Consumer Data Industry Association Fair Lending Teleseminar

May 10, 2016

D. Jean Veta, Covington & Burling LLP

Michael Nonaka, Covington & Burling LLP

Marsha J. Courchane, Charles River Associates

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The CFPB’s Increasing Role in Fair Lending

CFPB 2015 Fair Lending Report (April 2016)

New HMDA Rules

Supreme Court’s Inclusive Communities Decision

Fair Lending and its Intersection with UDAAP

Indirect Auto Lending

Small Business Lending

Agenda

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CFPB Examination and Enforcement Jurisdiction:

Insured depository institutions and insured credit unions with total

assets of more than $10 billion

Certain nondepository institutions that are “covered persons,”

including those who the CFPB has reasonable cause to

determine are engaging or have engaged in conduct that poses

risks to consumers with regard to the offering or provision of

consumer financial products or services and those who are larger

participants in certain markets for consumer financial services.

Credit bureaus;

Auto finance lenders;

Debt collectors

ECOA vs. FHA

The CFPB’s Role in Fair Lending

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The CFPB’s Role in Fair Lending

The CFPB’s Office of Fair Lending and Equal Opportunity

Patrice Alexander Ficklin, Director

From April 2014 – April 2015, fair lending enforcement actions

required institutions to provide approximately $224 million in

remediation to about 303,000 consumers

Number of fair lending enforcement actions by year: 2013 (two);

2014 (one); 2015 (four); 2016 (one to date)

Making policy through enforcement actions

E.g., Indirect Auto Lending

Leaked Memos

Use of Bayesian Improved Surname Geocoding (“BISG”)

Non-English Speaking Customers

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The CFPB’s Role in Fair Lending: Individual Liability

Broad scope – both individual liability and failure to admit

wrongdoing

Congress

DOJ – the Yates Memo; new FCPA pilot program

SEC under increasing scrutiny for failing to assess individual

liability

CFPB individual liability cases to date

Small entities; owner/operators

Clearly unlawful conduct

Potential sanctions

Civil money penalties

Sancho: ban from the “financial products industry”

Potential application in fair lending cases

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PHH’s appeal of Director Cordray’s ruling in captive reinsurance case has potentially broad consequences for industry and the Bureau

The facts

ALJ found that PHH violated RESPA through a mortgage reinsurance kickback scheme, and required disgorgement of $6.5 million

On administrative appeal, Director Cordray ordered a massive increase in disgorgement to $109 million. The increase was due to: Violation of RESPA every time PHH accepted a payment from a mortgage

reinsurer

Total gross revenue from premiums vs. profits

Did Cordray correctly interpret RESPA?

If so, did the industry have fair notice of his interpretation?

Contrary to HUD guidance

Contrary to widespread industry practice

Is there no statute of limitations applicable in CFPB administrative hearings?

Is the Bureau constitutional?

Single Director structure

Director can be fired only “for cause”

The CFPB’s Role in Fair Lending: CFPB v. PHH

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Fintech

Coming under increasing scrutiny

Relationship between fintech lenders and banks

Innovations in Underwriting

Big Data

Non-traditional data

New Small Business Initiative

Early stages

Fair lending risk

The CFPB’s Role in Fair Lending: The Future

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Changes to HMDA Reportable Data

Applicant/Borrower Characteristics Applicant/Borrower Age

Co-Applicant/Co-Borrowers

Collateral Characteristics Detailed Property Type – 1-Unit, 2-4 Units, 5+ Units

Detailed Occupancy Status – Differentiate b/w Investor Property & Second Home

Underwriting/Pricing Factors Applicant/Borrower Credit Score

Debt-to-Income Ratio

Loan-to-Value Ratio (via Property Type)

Combined Loan-to-Value Ratio

AUS Recommendation

80 Fed. Reg. 66128 (October 28, 2015)

http://www.gpo.gov/fdsys/pkg/FR-2015-10-28/pdf/2015-26607.pdf

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Additional HMDA Reportable Data (cont.)

Loan Characteristics Detailed Loan Purpose – Differentiate b/w Rate/Term & Cash-Out Refinance

ARM Features – Initial Fixed Rate Period

Prepayment Penalty Terms

Loan Term

Non-Amortizing Features – Balloon Loans, Interest Only Loans, etc.

Reverse Mortgages

HELOCs

Business Channel – Retail, Broker, Correspondent

Rates & Fees Note Rate

Total Points & Fees

Origination Charges

Discount Points

Lender Credits

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The new HMDA data will be mainly used to support disparate

impact theories under fair lending laws

This gives the government and private parties more data to

support fair lending claims

The ease of access through the CFPB’s website may also

advantage complaining parties

Conversely, a defendant will also have more data – including credit

characteristics – to respond to a complaint

Relying on the Inclusive Communities burden-shifting test—this

may give defendants a better chance of dismissing the action at

an early stage

Using history as a guide—the revised Regulation C will likely lead

to increased discrimination claims

Increased fair lending analyses by lenders will be required

The Likely Impact of the New Amendments to

Regulation C on Litigation

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Addresses the problem of “abusive disparate impact claims”— announced rules when litigating disparate impact litigation:

Courts must promptly assess the viability of a case

A mere “showing of a statistical disparity” is insufficient, as disparate impact litigation is not meant to impose “racial quotas” This is a significant requirement favoring lenders

The plaintiff must show that a statistical disparity was caused by the defendant’s policies and practices—causality required

The defendant may defeat a prima facie disparate impact claim by showing that the policy or practice at issue was “necessary to achieve a valid interest,” which may include “practical business choices and profit-related decisions”

If the defendant identifies such a valid interest, the burden shifts back to the plaintiff to show “that there is ‘an available alternative . . . practice that has less disparate impact and serves the [entity’s] legitimate needs.’”

Inclusive Communities Decision

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Underwriting Analysis

Detailed Loan Purpose

Detailed Occupancy Status

Detailed Property Type

Loan Amount

Debt-to-Income Ratio

Loan-to-Value Ratio

Combined Loan-to-Value Ratio

Applicant Credit Score

Automated Underwriting

Decision

Detailed Loan Product

Business Channel

Based upon a review of underwriting guidelines and rate sheets, develop

customized statistical models that may control for factors such as the following:

Pricing Analysis

Detailed Loan Purpose

Detailed Occupancy Status

Detailed Property Type

Loan Amount

Rate Lock Week

Loan-to-Value Ratio

Combined Loan-to-Value Ratio

Borrower Credit Score

Detailed Loan Product

Business Channel

MSA

Statistical Analysis of Underwriting and Pricing

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Protected

Class

Comparator

Group Protected Class

Odds

Ratio

Pseudo

R-Squared Model Total Denials Total Denials p-value

Conventional Mortgage Applications

African

American

Raw 44,126 11,459 3,008 1,493 2.810 0.000 0.013

Controlled 44,074 11,408 3,001 1,487 1.335 0.000 0.359

Hispanic Raw 44,126 11,459 3,999 1,433 1.592 0.000 0.003

Controlled 44,078 11,412 3,989 1,423 1.143 0.002 0.353

Hypothetical Lending Institution - Fair Lending

Analysis of 2014 HMDA – Incidence of Denial

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Protected

Class

Model

Comparator

Group

Loan Count

Protected

Class

Loan Count

Coefficient

(bps)

p-value

Adjusted

R-Squared

Conventional First Lien Mortgages

African

American

Raw 26,222 1,109 13.62 0.000 0.003

Controlled 26,222 1,109 3.32 0.000 0.765

Hispanic Raw 26,222 1,994 7.97 0.000 0.001

Controlled 26,222 1,994 1.27 0.016 0.767

Hypothetical Lending Institution - Fair Lending

Analysis of 2014 HMDA – APR Regression Results

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Fair Lending – Redlining Analyses

• Redlining investigations focus on geography – generally by looking at the

percentage minority in particular census tracts or by looking at the market

share on one institution compared to others offering the same product in the

same geography

• First look at originations for financial institution as % in majority minority tracts

compared to total originations.

• Next, calculate shares of financial institution in low, or high minority tracts

compared to peers’ shares in those tracts.

• Measure statistically significant differences.

Originations

Total

Count

Majority Minority

Tracts

Majority African

American and/or

Hispanic Tracts

Low Tract

Minority Share

High Tract

Minority Share

Low

Minority

Share /

High

Minority

Share Count % p-value Count % p-value % p-value % p-value

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There is no standard monitoring approach, but all involve an assessment

of the distribution of own institution’s lending activity during a given time

period within a defined geographic area versus a benchmark.

For own institution’s lending activity within the defined geographic area

determine the proportion that involved properties located in census tracts

with relatively high concentrations of minority residents.

Compare own institution’s proportion with that of lending activity for other

lending institutions operating in the same defined geographic area using

publicly available HMDA data from the same time period.

Prior to public release of HMDA data for given time period, monitor trends

by comparing own institution’s proportion during given time period with

own institution’s proportion during prior time period.

Monitoring of Redlining Risk

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First joint redlining consent order issued by CFPB/DOJ was for Hudson Savings Bank, September 24, 2015.

CMP of $5.5 million paid to CFPB

$25 million in “subsidy” fund

$200k advertising/marketing

$100k Financial Education

$750k CD partnerships

Required new branches be established

Required changes to CRA assessment areas to include full counties in NY and to add Camden NJ and Philadelphia

Drew heavily not only on own percentage applications in high minority areas, but explicitly compared to peers for majority-black-and-Hispanic tracts

Ignored higher than average approval rates and ignored purchased loans

CFPB/DOJ: Redlining Risk Remediation

Hudson Savings Bank

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Associated Bank – Conciliation Agreement dated May 22, 2015

$200 million settlement over 2008 – 2010 – focusing on allegedly

smaller “market share”

Subsidy assistance ($10 million – lower interest rates, down

payment assistance, closing costs)

$3 million to help home repairs

$1.35 million for community reinvestment and fair lending

education

$1.25 million for marketing/outreach (print media, radio, outreach)

4 new production centers in majority-minority areas (Milwaukee,

Chicago, Cook or DuPage counties)

3 new branches in majority-minority areas (Chicago, Milwaukee,

Racine)

HUD: Redlining Risk Remediation

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Lender Name

Total

Loans

Loans in

Majority

Minority

Tracts

Share in

Majority

Minority

Tracts

Institution A 40,000 2,400 6.0%

Institution B 35,000 2,275 6.5%

Institution C 30,000 1,500 5.0%

Institution D 24,500 1,470 6.0%

Institution E 13,750 688 5.0%

Institution F 12,500 563 4.5%

OWN INSTITUTION 7,000 210 3.0%

Institution G 6,500 488 7.5%

Institution H 3,750 113 3.0%

Institution I 3,500 350 10.0%

Institution J 3,500 525 15.0%

Institution K 1,250 63 5.0%

Institution L 750 68 9.0%

All Other Lenders 175,000 10,500 6.0%

Lenders with Similar Volumes 36,500 1,850 5.1%

Hypothetical Lending Institution – Redlining Risk

Assessment – Given Geographical Area -- 2014 HMDA

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Lender Name

Total

Loans

Loans in

Majority

Minority

Tracts

Share in

Majority

Minority

Tracts

Odds

Ratio p-value

OWN INSTITUTION 7,000 210 3.0% - -

All Other Lenders 175,000 10,500 6.0% 0.485 0.000

Lenders with Similar Volumes 36,500 1,850 5.1% 0.836 0.000

Hypothetical Lending Institution -- Analysis of Differences in

Proportions of Lending in Majority Minority Census Tracts

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Unfair – causes or is likely to cause substantial injury to

consumers; not reasonably avoidable; and injury not outweighed by

offsetting benefits (e.g. lower prices, more products). Substantial

injury involves monetary harm (e.g. costs or fees – even small

amount if large number of consumers impacted)

Deceptive – misleads or is likely to mislead in a material way

(central characteristics; expressed claims …); consumer’s

interpretation is reasonable (e.g. bait & switch). Evaluation with the

four P’s (prominence, presented in easy to understand format;

placement where consumers look; info in close proximity to claim) –

and may be interpreted relative to a particular target audience

UDAAP: Unfair, Deceptive and Abusive

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Materially interferes with consumer’s ability to understand a term or

condition

Takes unreasonable advantage of

Lack of understanding

Inability of consumer to protect its interest in choosing or using

product

Reasonable reliance of consumer on a covered person acting in

their interest

Abusive

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Assess quality of compliance risk management – review of internal

controls and policies and procedures

Doc review:

lists of products, descriptions, fees, disclosures, account statements

Procedure manuals and written policies

Mgmt and Board meeting minutes

Internal control and monitoring information

Compensation

Scripts, marketing, promotional materials

Third party agreements

Identify acts or practices that materially increase risk of UDA

Review Complaints

Gather facts and determine violations

Examination Objectives

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Are products underwritten on basis of ability to repay?

Does product profitability depend on penalty fees or back-end

rather than upfront fees?

Does product have high rates of re-pricing or changes in terms?

Does combination of terms increase difficulty in understanding?

Are there penalties for terminating relationship?

Does consumer bear fees or costs to get information about own

accounts?

Is product targeting to particular populations without making sure

marketing, disclosures suit that population?

Transaction Testing High Risk Areas Identified

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Indirect auto lending involves a prospective car purchaser’s

financing of the transaction through a third-party (bank, nonbank

affiliate of a bank, captive nonbank) that contracts with the dealer

Typical Process

Car dealer collects car purchaser’s information and forwards

information to indirect auto lenders that evaluate purchaser’s

creditworthiness

Indirect lenders decline to provide financing or give the dealer a

minimum interest rate at which the lender is willing to purchase

the financing agreement originated by the dealer

Indirect lenders may use discretion to modify interest rate, allow for

underwriting exceptions, or change other terms and conditions

Indirect lender may allow dealer to increase interest rate above

minimum rate and to receive compensation from the lender

based on the difference in the actual rate and minimum rate

Indirect Auto Lending

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CFPB Bulletin 2013-02 (Mar. 2013)

CFPB issued Bulletin 2013-02 applying ECOA to indirect auto lenders even though they do not interact with the car purchaser because they “participate” in decision to extend credit

Lenders may be liable for pricing disparities that exist on the basis of a prohibited class by (1) giving dealers discretion and incentive to increase actual interest rates and/or (2) allowing for discretion in modifying their own criteria and terms and conditions

Lenders may be liable under ECOA under disparate treatment and disparate impact theories of discrimination

Bulletin recommends steps to mitigate fair lending risk:

Impose controls on dealer discretion and compensation policies

Eliminate dealer discretion to increase interest rate and provide for alternative compensation structure

Robust fair lending compliance management program

CFPB Supervisory Highlights (Sept. 2014)

Pricing disparities are due to discretionary pricing adjustments

Indirect Auto Lending (cont.)

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CFPB Larger Participant Rule

In June 2015, CFPB finalized rule to supervise larger nonbank auto finance companies and issued related examination procedures

CFPB already had supervised auto lending conducted by larger banks and thrifts

Nonbank auto finance companies that originate more than 10,000 loans or leases are subject to CFPB examination for compliance with, among others, ECOA, TILA, UDAAP, and Consumer Leasing Act

Examination focal points:

Disclosure of auto financing terms/fair marketing

Provision of accurate information to credit bureaus

Fair debt collection

Fair lending

Other areas of emphasis: ancillary products, use of service providers, and repossession/bankruptcy

Indirect Auto Lending (cont.)

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Indirect auto lending has been major enforcement priority of CFPB:

Nonbank lenders – Westlake Services, LLC/Wilshire Consumer Credit

Security National Automotive Acceptance Company

First Investors Financial Services Group

Captives – Toyota Motor Credit Corporation (CFPB/DOJ)

American Honda Finance Corporation (CFPB/DOJ)

Dealers – Herbies Auto Sales

CarHop/Universal Acceptance Corporation

DriveTime/DT Acceptance Corporation

Banks – Fifth Third Bank

Ally Financial

U.S. Bank/Dealers’ Financial Services

Indirect Auto Lending (cont.)

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CFPB’s fair lending enforcement initiative on auto lending includes

coordinated action with the DOJ

CFPB and DOJ have Memorandum of Understanding to

coordinate fair lending enforcement (information sharing, joint

investigations, referrals and notifications)

CFPB examines institutions for compliance with ECOA and is

required to refer matter to the DOJ if there is a pattern or practice of

discrimination

Indirect Auto Lending (cont.)

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CFPB and DOJ fair lending enforcement actions relating to auto

lending typically include following findings and remedial provisions:

Minority borrowers typically paid higher dealer markups (i.e.,

interest rate increases) that were not based on creditworthiness

or transaction characteristics

Pricing discrepancies affected thousands of borrowers and were

attributable to discretionary pricing systems established by

indirect lenders with dealers

e.g., protected class borrowers charged 30 basis points more than similarly

situated non-Hispanic whites

Remedial provisions

Revised dealer compensation policy – pricing mark-up limited to 1.00-1.25%

depending on loan term

Remediation to customers who were “overcharged”

Retain settlement administrator to distribute funds

Indirect Auto Lending (cont.)

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CFPB’s efforts to regulate indirect auto lending have been sharply

criticized and subject to controversy

Auto dealers carved out of CFPB jurisdiction in Dodd-Frank Act

Regulation via enforcement

Disparate impact

Proxy methods for race and gender (see appendix)

House passed bill to withdraw Bulletin 2013-02 and subject

further efforts to notice and comment rulemaking

Indirect Auto Lending (cont.)

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ECOA applies to commercial credit and consumer credit – fair

lending protections apply to commercial credit applications and

originations

CFPB has started fair lending examinations of business lenders

while navigating Dodd-Frank statutory framework for CFPB

authority

Fair lending priorities: mortgages, auto loans, credit cards, small

business loans

Small Business Lending

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Section 1071 of Dodd-Frank Act

Amends ECOA to establish a robust HMDA-like data collection

requirement for loan applications from women-owned, minority-

owned, and small businesses

Requires itemization of data fields such as application date, loan

purpose, action taken, census tract, gross annual revenue of

business applicant, race/sex/ethnicity of principal owners

Purpose is to facilitate enforcement of fair lending laws and identify

business and community development needs of certain businesses

Small Business Lending (cont.)

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Section 1071 of Dodd-Frank Act

CFPB has delayed action on section 1071 rules several times

CFPB outreach and research

Interdisciplinary panel for rulemaking

Assistant Director, Small Business Lending position

Recent CFPB rulemaking agenda slated pre-rule activities for

September 2016

SBREFA panel

ANPR or NPR?

Public interest groups have pushed for expansive regulations

implementing section 1071

Small Business Lending (cont.)

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Questions

Jean Veta is described by Chambers USA as “one of the premier banking and financial regulatory

enforcement litigators in the country.” She defends financial institutions and their officers and

directors in civil and regulatory enforcement matters, government investigations, internal corporate

investigations, and congressional investigations. One client said, “[s]he brought a discipline and

toughness that was necessary in dealing with private litigants and was very experienced at dealing

with government litigations.” Based on her success in defending IndyMac Bank’s former CEO,

Jean was named Litigator of the Week by The American Lawyer, and was featured when

Covington was named an Am Law 2014 Litigation Department of the Year finalist. D. Jean Veta

+1 202 662 5294

jveta@cov.com

Michael Nonaka advises banks, financial services providers, and non-bank companies on a broad

range of compliance, enforcement, transactional, and legislative matters. He has worked

extensively with federal and state banking agencies and with other federal agencies authorized to

regulate financial services. Mr. Nonaka has significant experience advising clients on issues arising

under financial services legislation such as the Dodd-Frank Wall Street Reform and Consumer

Protection Act. He has advised clients on, among other areas in Dodd-Frank, regulation as a

systemically important financial institution, resolution planning, the Federal Deposit Insurance

Corporation’s orderly liquidation authority under Title II, and the scope of the Consumer Financial

Protection Bureau’s authority.

Michael Nonaka

+1 202 662 5727

mnonaka@cov.com

Dr. Marsha J. Courchane, Practice Leader of Financial Economics, specializes in financial

institution analyses for regulatory reviews and in support of litigation. Dr. Courchane is a leading

expert in the areas of mortgage and consumer lending and has worked with many of the largest

lenders in the US. Her client work and research focus on issues including fair lending, affordable

lending, credit scoring and the origination, pricing, securitization, and servicing of mortgages. Dr.

Courchane held a number of academic and professional positions prior to her consulting

experience. She served as Director of Financial Strategy and Research in the housing economics

and financial research department at Freddie Mac, and she was Senior Financial Economist at the

Office of the Comptroller of the Currency, focusing on fair lending and credit scoring matters for

large national banks.

Marsha J. Courchane

Charles River Associates

+1 202 662 3804

mcourchane@crai.com

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Appendix – BISG Proxy Calculations

http://www.census.gov/genealogy/www/data/2000surnames/index.html

Step 1: Surname

Race/Ethnicity Probabilities for surname

"Johnson" Race/Ethnicity Share

Hispanic 1.5%

African American 33.8%

Asian/PI 0.4%

American Indian 0.9%

White 61.6%

2+ Races 1.8%

Total 100.0% Source: Census Bureau

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Appendix – BISG Proxy Calculations

*Important – BISG does not use the Intra-tract shares commonly used in other geography-based proxies.

Step 2: Geography

18+ Population of Tract 0050.02 - Washington, DC

Race/Ethnicity Tract Counts

Intra-Tract

Shares*

U.S. 18+

Population Count Share of U.S.

Hispanic 1,340 24.5% 36,138,485 0.0037%

African American 1,008 18.4% 27,327,470 0.0037%

Asian/PI 307 5.6% 11,637,514 0.0026%

American Indian 15 0.3% 1,600,043 0.0009%

White 2,693 49.2% 157,123,289 0.0017%

2+ Races 109 2.0% 3,177,961 0.0034%

Total 5,472 100.0% 237,004,762 0.0023% Source: Census Bureau

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Appendix – BISG Proxy Calculations

1Elliott, Marc N. et al, “Using the Census Bureau’s Surname List to Improve Estimates of Race Ethnicity and Associated

Disparities,” Health Serv Outcomes Res Method (2009) 9:69–83.

Step 3: BISG1 Probabilities

BISG Calculation Example

Race/Ethnicity

Surname

"Johnson"

Tract 0050.02

Wash, DC

BISG

Probability

Vector

Hispanic 1.5% 0.0037% 2.3%

African American 33.8% 0.0037% 51.1%

Asian/PI 0.4% 0.0026% 0.5%

American Indian 0.9% 0.0009% 0.3%

White 61.6% 0.0017% 43.2%

2+ Races 1.8% 0.0034% 2.6%

Total 100.0% 0.0023% 100.0% Source: Census Bureau