 
been able to make reasonably informed conclusions about the underlying accuracy 
and would have had an incomplete picture of the effect of its new automation 
because, as explained above, the reconsideration metric is inherently incomplete. 
The Bank should have taken steps to evaluate a meaningful cross-section of claims 
that were being denied automatically that were not associated with reconsideration 
requests to ensure the automatic denials were effective and not negatively 
impacting legitimate claimants.  
56. 
The Bank could have reasonably utilized any number of proactive 
methods, such as sampling, surveys, and/or intentional feedback to give it greater 
confidence in CFF’s efficacy.44 The OCC handbook identifies sampling and 
 
 
44 Pesce Rep. ¶48 While Pesce notes raises “
” this is not related to the use of 
the CFF automations for the purpose of claim denials, but to prevent fraudulent transactions, a 
process that is not triggered by a cardholder contacting the bank asserting fraud.  
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35 
surveys, also referred to as questionnaires, as additional ways to measure 
effectiveness.45 Neither Cronan nor Pesce describe these efforts by the Bank, again 
because the records indicate that the Bank took no such efforts.  
57. 
Sampling would have involved a process where the Bank examined 
and scrutinized a randomly selected group of denied claims (regardless of whether 
the claimants requested reconsideration). Had the Bank taken steps to sample 
automatically denied unauthorized transaction claims—specifically, those 
decisioned only using CFF-1—it could have identified issues like the improper 
reversal of permanent credits or observed legitimate victims with common 
attributes, like card skimming. Given the pure number of cardholders that were 
subject to the new automation, it is in my opinion wholly deficient to exclusively 
rely on reconsideration rates. 
58. 
The Bank could also have implemented surveying, whereby it would 
have issued questionnaires or inquiries to front-line employees to determine 
whether they experienced any unexpected issues with the new automation. My 
examination of the records provided to me reveals that several Bank employees 
proactively communicated serious issues about CFF-1 without any prompting from 
 
45 Office of the Comptroller of the Currency, Administrator of National Banks, “Comptroller’s 
Handbook: Internal Control, ” (January 2001), pgs. 23, 37, available at 
https://www.occ.gov/publications-and-resources/publications/comptrollers-
handbook/files/internal-control/index-internal-control.html.  
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36 
Bank supervision or management.46 Given this reality, it stands to reason that 
sampling would have identified additional issues that could have reasonably 
informed changes to CFF-1 processes. 
59. 
When implementing a new process, automated or otherwise, a bank’s 
front-line employees are in an ideal position to provide feedback, as they are the 
ones dealing with the immediate fallout of any problems or issues. Recognizing 
this fact, many successful banks allow front-line employees to provide feedback, 
particularly in the early stages of a new process. These intentional and deliberate 
processes can include setting up email accounts or internal phone numbers 
specifically for feedback and tasking a team to evaluate the feedback to identify 
systemic issues, errors, and—in the cases of automated or technical process—
code-based bugs. 
60. 
Cronan and Pesce do not address this aspect of implementation in 
their reports. Once again, this is because the record indicates that the Bank made 
no effort to develop such processes; and to the extent the Bank’s vendors supported 
 
46 BANA_EDD_MDL-00057504 (Email string initiated on October 30, 2020 by Bradley 
Garfield 
 
; BANA_MDL_EDD-00090640 (December 29, 2020 
email 
); 
BANA_EDD_MDL-00297295 (April 23, 2021, email 
 
; BANA_EDD_MDL-00090683, at -9068586 (Email string 
starting on January 11, 2021 
 
 
) 
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37 
such feedback mechanisms, the record indicates that the Banks did not escalate 
these issues to consider modifying the overbroad scope of CFF-1. Based on my 
experience piloting automations and rolling out version updates, it is standard 
practice to solicit feedback in the initial rollout of new processes. Businesses often 
find intentional feedback processes critical and have established repeatable 
processes for collecting feedback. The alternative is to adjust processes based on 
unsolicited feedback obtained on an ad hoc basis, which can prove difficult if not 
impossible. My review of the documents provided reveals that the Bank’s agents 
were aware of CFF-1 auto decisioning denials to victims and permanent credit 
holders having their credits reversed. However, it appears that these employees 
raised these issues sua sponte rather than in response to any concerted or proactive 
effort by the Bank. Had the Bank taken steps to solicit feedback, it likely would 
have become more aware of the problems with CFF-1 in the initial months of the 
CFF automation deployment. In my opinion, the Bank’s failure to adopt a formal 
mechanism to apprise and obtain feedback from its employees is further evidence 
that its overall approach to implementing CFF was unreasonable.  
61. 
Sampling, surveying, and soliciting proactive feedback would not 
necessarily have revealed all issues, but they are generally accepted—and typically 
low-cost—methods for a business to measure the effect of any new process, 
including automation. The record I reviewed indicates that the Bank made no 
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38 
attempt to use these measures or any other proactive rather than reactive measures 
to assess CFF-1. In my opinion, this failure was unreasonable and is surprising for 
a financial institution with the Bank’s sophistication and resources. The Bank’s 
failure to reasonably assess the effectiveness and impacts of CFF-1 through 
standard, generally accepted processes that one would typically expect to see, 
further contradicts Cronan and Pesce’s opinion that the Bank could have 
reasonably believed that the CFF was an acceptable use of automation. 
D. 
The Bank’s inaccurate documentation regarding the CFF-1 
automation further highlights that the Bank’s overall approach to 
implementing CFF was not reasonable.  
62. 
The Bank’s regulator requires banks to have well-documented 
processes that clearly describe the operations and the lines of responsibility.47 
Sound documentation better ensures processes can be properly implemented by the 
developers, understood by the employees, and effectively reviewed by 
management. Proper and accurate documentation assists a bank in identifying any 
errors or improper implementations. 
63. 
Neither Cronan nor Pesce address whether the Bank’s documentation 
of the CFF automation was accurate or reasonable. My review of the records 
 
47 Office of the Comptroller of the Currency, OCC Bulletin 2019-37: Operational Risk: Fraud 
Management Principals (July 24, 2019), https://www.occ.treas.gov/news-
issuances/bulletins/2019/bulletin-2019-37.html (“Banks with significant and far-reaching retail-
oriented business activities should have well-documented fraud risk management with 
appropriate monitoring measurements and reporting, and mitigation.”) 
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39 
provided revealed several documents indicating that CFF-1 was hastily created. In 
my overall estimation, the documents show a curtailed rollout process checkered 
with last-minute decisions resulting in a great deal of confusion regarding CFF-1’s 
nature and scope. For example, email correspondence on the same day the CFF 
process was implemented demonstrates that the rollout team was 
 
 
 
.48 The confusion is understandable, because the prior work of the group that 
developed fraud detection strategies had looked to 
 
 the mere fact that a claim involved an ATM 
transaction.49  
48 BANA_EDD_MDL-00709230(
). 
49    See e.g., BANA_EDD_MDL-00070242 (Slide deck entitled
; see 
also BANA_EDD_MDL-00102789 (September 2, 2020, email 
; BANA_EDD_MDL-00874835 and BANA_EDD_MDL-00885638 
(
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40 
64. 
My understanding is that the Bank’s 
 
 
50 The confusion about whether the CFF-1 results were 
 
 
.51 
 
 
.52  
 
00087718 and BANA_EDD_MDL-87719 (
 
 
 
 
 
;  
BANA_EDD_MDL-00076931 and BANA_EDD_MDL-00076932 (
 
 
 
 
 
 
BANA_EDD_MDL-00125177 (
 
 
.  
50 BANA_EDD_MDL-00497802. 
51 BANA_EDD_MDL-00592322, BANA_EDD_MDL-00592324 at. –592326 (November 9, 
2020, email from 
 
 
). 
52 BANA_EDD_MDL-00592324 at -592357(see, e.g., §1.3 (Assumptions); §1.4 (
 
) 
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41 
65. 
In addition to inaccurately and inconsistently 
 
 
 
 
 
. 53 
However, as discussed above, the Bank employees knew that reconsideration 
requests took on average 
, and potentially much longer considering the 
customer service obstacles experienced by cardholders in trying to verify their 
identity with the Bank and EDD.  
 
 
.  
66. 
In my opinion, these inconsistencies and inaccuracies, when viewed 
collectively, further demonstrate that the Bank did not have a deliberate and well-
executed approach to rolling out CFF-1. They therefore provide further support for 
 
53 Illustrative of presentations that included this same process visual. BANA_EDD_MDL-
00077039, BANA_EDD_MDL-00077040 (December 12, 2020, email from
 
 
).  
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42 
my opinion that the Bank’s overall approach in implementing CFF-1 was not 
reasonable.  
IX. THE BANK DID NOT REASONABLY RESPOND TO THE 
REVERSING OF CARDHOLDERS’ PERMANENT CREDITS BASED 
ON CFF-1. 
67. 
As explained above, the basis for automatically decisioning 
cardholder unauthorized-transaction claims based solely on CFF-1 is contrary to 
standard practices and flawed. Accordingly, I rely on the reasoning stated above, as 
well as the fact that this population of cardholders that had received permanent 
credits on prior unauthorized transaction claims also had an individualized 
investigation that identified information supporting the cardholder’s assertion that 
the transactions in question were in fact unauthorized.  
68. 
The Bank employees tasked with developing and implementing the 
code to retroactively apply the CFF filters to the cardholders who initiated claims 
starting on April 1, 2020, through September 27, 2020, should have considered this 
cross-section of cardholders that had already undergone evaluation and wrote code 
to exclude them from the automation. Although 
 
,54 it does not 
 
54 BANA_EDD_MDL-00169953 at -16995 (September 21, 2020, email exchange from
 
 
 
).  
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43 
appear from the documents I reviewed that this scenario was specifically provided 
to the employees developing the code as a necessary requirement.  
69. 
The Bank’s regulators require reasonably tailored systems of controls 
to ensure programs and processes operate effectively. Cronan and Pesce did not 
address—and I did not review records that demonstrated—that the Bank engaged 
in any meaningful quality assurance or scenario testing after the implementation of 
the retroactive CFF filters to ensure that the process worked as intended. As 
described above, the Bank did not apprise or solicit feedback from its front-line 
employees on CFF generally, much less the impact on specific cardholders who 
had claims initiated between April 1, 2020, and September 27, 2020.  
70. 
Based on document data from as early as January 2021, the Bank was 
aware that 
 
 
.55 Had the Bank deliberately sought out this type of 
oversight, it would have been possible for the Bank to issue corrective measures 
for the entire subpopulation in a timely manner.  
 
55 BANA_MDL_EDD-00571297, BANA_EDD_MDL-00571299 (Email exchange with 
attachment from January 2021 between 
 
 
. 
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71. The Bank's failure to implement proactive measures to identiS, issues
and measure effectiveness was not reasonable with respect to the cardholders who
had permanent credits reversed.
Dated: April 4,2025
Loebner
44
N
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APPENDIX A: MATERIALS RELIED UPON 
Production Materials 
BANA_EDD_MDL-0041755-00417558 
BANA_EDD_MDL-00163307-163308 
BANA_EDD_MDL-00004996-5020 
BANA_EDD_MDL-00559693-559980 
BANA_EDD_MDL-00228914-228915 
BANA_EDD_MDL-00455617-455619 
BANA_EDD_MDL-00154042-154044 
BANA_EDD_MDL-00057504-57506 
BANA_EDD_MDL-00166345 
BANA_EDD_MDL-00087718-87720 
BANA_EDD_MDL-00076931-76947 
BANA_EDD_MDL-00070242-70247 
BANA_EDD_MDL-00592322-592330 
BANA_EDD_MDL-00127437-127441 
BANA_EDD_MDL-00646320-646335 
BANA_EDD_MDL-00225375-225393 
BANA_EDD_MDL-00426407-426411 
BANA_EDD_MDL-00297295 
BANA_EDD_MDL-00273305-273307 
BANA_EDD_MDL-00667102-667118 
BANA_EDD_MDL-00667119-667120 
BANA_EDD_MDL-00004535-4580 
BANA_EDD_MDL-00100634-100679 
BANA_EDD_MDL-00123235-123236 
BANA_EDD_MDL-00090640-90647 
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BANA_EDD_MDL-00090683-90686 
BANA_EDD_MDL-00708960-708962 
BANA_EDD_MDL-00102786-102787 
BANA_EDD_MDL-00874835-885639 
BANA_EDD_MDL-00630749-630753 
BANA_EDD_MDL-00169902-169904 
BANA_EDD_MDL-00169912-169915 
BANA_EDD_MDL-00630837-630838 
BANA_EDD_MDL-00169953-169957 
BANA_EDD_MDL-00087760-87765 
BANA_EDD_MDL-00125058-125059 
BANA_EDD_MDL-00170045-170047 
BANA_EDD_MDL-00087766-87769 
BANA_EDD_MDL-00087775-87778 
BANA_EDD_MDL-00630880-630882 
BANA_EDD_MDL-00125073-125075 
BANA_EDD_MDL-00087779-87780 
BANA_EDD_MDL-00709230-709234 
BANA_EDD_MDL-00140948 
BANA_EDD_MDL-00169898-169899 
BANA_EDD_MDL-00371998 
BANA_EDD_MDL-372000-372005 
BANA_EDD_MDL-00705489-705492 
BANA_EDD_MDL-00125177-125179 
BANA_EDD_MDL-00162410-162411 
BANA_EDD_MDL-00845906-845908 
BANA_EDD_MDL-00497802-497804 
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Page 50 of 71

BANA_EDD_MDL-00405158-405160 
BANA_EDD_MDL-00510141-510148 
BANA_EDD_MDL-00080294-80352 
BANA_EDD_MDL-00087715-87716 
BANA_EDD_MDL-00019602-19603 
BANA_EDD_MDL-00019618-19628 
BANA_EDD_MDL-00057837-57878 
BANA _EDD_MDL-00631444-631446 
BANA_EDD_MDL-00076996 
BANA_EDD_MDL-00076997-70000 
BANA_EDD_MDL-00118459 
BANA_EDD_MDL-118460-61 
BANA_EDD_MDL-00571029 
BANA_EDD_MDL-571030-33 
BANA_EDD_MDL-00077039 
BANA_EDD_MDL-77040-43 
BANA_EDD_MDL-00571046 
BANA_EDD_MDL-571047-48 
BANA_EDD_MDL-00159044-159050 
BANA_EDD_MDL-00571297 
BANA_EDD_MDL-00571299-571303 
BANA_EDD_MDL-00510148 
BANA_EDD_MDL-00118652-00118655 
BANA_EDD_MDL-00159489 
BANA_EDD_MDL-159490-94 
BANA_EDD_MDL-00166412 
BANA_EDD_MDL-166413-16 
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Page 51 of 71

BANA_EDD_MDL-00166432 
BANA_EDD_MDL-166433-38 
BANA_EDD_MDL-00884198 
BANA_EDD_MDL-00884199 
BANA_EDD_MDL-00884200 
BANA_EDD_MDL-00430148-0162 
BANA_EDD_MDL-00154004-008 
BANA_EDD_MDL-00102554-102587 
BANA_EDD_MDL-00120424-120425 
BANA_EDD_MDL-00570333–570334 
BANA_EDD_MDL-00154700-154708 
BANA_EDD_MDL-00205361 
BANA_EDD_MDL-00088501–88505 
BANA_EDD_MDL-0088506–88521 
BANA_EDD_MDL-00088501–88505 
BANA_EDD_MDL-00884198 
BANA_EDD_MDL-00085694 
BANA_EDD_MDL-00085786 
BANA_EDD_MDL-00085788-BANA_EDD_MDL-00085789 
BANA_EDD_MDL-00085803–85808 
BANA_EDD_MDL-00086221–86225 
BANA_EDD_MDL-00118436–118437 
BANA_EDD_MDL-00416783–416784 
BANA_EDD_MDL-00705503–705510 
BANA_EDD_MDL-00055974–55981 
BANA_EDD_MDL-00076984–76985 
BANA_EDD_MDL-00076983 
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BANA_EDD_MDL-00117097–117099 
BANA_EDD_MDL-00142730–142731 
BANA_EDD_MDL-00158864-158865 
BANA_EDD_MDL-00158866 
BANA_EDD_MDL-00203324-203325 
BANA_EDD_MDL-00203326 
BANA_EDD_MDL-00159090-159091 
BANA_EDD_MDL-00159092 
BANA_EDD_MDL-00159120-159124 
BANA_EDD_MDL-00159125 
BANA_EDD_MDL-00159126-159127 
BANA_EDD_MDL-00159128 
BANA_EDD_MDL-00143388-143389 
BANA_EDD_MDL-00143390 
BANA_EDD_MDL-00143391-143392 
BANA_EDD_MDL-00143393 
BANA_EDD_MDL-00143416-143417 
BANA_EDD_MDL-00143418 
BANA_EDD_MDL-00143431-143432 
BANA_EDD_MDL-00143433 
BANA_EDD_MDL-00143518-143519 
BANA_EDD_MDL-00143520 
BANA_EDD_MDL-00143521-143522 
BANA_EDD_MDL-00143523 
BANA_EDD_MDL-00143569-143570 
BANA_EDD_MDL-00143571 
BANA_EDD_MDL-00143627-143628 
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BANA_EDD_MDL-00143629 
BANA_EDD_MDL-00143661-143662 
BANA_EDD_MDL-00143663 
BANA_EDD_MDL-00159248-159250 
BANA_EDD_MDL-00159251 
BANA_EDD_MDL-00143718-143719 
BANA_EDD_MDL-00143720 
BANA_EDD_MDL-00143735-143736 
BANA_EDD_MDL-00143737 
BANA_EDD_MDL-00143762-143763 
BANA_EDD_MDL-00143764 
BANA_EDD_MDL-00143781-143782 
BANA_EDD_MDL-00143783 
BANA_EDD_MDL-00143802-143803 
BANA_EDD_MDL-00143804 
BANA_EDD_MDL-00143859-143860 
BANA_EDD_MDL-00143861 
BANA_EDD_MDL-00143890-143891 
BANA_EDD_MDL-00143892 
BANA_EDD_MDL-00143958-143959 
BANA_EDD_MDL-00143960 
BANA_EDD_MDL-00144041-144042 
BANA_EDD_MDL-00144043 
BANA_EDD_MDL-00144074-144075 
BANA_EDD_MDL-00144076 
BANA_EDD_MDL-00144109-144110 
BANA_EDD_MDL-00144111 
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BANA_EDD_MDL-00144136-144137 
BANA_EDD_MDL-00144138 
BANA_EDD_MDL-00144187-144188 
BANA_EDD_MDL-00144189 
BANA_EDD_MDL-00144230-144231 
BANA_EDD_MDL-00144232 
BANA_EDD_MDL-00144236-144237 
BANA_EDD_MDL-00144238 
BANA_EDD_MDL-00144341-144342 
BANA_EDD_MDL-00144343 
BANA_EDD_MDL-00163440-163441 
BANA_EDD_MDL-00163442 
BANA_EDD_MDL-00163455-163455 
BANA_EDD_MDL-00163457 
BANA_EDD_MDL-00163466-163467 
BANA_EDD_MDL-00163468 
BANA_EDD_MDL-00129835 
BANA_EDD_MDL-00129836 
BANA_EDD_MDL-00142492-142493 
BANA_EDD_MDL-00143494 
BANA_EDD_MDL-00145314-145316 
BANA_EDD_MDL-00145317 
BANA_EDD_MDL-00159396-159398 
BANA_EDD_MDL-00159399 
BANA_EDD_MDL-00159992-159993 
BANA_EDD_MDL-00159994 
BANA_EDD_MDL-00203372 
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BANA_EDD_MDL-00203373 
BANA_EDD_MDL-00158937-158938 
BANA_EDD_MDL-00203434 
BANA_EDD_MDL-00203435 
BANA_EDD_MDL-00157129-157130 
BANA_EDD_MDL-00203523-203526 
BANA_EDD_MDL-00203527 
BANA_EDD_MDL-00203585 
BANA_EDD_MDL-00203586 
BANA_EDD_MDL-00203610-203611 
BANA_EDD_MDL-00203612 
BANA_EDD_MDL-00118440-118441 
BANA_EDD_MDL-00203637-203638 
BANA_EDD_MDL-00203639 
BANA_EDD_MDL-00203643-203644 
BANA_EDD_MDL-00203645 
BANA_EDD_MDL-00182465 
BANA_EDD_MDL-00182466 
BANA_EDD_MDL-00571084-571085 
BANA_EDD_MDL-00571086 
BANA_EDD_MDL-00203826-203827 
BANA_EDD_MDL-00203828 
BANA_EDD_MDL-00554834 
BANA_EDD_MDL-00554835 
BANA_EDD_MDL-00107267-107271 
BANA_EDD_MDL-00460915 
BANA_EDD_MDL-00460916-460924 
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BANA_EDD_MDL-00571307-571309 
BANA_EDD_MDL-00571310 
BANA_EDD_MDL-00571335 
BANA_EDD_MDL-00571336-571338 
BANA_EDD_MDL-00081273-81276 
BANA_EDD_MDL-00081277-81280 
BANA_EDD_MDL-00143629 
BANA_EDD_MDL-00143625 
BANA_EDD_MDL-00561268 
BANA_EDD_MDL-00556748 
BANA_EDD_MDL-00556749-556750 
BANA_EDD_MDL-00556752 
BANA_EDD_MDL-00547571-547572 
BANA_EDD_MDL-00547573-547577 
BANA_EDD_MDL-00547578 
BANA_EDD_MDL-00081758-81759 
BANA_EDD_MDL-00081760-81764 
BANA_EDD_MDL-00166456-166457 
BANA_EDD_MDL-00166458-166462 
BANA_EDD_MDL-00182930-182931 
BANA_EDD_MDL-00182932 
BANA_EDD_MDL-00159090-159091 
BANA_EDD_MDL-00159092 
BANA_EDD_MDL-00143391-143393 
BANA_EDD_MDL-00406128-406130 
BANA_EDD_MDL-00417271-417272 
 
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Pleadings  
Consent Order, In the Matter of Bank of Am., N.A., No. 2022-CFPB-0004 (July 14, 2022) 
Consent Order, In the Matter of Bank of Am., N.A., No. AA-ENF-2022-21 (July 14, 2022) 
Plaintiffs’ Memorandum of Points and Authorities in Support of Motion for Class Certification, 
In re: Bank of America California Unemployment Benefits Litigation, Case No.: 3:21-md-02992-
LAB-MSB (August 29, 2024). 
Defendant's Memorandum of Points and Authorities in Opposition to Plaintiffs’ Motion for Class 
Certification, In re: Bank of America California Unemployment Benefits Litigation, Case No.: 
3:21-md-02992-LAB-MSB (October 24, 2024). 
Plaintiffs’ Reply in Support of Motion for Class Certification, In re: Bank of America California 
Unemployment Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (November 11, 2024). 
Third Amended Master Consolidated Complaint, In re: Bank of America California 
Unemployment Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (January 24, 2025). 
Statutes and Legal Codes  
12 CFR Part 30 
12 CFR § 1005.2 
Public Law 95-630 
12 CFR § 1005.11 
18 U.S. Code § 1517 
31 C.F.R. § 1020.320 
31 C.F.R. § 1020.220 
31 C.F.R. § 1020.210 
Publicly Available Materials 
95th Congress, Public Law 95-630, November 10, 1978, available at 
https://www.govinfo.gov/content/pkg/STATUTE-92/pdf/STATUTE-92-Pg3641.pdf 
 
Authenticated U.S. Government Information, Federal Reserve System, 12 CFR Part 205, January 
10, 2006, available at https://www.govinfo.gov/content/pkg/FR-2006-01- 
10/pdf/06-145.pdf 
 
Auditor of the State of California, Employment Development Department: EDD’s Poor Planning 
and Ineffective Management Left it Unprepared to Assist Californians Unemployed by COVID-
19 Shutdowns (Jan. 2021), https://information.auditor.ca.gov/pdfs/reports/2020-128and628.1.pdf 
Case 3:21-md-02992-GPC-MSB     Document 634-11     Filed 01/09/26     PageID.52138 
Page 58 of 71

 
Bertrand, M., Mullainathan, S., & Shafir, E. “Behavioral Economics and Marketing in Aid of 
Decision Making among the Poor.” Journal of Public Policy & Marketing, (2006): 25(1), 8-23. 
https://doi.org/10.1509/jppm.25.1.8 (Original work published 2006) 
 
Bob Hager, Evaluating Effectiveness: The Impact of a Rules Coverage Assessment on 
Transaction Monitoring Solutions, NICE Actimize (June 13, 2024), 
https://www.niceactimize.com/blog/aml-evaluating-effectiveness-the-impact-of-a-rules-
coverage-assessment-ontransaction-monitoring-solutions/ 
 
Bd. of Govs. of Fed. Reserve System, Electronic Fund Transfers, 71 Fed. Reg. 1638, 1654 
 
Board of Governors of the Federal Reserve System, FDIC, FinCEN, NCUA, OCC, (January 19, 
2021), Answers to Frequently Asked Questions Regarding Suspicious Activity Reporting and 
Other Anti-Money Laundering Considerations, https://www.fincen.gov/sites/default/files/2021-
01/Joint%20SAR%20FAQs%20Final%20508.pdf 
 
Bureau Of Consumer Financial Protection, 12 CFR Parts 1005 and 1026, “Prepaid 
Accounts Under the Electronic Fund Transfer Act (Regulation E) and the Truth In 
Lending Act (Regulation Z),” Federal Register, Vol. 81, No. 225, November 22, 2016, 
available at https://www.govinfo.gov/content/pkg/FR-2016-11-22/pdf/2016-24503.pdf 
 
Department of Labor, Questions and Answers: Pandemic Unemployment Assistance (PUA) 
Program, https://www.dol.gov/sites/dolgov/files/ETA/advisories/UIPL/2020/UIPL_16-
20_Change_1_Attachment_1.pdf" 
Centers for Disease Control, Timing of State and Territorial COVID-19 Stay-at-HomeOrders and 
Changes in Population Movement – United States, March 1–May 31, 2020 (Sep. 4, 2020), 
https://www.cdc.gov/mmwr/volumes/69/wr/mm6935a2.htm 
Congressional Research Service, Unemployment Rates During the COVID-19 Pandemic (Aug. 
20, 2021), https://crsreports.congress.gov/product/pdf/R/R46554 
Consulting.US, Global Consulting Firm Accenture is Firing 25,000 Employees (Aug. 26, 2020), 
https://www.consulting.us/news/4776/global-consulting-firm-accenture-is-firing-25000-
employees 
Employment Development Department, State of California, Annual Report California Fraud 
Deterrence and Detection Activities, 
https://edd.ca.gov/siteassets/files/about_edd/pdf/fraud_deterrence_and_detection_activities_2021
.pdf; 
Employment Development Department, EDD Announces Reset in Response to Strike Team 
Recommendations to Process Claims Faster, Reduce Fraud and Tackle Backlog Issues, 
(September 19, 2020), https://www.labor.ca.gov/2020/09/19/edd-announces-reset/ 
Case 3:21-md-02992-GPC-MSB     Document 634-11     Filed 01/09/26     PageID.52139 
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“Examining Widespread Fraud in Pandemic Unemployment Relief Programs”, Report Prepared 
by the House Committee on Oversight and Accountability Majority Staff, September 10, 2024, 
https://oversight.house.gov/wp-content/uploads/2024/09/UI-Report-FINAL.pdf 
 
FinCEN, What We Do, https://www.fincen.gov/what-we-do 
 
FFIEC Manual, Assessing Compliance with BSA Regulatory Requirements, Customer 
Identification Program, 
https://bsaaml.ffiec.gov/manual/AssessingComplianceWithBSARegulatoryRequirements/01 
 
FFIEC Manual, Assessing Compliance with BSA Regulatory Requirements, Customer Due 
Diligence, Overview (2018), 
https://bsaaml.ffiec.gov/manual/AssessingComplianceWithBSARegulatoryRequirements/02 
 
Federal Trade Commission, When a Company Declines Your Credit or Debit Card, Consumer 
Advice, (August, 2022), https://consumer.ftc.gov/articles/when-company-declines-your-credit-or-
debit-card 
 
FinCEN Press Release, The Financial Crimes Enforcement Network (FinCEN) Encourages 
Financial Institutions to Communicate Concerns Related to the Coronavirus Disease 2019 
(COVID-19) and to Remain Alert to Related Illicit Financial Activity (Mar. 16, 2020), 
https://www.fincen.gov/news/news-releases/financial-crimes-enforcementnetwork-fincen-
encourages-financial-institutions 
 
FinCEN Press Release, The Financial Crimes Enforcement Network Provides Further 
Information to Financial Institutions in Response to the Coronavirus Disease 2019 (COVID-19) 
Pandemic (Apr. 3, 2020), https://www.fincen.gov/news/news-releases/financial-
crimesenforcement-network-provides-further-information-financial 
FinCEN, Advisory on Medical Scams Related to the Coronavirus Disease 2019 (COVID-19) 
(May 18, 2020), https://www.fincen.gov/sites/default/files/advisory/2020-05-
18/Advisory%20Medical%20Fraud%20Covid%2019%20FINAL%20508.pdf 
FinCEN, Notice Related to the Coronavirus Disease 2019 (COVID-19) (May 18, 2020), 
https://www.fincen.gov/sites/default/files/shared/May_18_Notice_Related_to_COVID-19.pdf 
FinCEN, Advisory on Imposter Scams and Money Mule Schemes Related to Coronavirus 
Disease 2019 (COVID-19) (July 7, 2020), 
https://www.fincen.gov/sites/default/files/advisory/2020-07-
07/Advisory_%20Imposter_and_Money_Mule_COVID_19_508_FINAL.pdf 
FinCEN, Advisory on Cybercrime and Cyber-Enabled Crime Exploiting the Coronavirus Disease 
2019 (COVID-19) Pandemic (July 30, 2020), 
https://www.fincen.gov/sites/default/files/advisory/2020-07-
30/FinCEN%20Advisory%20Covid%20Cybercrime%20508%20FINAL.pdf 
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FinCEN, Advisory on Unemployment Insurance Fraud During the Coronavirus Disease 2019 
(COVID-19) Pandemic (Oct. 13, 2020), 
https://www.fincen.gov/sites/default/files/advisory/2020-10-
13/Advisory%20Unemployment%20Insurance%20COVID%2019%20508%20Final.pdf 
FinCEN, Consolidated COVID-19 Suspicious Activity Report Key Terms and Filing Instructions 
(Feb. 24, 2021), https://www.fincen.gov/sites/default/files/shared/Consolidated%20COVID-
19%20Notice%20508%20Final.pdf 
FinCEN, Advisory on Financial Crimes Targeting COVID-19 Economic Impact Payments (Feb. 
24, 2021), https://www.fincen.gov/sites/default/files/advisory/2021-02-
24/Advisory%20EIP%20FINAL%20508.pdf 
FinCEN Press Release, The Financial Crimes Enforcement Network Provides Further 
Information to Financial Institutions in Response to the Coronavirus Disease 2019 (COVID-19) 
Pandemic (Apr. 3, 2020), https://www.fincen.gov/news/news-releases/financial-crimes-
enforcement-network-provides-further-information-financial 
FFIEC Manual, Introduction, Role of Government Agencies in the BSA (2015), 
https://bsaaml.ffiec.gov/manual/Introduction/01; Office of the Comptroller of the Currency, Bank 
Secrecy Act (BSA), https://www.occ.treas.gov/topics/supervision-and-examination/bsa/index-
bsa.html 
Financial Crimes Enforcement Network, Information on Complying with the Customer Due 
Diligence (CDD) Final Rule, https://www.fincen.gov/resources/statutes-and-regulations/cdd-
final-rule 
F5, How Fraud Detection Works: Common Software and Tools, 
https://www.f5.com/glossary/fraud-detection 
 
Jason Bramwell, The COVID-19 Pandemic is Causing Many Accountants (and a Whole Lot of 
Consultants) to Freak Out About Losing Their Jobs, going concern (Mar. 26, 2020), 
https://www.goingconcern.com/covid-19-accountants-layoffs-survey/ 
 
Kela, Targeted Cyber Intelligence, RaDark Intelligence Services, ID.me Bypass (Dec. 14, 2020)) 
 
Kelsey Coyle, et al., Consumer Payments and the Covid-19 Pandemic, Federal Reserve Bank of 
San Francisco (Feb. 9, 2021), https://www.frbsf.org/wpcontent/uploads/sites/7/consumer-
payments-covid-19-pandemic-2020-diary-consumer-payment-choice-supplement-2.pdf 
 
Letter from Elaine M. Howle, 2020-502, (November . 19, 2020) 
https://information.auditor.ca.gov/pdfs/reports/2020-502.pdf 
 
Mary Ann Milbourn, Unemployment Payouts Go Plastic in July, The Orange County Register, 
https://www.ocregister.com/2011/03/16/unemployment-payouts-go-plastic-in-july/ 
 
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NAB News, Why ‘Helpful Friction’ is Crucial in the Battle Against Scammers (Nov. 30, 2023), 
https://news.nab.com.au/news/why-helpful-friction-is-crucial-in-the-battleagainst-scammers/ 
 
Office of the Comptroller of the Currency, Acting Comptroller of the Currency Michael J. Hsu 
Remarks for the Financial Literacy and Education Commission’s Public Meeting, (July 10, 
2024), https://www.occ.treas.gov/news-issuances/speeches/2024/pub-speech-2024-75.pdf 
Office of the Comptroller of the Currency, Comptroller’s Handbook, “Corporate and Risk 
Governance,” July 2019, available at https://www.occ.gov/publications-andresources/ 
publications/comptrollers-handbook/files/corporate-risk-governance/pub-chcorporate- 
risk.pdf 
 
Office of the Comptroller of the Currency, Administrator of National Banks, “Comptroller’s 
Handbook: Internal Control,” (January 2001), available at https://www.occ.gov/publications-
andresources/publications/comptrollers-handbook/files/internal-control/pub-ch-internalcontrol. 
pdf 
 
Office of the Comptroller of the Currency, “OCC Finalizes Its Heightened Standards for Large 
Financial Institutions,” September 2, 2014, available at https://www.occ.gov/news-
issuances/news-releases/2014/nr-occ-2014-117.html 
Office of the Comptroller of the Currency, OCC Bulletin 2019-37: Operational Risk: Fraud 
Management Principals (July 24, 2019), https://www.occ.treas.gov/news-
issuances/bulletins/2019/bulletin-2019-37.html 
Office of the Comptroller of the Currency, PPM-5000-7, Section: Bank, Supervision Subject: 
Civil Money Penalties, November 13, 2018, available at https://www.occ.gov/news-
issuances/bulletins/2018/ppm-5000-7.pdf 
Office of the Comptroller of the Currency, Comptroller’s Handbook, “Litigation and Other Legal 
Matters,” Version 1.1, December 28, 2018, available at https://www.occ.treas.gov/publications-
and-resources/publications/comptrollershandbook/files/litigation-other-legal-matters/pub-ch-
litigation.pdf 
Office of the Comptroller of the Currency, Bank Secrecy Act (BSA), 
https://www.occ.treas.gov/topics/supervision-and-examination/bsa/index-bsa.html 
Office of the Comptroller of the Currency, OCC Issues Cease and Desist Order, Assesses $450 
Million Civil Money Penalty, and Imposes Growth Restriction Upon TD Bank, N.A. for 
BSA/AML Deficiencies (Oct. 10, 2024), https://www.occ.treas.gov/news-issuances/news-
releases/2024/nr-occ-2024-116.html 
Office of the Comptroller of the Currency, OCC Issues Cease and Desist Order, Assesses $450 
Million Civil Money Penalty, and Imposes Growth Restriction Upon TD Bank, N.A. for 
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BSA/AML Deficiencies (October 10, 2024), https://www.occ.treas.gov/news-issuances/news-
releases/2024/nr-occ-2024-116.html 
Oraz Kereibayev, AML Transaction Monitoring Rules: Best Examples, The Sumsuber (Oct. 3, 
2024) https://sumsub.com/blog/aml-transaction-monitoring-rules-scenarios/ 
 
Prepaid Accounts Under the Electronic Fund Transfer Act (Regulation E) and the Truth In 
Lending Act (Regulation Z), Federal Register, Vol. 81, No. 225, Rules and Regulations 
 
Post by @CA_EDD from X.com (Sep. 30, 2020), 
https://x.com/CA_EDD/status/1311335315043475457 
 
Trump White House, Proclamation on Declaring a National Emergency Concerning 
the Novel Coronavirus Disease (COVID-19) Outbreak (Mar. 13, 2020), 
https://trumpwhitehouse.archives.gov/presidential-actions/proclamation-declaringnational- 
emergency-concerning-novel-coronavirus-disease-covid-19-outbreak/ 
SAMUELSON, WILLIAM, and RICHARD ZECKHAUSER. “Status Quo Bias in Decision 
Making.” Journal of Risk and Uncertainty 1, no. 1 (1988): 7–59. 
http://www.jstor.org/stable/41760530. 
 
Thaler, Richard H., and Cass R. Sunstein. “Libertarian Paternalism.” The American Economic 
Review 93, no. 2 (2003): 175–79. http://www.jstor.org/stable/3132220. 
U.S. Secret Service, Massive Fraud Against State Unemployment Insurance Programs (May 14, 
2020 
U.S. Department of the Treasury, About the CARES Act and the Consolidated Appropriations 
Act, https://home.treasury.gov/policy-issues/coronavirus/about-the-cares-act 
U.S. Department of Labor Press Release, U.S. Department of Labor Announces New Guidance to 
States on Unemployment Insurance Programs, 
https://www.dol.gov/newsroom/releases/eta/eta20201230-1 
U.S. Department of Labor, Office of Inspector General, COVID-19: ETA and States Did Not 
Protect Pandemic-Related UI Funds From Improper Payments Including Fraud or From 
Payment Delays, https://www.oig.dol.gov/public/reports/oa/2022/19-22-006-03-315.pdf 
United States Government Accountability Office, GAO-22-105715: Significant Improvements 
Are Needed to Ensure Transparency and Accountability for COVID-19 and Beyond, 
https://www.gao.gov/assets/gao-22-105715.pdf 
United States Government Accountability Office, GAO-22-105715: Significant Improvements 
Are Needed to Ensure Transparency and Accountability for COVID-19 and Beyond (Mar. 17, 
2022) 
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U.S. Department of Justice, National Unemployment Insurance Fraud Task Force, 
Unemployment Insurance Fraud Consumer Protection Guide (Sep. 21, 2020), 
https://www.oig.dol.gov/public/Unemployment%20Insurance%20Fraud%20Consumer%20Prote
ction%20Guide,%20Final.pdf 
U.S. Department of Justice Press Release, COVID-19 Fraud Enforcement Task Force Releases 
2024 Report (Apr. 9, 2024), https://www.justice.gov/opa/pr/covid-19-fraud-enforcement-task-
force-releases-2024-report 
U.S. Department of Justice, COVID-19 Fraud Enforcement Task Force 2024 Report (Apr. 2024), 
https://www.justice.gov/coronavirus/media/1347161/dl?inline 
Yolanda Richardson, et al., Employment Development Department Strike Team Detailed 
Assessment and Recommendations (Sept. 16, 2020)  
Depositions  
Deposition Transcript of Shane Daniels, In re: Bank of America California Unemployment 
Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Feb. 6, 2024), and Exhibits 
Deposition Transcript of Robert Chestnut, In re: Bank of America California Unemployment 
Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Feb. 8, 2024) 
Deposition Transcript of Matthew Martin, In re: Bank of America California Unemployment 
Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Feb. 14, 2024), and Exhibits  
Deposition Transcript of Michael Letson, In re: Bank of America California Unemployment 
Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Feb. 16, 2024), and Exhibits  
Deposition Transcript of Jennifer Lennon, In re: Bank of America California Unemployment 
Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Feb. 23, 2024) 
Deposition Transcript of Renee Johnson, In re: Bank of America California Unemployment 
Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (May 7, 2024), and Exhibits  
Deposition Transcript of Ryan Schwartz, In re: Bank of America California Unemployment 
Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Dec. 4, 2024) 
Deposition Transcript of Bradley Garfield, In re: Bank of America California Unemployment 
Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Dec. 10, 2024), and Exhibits 
Deposition Transcript of Anne Holt, In re: Bank of America California Unemployment Benefits 
Litigation, Case No.: 3:21-md-02992-LAB-MSB (Jan. 8, 2025) 
Deposition Transcript of Faiz Ahmad, In re: Bank of America California Unemployment Benefits 
Litigation, Case No.: 3:21-md-02992-LAB-MSB (January 29, 2025), and Exhibits  
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Deposition Transcript of Melissa Ramirez, In re: Bank of America California Unemployment 
Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Feb. 11, 2025), and Exhibits 
Deposition Transcript of William Fox, In re: Bank of America California Unemployment Benefits 
Litigation, Case No.: 3:21-md-02992-LAB-MSB (February 13, 2025) 
Deposition Transcript of Paul Simpson, In re: Bank of America California Unemployment 
Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Feb 21, 2025), and Exhibits  
Expert Reports and Declarations  
Expert Class Certification Report of J. Daniel Kreis, In re: Bank of America California 
Unemployment Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Aug. 29, 2024) 
Expert Declaration of Teresa A. Pesce, In re: Bank of America California Unemployment 
Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Oct. 24, 2024) 
Declaration of Russell Cronan, In re: Bank of America California Unemployment Benefits 
Litigation, Case No.: 3:21-md-02992-LAB-MSB (Oct. 24, 2024) 
Declaration of Michael J. Letson in Support of Defendant's Memorandum of Points and 
Authorities in Opposition to Plaintiffs' Motion for Class Certification, In re: Bank of America 
California Unemployment Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Oct. 24, 
2024) 
Expert Class Certification Rebuttal Report of William J. Abernathy, Jr., In re: Bank of America 
California Unemployment Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Nov. 19, 
2024) 
Expert Rebuttal Report of J. Daniel Kreis, In re: Bank of America California Unemployment 
Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Nov. 21, 2024) 
Expert Report of William J. Abernathy, Jr., In re: Bank of America California Unemployment 
Benefits Litigation, Case No.: 3:21-md-02992-LAB-MSB (Mar. 3, 2025) 
Expert Report of Russell Cronan, In re: Bank of America California Unemployment Benefits 
Litigation, Case No.: 3:21-md-02992-LAB-MSB (Mar. 4, 2024) 
Expert Report of Teresa A. Pesce, In re: Bank of America California Unemployment Benefits 
Litigation, Case No.: 3:21-md-02992-LAB-MSB (Mar. 4, 2024) 
Expert Report of J. Daniel Kreis, In re: Bank of America California Unemployment Benefits 
Litigation, Case No.: 3:21-md-02992-LAB-MSB (Mar. 4, 2024) 
 
 
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Discovery Responses 
Bank of America’s Responses and Objections to Plaintiff Yick’s Fourth Set of Interrogatories, In 
re: Bank of America California Unemployment Benefits Litigation, Case No.: 3:21-md-02992-
LAB-MSB (Jan. 2, 2024) 
Bank of America’s Responses and Objections to Plaintiff Yick’s Fifth Set of Interrogatories 
(Exhibit 11 to Interrogatory 32), In re: Bank of America California Unemployment Benefits 
Litigation, Case No.: 3:21-md-02992-LAB-MSB (Feb. 2, 2024).  
Bank of America’s Second Set of Responses and Objections to Plaintiff Yick’s Seventh Set of 
Interrogatories (Interrogs. 39 & 42), In re: Bank of America California Unemployment Benefits 
Litigation, Case No.: 3:21-md-02992-LAB-MSB (April 23, 2024) 
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NATALIE M. LOEBNER
A driven leader with diverse subject matter expertise in data analytics, machine learning, 
software development, federal litigation (civil and criminal), government data (both enterprise and 
analytical environments), and complex financial fraud investigations. Eager to leverage existing 
relationships and domain expertise to build coalitions within and across federal agencies and the 
private sector. Proven track-record of bringing together the right mix of technical and domain 
experts to reduce communication frustrations. Armed with an understanding from both government 
and private sector work to develop collaborative environments that lead to meaningful 
improvements. History of success of inspiring reluctant stakeholders to adopt new technologies and 
approaches. Passion for developing accessible trainings for “non-data people” to understand how to 
communicate with technologists.   
SELECTED ACCOMPLISHMENTS 
•
Cross-Sector Projects: Presently leading an initiative with the non-profit The Knoble, an anti-
human crime organization, to assist financial institutions and law enforcement improve
information sharing to address cyber enabled financial crimes. The objective of the project is to
help law enforcement be more strategic in their requests and create a uniform taxonomy of
terms to reduce miscommunication. Developed a “wish-list” of data fields maintain at financial
institutions that would enable law enforcement to investigate cases more effectively and
efficiently. The wish-list was developed in conjunction with numerous federal law enforcement
agencies where I leveraged my understanding of agency operations to bring together analysts
with the right skillsets and expertise.  With the help of The Knoble’s membership of financial
institutions created a survey to evaluate the data fields that most institutions would be able to
pilot in 2025.
•
IRS-CI Consolidated Report (CICR): Personally, developed a proof-of-concept report
demonstrating to stakeholders that agents needed a better way to immediately understand
taxpayer interactions over a 10-year period. I worked with CI Agents, IRS Analysts, and
software developers to perfect the report contents during a 4-month pilot. Resulting in a report
that, based on a TIN, pulls validated data to present a 10-year history of the taxpayer’s
interactions with the IRS, FinCEN, and other available data sets. While also ensuring that all
data elements were validated with system of record sources and hyper-linked to the data lake
metadate tables. Oversaw and approved documentation and training materials for the users
which included live training for every IRS-CI field office which also included how to leverage
existing complementary tools. The training addressed a secondary need - to educate the field
offices about analytical resources over and above the CICR. The report is currently available to
all CI field offices and developing spin-off reports for civil functions. Based on user feedback
the report reduces at least 8 hours of manual research per target to ~1 minute. Over
15,000 reports have been requested since mid-2022.
APPENDIX B
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• 
Optimizing Financial Record Requests (OFRR) [cross-agency effort to streamline 
discovery from financial institutions]: The crux of the initiative is to remove the manual and 
time intensive process of converting PDF banks statements to tabular data so the information 
can be evaluated by obtaining structured data directly from the financial institutions. The genesis 
of the effort began as 1-hour free training I developed for law enforcement on the benefits of 
requesting structured transactional data maintained in banking core systems and how to modify 
the current subpoena attachments to obtain this electronically stored information using Federal 
Rules of Evidence 902(13) & (14). I independently researched the topic by engaging with 
treasury agencies, primarily regulators, and FinTechs that use API data hooks into banking 
systems. Based on my training the project grew to be a funded cross-agency initiative run by 
IRS-CI and involving FBI, Secret Service, Homeland Security, DEA, and the US Postal Service 
Criminal Investigations. I am currently serving as a domain expert to help law enforcement 
engage with private sector financial institutions which involve training and advocacy. Working 
with various law enforcement partners to survey existing tools that may be augmented to ingest 
the new data sets, researching commercially available solutions, and developing prototypes for 
custom analytical reports. To date the effort caused several financial institutions to voluntarily 
create CSV reports to pilot with law enforcement.  There are already success stories where cases 
were solved in a fraction of the time with fewer investigative resources.  
 
• 
Longstanding Efforts: Providing program management and guidance for longstanding projects 
with IRS to ensure mature platforms are housed in the appropriate environments, that 
technology stacks are periodically refreshed, and engaging end-users to update or expand 
functionality as operational needs change. This necessitates engaging with executives, data 
owners, IT security stakeholders, and the developers to ensure clear and complete 
communication. Example efforts:  
o Overseeing the team tasked with updating entity resolution processes to leverage newly 
added IRS data sets, utilize updated technology, and address shifts in IRS objectives.  
o  Lead the initiative to successfully move a project inventory platform from the 
development environment to the IRS IT environment because it had matured into an 
enterprise solution.   
o Managed the update of a platform that needed to conform with new IT security 
requirements. I identified and worked with the power users and development team to 
implement additional changes to improve functionality of the tool to enhance the user 
experience while simultaneously completing the IT security refresh. Due to feature 
updates, usage of the tool increased from a handful of users to over 100 users.    
  
CONSULTING EXPERIENCE  
 
  
LOEBNER CONSULTING, LLC |Alexandria, VA | Nov. 2022 to Present  
President/Owner: Consulting for RegTech and Financial Services companies that offer, or seek to 
offer, tools and platforms that support financial investigations, AML/BSA compliance, and complex 
financial litigation. Evaluate existing tools to help companies understand both public and private 
sector applications. Advise on future development needs and, in certain engagements, provide 
domain support for that development. Some recent examples of this include: (1) identifying when 
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tools will exceed the target userbase’s technical skills and offer suggestions for teaming partners that 
can fill the services gap; (2) identify gaps in proposed workflows to ensure successful integrations 
for companies seeking to sell batch data; and (3) advising on customer engagement strategies to help 
companies effectively and honestly communicate to their offerings. Currently supporting several 
emerging businesses in the digital asset space – with a focus on companies that seek to address off 
chain analytics, evaluation of blockchain application protocols, conversion of digital assets to FIAT 
markers, and beneficial owner identification. Requested speaker at domestic and international 
conferences and summits by both government and private sector.  
  
EASTPORT ANALYTICS, INC. | Washington, D.C. | Nov. 2019 to September 2023  
Executive Director of Analytical Services: Responsible for the successful execution of analytical projects 
across all company engagements. Providing subject matter expertise in tax administration, litigation, 
permissible data usage, data modeling, solution architecture, and analytics. Working with clients to 
identify what problem they are really trying to solve – to put their needs in terms (“requirements”) 
that a technical development team can execute, including working with government clients to 
translate internal manuals and standard operating procedures into technical terms. Engaging directly 
with stakeholders, end-users, data analysts, engineers, subject matter experts, and executives to 
ensure clients like the Department of Justice and the IRS obtain meaningful outcomes. See notable 
projects above for some examples. Focuses on helping law enforcement efficiently and 
programmatically identify crucial information, records, and trends buried in high-volume data sets or 
sometimes first-party data inaccessible to due current user tools. Leverages interdisciplinary 
background in technology, analytics, law, investigations, and training to address operational needs 
while allowing law enforcement to pierce through the mysterious “black box” often associated with 
technical support. Heavy emphasis on educating stakeholders so they become increasingly savvy 
consumers of technology.  
  
RELEVANT LEGAL EXPERIENCE  
 
  
US DISTRICT BANKRUPTCY COURT, E.D. CAL. | Sacramento, CA | Aug. 2018 to Oct. 2019 Law 
Clerk to Judge Christopher Klein: Was the sole law clerk who prepared bench notes, pre-hearing 
dispositions, and drafted orders, judgements, and opinions for the active docket often exceeding 200 
matters a week. Personally developed templates and macros to efficiently handle routine matters, 
freeing up time to conduct in-depth research for more complex issues before the court relating to 
Chapter 11 and Chapter 15 (Cross-Border Insolvency) cases. Successfully trained successor to utilize 
time saving automations.   
  
US DEPARTMENT OF JUSTICE, TAX DIVISION | Washington, D.C. | Nov. 2014 to Jul. 2018  
Civil Trial Attorney: Lead attorney on over twenty active cases at any given time, including, but not 
limited to, suits to permanently enjoin unscrupulous tax return preparers and promoters, defend IRS 
claims in bankruptcy court, reduce federal tax assessments to judgment, enforce federal tax liens 
involving alter ego and nominee allegations, and enforce IRS administrative summonses. From 2015 
to 2018 served as an Electronically Stored Information (ESI) Coordinator for the division, providing 
guidance and training to trial attorneys on topics such as technology assisted review (TAR), 
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discovery requests from relational databases, and leveraging data maintained in IRS databases.  
Efforts with notable relevance to job:  
• 
As an ESI Coordinator, supported cases with novel or voluminous data questions. 
Including supporting the John Doe Summons litigation team in the first digital asset case 
against Coinbase. Provided guidance on data that the exchange should be maintaining 
based on my prior work with FinCEN data at the US Attorney’s Office and explained 
potential options for requesting data from relational data sets to ensure attributes needed 
to identify account holders could be mapped together.   
• 
On a large national preparer injunction case worked closely with IRS Counsel, SB/SE, 
and IRS RAAS to craft data queries to substantiate habitual and systemic conduct 
allegations.  The same case also necessitated discovery of back-end data sets from 
defendants’ proprietary software tools, requiring technical expertise in both data and 
eDiscovery obligations when meetings with defendant’s IT divisions to agree on 
production requirements.  The two large datasets, when analyzed together quickly 
presented a strong story in favor of the IRS’ position and resulted in a swift settlement. 
The resulting process has been leveraged for subsequent cases.  
  
JEMISON & MENDELSOHN, P.C. | Montgomery, AL | Oct. 2013 to Oct. 2014  
Contract Legal Research and e-Discovery Support: Provided legal research and analysis for pending Qui Tam 
matters involving tax violations of an international corporation. Conducted internal investigations to 
determine the veracity of allegations regarding embezzlement and trust mismanagement. 
Responsible for selecting experts and technology platforms to support the needs of ongoing 
litigation.   
  
US ATTORNEY’S OFFICE, S.D. ALABAMA | Mobile, AL | Sep. 2012 to Sep. 2013  
SAUSA (Civil Division): Predominately handled affirmative matters involving civil asset forfeitures,  
Bank Secrecy Act (BSA) violations, and False Claims Act violations. Supervised and managed 
monthly meetings of interagency financial crimes task force (21 law enforcement agencies 
represented).  Delivered training on the BSA and FinCEN filings to federal agents and bank 
compliance departments. Replaced outdated FinCEN filing paper review with data-driven process, 
enabling cross-agency deconfliction, prompt identification of relevant new filings, and the cross-
reference of CTR filings with outstanding restitution and judgment matters. Lead changes within the 
US Attorney Office and partnering agencies to adopt a new way of working with data and 
integrating data analyses from the agencies in efforts that were typically only attended by agents.  
Fostered confidence in long standing members for the task force with plain language explanations of 
technical terms to reduce barriers (often mental barriers) to adopting new technologies. Convinced 
the US Attorney and Criminal Chief to champion a new type of collaboration that led to increased 
identification of complex financial cases in the district.   
  
UW LAW FEDERAL TAX CLINIC | Seattle, WA | Summer 2009 and Sep. 2011 to Apr. 2012  
Taxpayer Advocate: Represented low-income taxpayers in disputes before the IRS involving both 
personal and employment tax matters. Successfully defended a non-profit organization contesting a 
tax liability assessed by the IRS predicated on drug-related proceeds surreptitiously laundered 
through its bank accounts. Leveraged timestamp and GPS metadata associated with ATM 
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transactions to demonstrate to the IRS that the problematic conduct was not associated with the 
operations of the entity.  
  
LAW OFFICES OF SHEPPARD MULLIN | San Francisco, CA | Jan. 2007 to Jul. 2008   
Paralegal/Electronic Discovery Specialist, Antitrust and Trade Regulation Practice Group: Supported attorneys 
in all aspects of litigation. Worked on state, federal, and international cases in both criminal and civil 
matters. Worked with attorneys to data mine large discovery productions with millions of records. 
Implemented creative use of Boolean logic to isolate key documents and leveraged automated 
scripts to run resource heavy computations overnight.   
  
ADDITIONAL EXPERIENCE  
 
  
STANFORD LINEAR ACCELERATOR CENTER| Stanford, CA | Summer 2003 and 2004   
Summer Intern: Wrote computer code using C++, Fortran, OpenGL, and Hexadecimal to visually 
represent data generated in connection with the center’s model for “The Next Linear Collider.” The 
result was an interactive visual representation of a particle’s path in the collider. Users could select 
different modes to display heat, magnetism, and positions in time.    
  
EDUCATION   
 
  
UNIVERSITY OF WASHINGTON SCHOOL OF LAW | Seattle, WA   
Master of Laws in Taxation, March 2012 -- Roland Hjorth Merit Scholarship Recipient   
Juris Doctor, Jun. 2011 -- ABA Janet D. Steiger Fellowship Recipient   
  
WHITMAN COLLEGE | Walla Walla, WA   
Bachelor of Arts, May 2006; Majors in Pure Mathematics and Rhetoric & Film Studies -- Penrose 
Merit Scholarship, Perry Research Grant  
Specific mathematics course focus in linear algebra, mathematical modeling, and machine learning  
    
Case 3:21-md-02992-GPC-MSB     Document 634-11     Filed 01/09/26     PageID.52151 
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