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
| Court | U.S. District Court for the Southern District of California |
|---|---|
| Filed | 2026-01-09 |
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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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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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
(
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52123
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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 (
)
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52124
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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
).
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52125
Page 45 of 71
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
).
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52126
Page 46 of 71
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
.
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52127
Page 47 of 71
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
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52128
Page 48 of 71
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
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52129
Page 49 of 71
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
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52130
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
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52131
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
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52132
Page 52 of 71
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
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52133
Page 53 of 71
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
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52134
Page 54 of 71
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
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52135
Page 55 of 71
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
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52136
Page 56 of 71
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
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52137
Page 57 of 71
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
Page 59 of 71
“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
Case 3:21-md-02992-GPC-MSB Document 634-11 Filed 01/09/26 PageID.52140
Page 60 of 71
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
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Financial Institutions,” September 2, 2014, available at https://www.occ.gov/news-
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Management Principals (July 24, 2019), https://www.occ.treas.gov/news-
issuances/bulletins/2019/bulletin-2019-37.html
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Million Civil Money Penalty, and Imposes Growth Restriction Upon TD Bank, N.A. for
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releases/2024/nr-occ-2024-116.html
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Million Civil Money Penalty, and Imposes Growth Restriction Upon TD Bank, N.A. for
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releases/2024/nr-occ-2024-116.html
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Lending Act (Regulation Z), Federal Register, Vol. 81, No. 225, Rules and Regulations
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2020
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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
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Are Needed to Ensure Transparency and Accountability for COVID-19 and Beyond,
https://www.gao.gov/assets/gao-22-105715.pdf
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Are Needed to Ensure Transparency and Accountability for COVID-19 and Beyond (Mar. 17,
2022)
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ction%20Guide,%20Final.pdf
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2024 Report (Apr. 9, 2024), https://www.justice.gov/opa/pr/covid-19-fraud-enforcement-task-
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https://www.justice.gov/coronavirus/media/1347161/dl?inline
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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
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