Fair Lending Analysis Made Easy - CUNA Councils...The lawsuit alleges that SunTrust's policies promoted racial discrimination by giving loan officers and mortgage brokers significant

Fair Lending Analysis Made Easy

Presented by: Ian DunnCEO,

Agenda

The Basics

Assessing Your Potential Fair Lending Risk Exposure

Key Fair Lending Analysis & Reporting3

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The Basics- Fair Lending Laws and Prohibited Basis Groups

Fair Housing Act (FH Act)Prohibits discrimination in residential real estate transactions:

-making loans to buy, build, repair, improve a dwelling-purchasing real estate loans-selling, brokering, or appraising residential real estate-selling or renting a dwelling

Equal Credit Opportunity Act (ECOA)Prohibits discrimination in any aspect of a credit transaction

Reg B, HMDA, and CRA

Then Basics-Fair Lending Laws and Prohibited Basis Groups

Race/Color

Religion

Gender

Age

Marital Status

Source of Income

National Origin

Handicap

Familial Status

Both ECOA FH Act

The Basics- Types of Discrimination

Disparate Treatment>Overt-Lender openly discriminates on a prohibited basis (can be written or verbal)

>Comparative Evidence-Differences in treatment not fully explained by legitimate non-discriminatory factors

*A disparate treatment claim does not require evidence the lender intended to discriminate or was motivated by prejudice.

Disparate Impact (“Effects Test”)>When an otherwise neutral policy or practice has a disproportionately negative impact on persons from a prohibited basis group.

*A disparate impact claim must show the challenged policy or practice is either:1) not justified by a valid business propose or 2) that the business justification could be accomplished using a less discriminatory alternative.

Applies to: Applicants Associations Occupants Neighborhood

The Basics- Prohibited PracticesFail to provide or provide different information or services regarding any aspect of the lending process

Discourage or selectively encourage individuals who inquire or apply for credit

Refuse to extend credit or use different standards in determining whether to extend credit

Vary the terms of credit offered

Use different standards to evaluate collateral

Treat a borrower differently in servicing a loan or making invoking default remedies

Use different standards for pooling or packaging loans in the secondary market

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The Basics- Fair Lending touches every part of Lending Process

Application Activities-Level of Assistance-Use of 3rd Parties-Initial Terms & Conditions

Pre-Application Activities-Advertising & Market Selection-Channels-Responding to Inquiries

Underwriting/Closing-Approval Criteria-Final Terms & Conditions-Appraisal Practices

Servicing/Post-Closing-Modifications-Default Remedies-REO

The Basics-Example 1

-For joint applicants, combine the debts and income of married joint applicants to calculate debt-to-income ratio and for unmarried joint applicants calculate an individual debt-to-income ratio for each applicant.

Discrimination? -Yes!

Prohibited Basis: -Marital Status

Discrimination Type: -Disparate Treatment: Overt

Prohibited Practice:-Refuse to extend credit or use different standards (married vs. unmarried) in determining

whether to extend credit

The Basics-Example 2

Discrimination? -Potentially

Prohibited Basis: -National Origin

Discrimination Type: -Disparate Treatment: Comparative Evidence

Prohibited Practice:-Vary the terms of credit offered

Borrower National Origin Number of Home Purchase Loans Average Interest RateHispanic 125 12.5%

Non Hispanic White 150 8.5%

The Basics-Example 3The financial institution has a policy not to make home loans less than $100,000.

Discrimination? -Potentially

Prohibited Basis: -Race, Familial or Marital Status

Discrimination Type: -Disparate Impact

Prohibited Practice:-Fail to provide services, discourage applications

Notes:-Does this policy effectively exclude certain low income and/or minority majority areas?-Does the $100k min. policy have a legitimate business need? Can that need be satisfied in a less discriminatory manner?

The Basics-Example 4Discrimination?

-PotentiallyProhibited Basis:

-Race, Familial or Marital StatusDiscrimination Type:

-Disparate ImpactProhibited Practice:

-Fail to provide services, discourage applicationsNotes:-With few exceptions, there are no compliance

requirements to advertise in a foreign language-Are all triggers/key terms/disclosures also in the

foreign language?

Agenda

The Basics


Key Fair Lending Analysis & Reporting3

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Marketing Activities

Discretion Permitted

Exception Handling

Use of Third Parties

How and where you offer credit products are not just

fundamental business decisions, they have fair lending implications as

well.

The level of individual discretion permitted in

certain activities such as approvals or pricing

increases an institution’s fair lending risk.

Exceptions should be made consistently for similar

reasons for similarly situated applicants or borrowers.

From a fair lending perspective TPOs are the financial

institution and if any TPO does not comply with fair lending

regulations the financial institution may be as culpable

as if you were the initial creditor.

Incentives & Compensation

Compensation tied to loan production can be a

great motivator for a tough job, but it can also

lead to significant fair lending issues.


Redlining

The illegal practice of refusing to make loans or imposing more onerous terms on borrowers because of the racial, national origin, or other prohibited basis characteristics of the residents of a subject neighborhood.

Reverse Redlining

Reverse redlining is the deliberate targeting of residents of such neighborhoods with less advantageous or potentially predatory products.

Marketing ActivitiesFigure 1. Home Owner’s Loan Corporation, Philadelphia, PA., 1936

Evans Bank (NY): https://www.youtube.com/watch?v=nZix2Joay-0

The Department of Justice (DOJ) recently settled two redlining cases: one against Citizens Bank of Flint, Michigan and one against Midwest BankCentre of St. Louis County, Missouri.

Steering

The guiding of an applicant or a borrower to a less advantageous product on a prohibited basis rather than on the legitimate needs.

(Steering in Real Estate refers to guiding a prospective purchaser towards or away from certain neighborhoods based on race.)



Advertising/Pre-screening

Advertising methods that could discourage individuals from applying for loans or in media that exclude specific regions are sources of fair lending risk.

1 (Low) 2 3 4 5 (High)

Market Demographics:Stable, Low Competition, Low Diversity Evolving, High Competition, High Diversity

Delivery Channels:Limited, Processes don’t vary, No Subprime Subsidiaries/Affiliates

Multiple, Processes vary, Subprime Subsidiaries/Affiliates

Product Complexity:Traditional MixNew Products Reviewed for Fair Lending Compliance

Complex and Non-traditional MixNew Products Not Reviewed

Advertising:Limited, No Recent Changes, Broad Based Extensive, Constantly Changing, Targeted

Score:

Marketing Activities (Score 1 to 5)


The lawsuit alleges that SunTrust's policies promoted racial discrimination by giving loan officers and mortgage brokers significant discretion to vary a loan's interest rate and other fees from the prices the company set based on a borrower's creditworthiness.In 2009, new SunTrust Mortgage policies reduced the discretion of loan officers and mortgage brokers to alter prices. The company also now requires variations in prices to be documented and reviewed by a supervisor.

SunTrust Mortgage Inc. has agreed to pay $21 million Settlement

Discretion Permitted (Score from 1 to 5)

Score:

How much discretion is permitted? Does the degree of permitted discretion vary by geography, channel, or activity? Can the exercise of discretion impact compensation?

1 (Low) 2 3 4 5 (High)

Limited and Consistent Discretion Broad and Variable Discretion

Discretion Criteria is Clear Discretion Criteria is Broad or Non-existent

Discretion Does Not Impact Compensation Discretion Does Impact Compensation

Exception Handling

Score:

Is file documentation accurate and descriptive? Is rationale objective? Are exceptions low in number? Do certain loan officers, branches, etc. have a higher level of granting exceptions?

1 (Low) 2 3 4 5 (High)

Exceptions are Objective and Well Documented Exceptions are Subjective and Not Well Documented

Exceptions are Few in Number Exceptions are Many

Exception Handling (Score from 1 to 5)

Consumer

Dealer

Lender(s)

DealerConsumer

Dealer

Lender

1. Consumer submits application to Dealer

2. Dealer submits application to Lender(s)

3. Lender(s) submit “buy rate” and dealer compensation to dealer

4. Dealer sets actual rate to consumer

5. Consumer and Dealer close the sale

Lender(s) Lender(s)

6. Dealer sales the contract to the chosen Lender

Q: Where is a major source of Fair Lending Risk in this process?

Use of Third Parties

Score:

Do you use Third Party Operators (TPOs)? Is due diligence performed? Do you have written agreements addressing fair lending obligations? Do agreements define who is responsible and accountable? Do you receive regular reporting? Does the TPO have frequent complaints? Does the TPO conduct fair lending training?

1 (Low) 2 3 4 5 (High)

Little or No Use of Third Parties Extensive Use of Third Parties

Due Diligence Performed No Due Diligence Performed

Written Agreements/Accountability Defined No Written Agreements/Accountability Not Defined

Receive Regular Reporting Receive No Reporting

Regular Fair Lending Training No Fair Lending Training

Use of Third Parties (Score from 1 to 5)


https://www.youtube.com/watch?v=t-W_ilJqt4A

Bank of America $335 Million Settlement “Compensation structure was such that a (loan officer) would make more money if they put people into a poor quality, higher priced loans.”

Lisa Madigan, Attorney General, Illinois

Incentives & Compensation (Score from 1 to 5)

Score:

Are your loan officer and other decision makers compensated on loan production? Is compensation tied to higher pricing or higher fees?

1 (Low) 2 3 4 5 (High)

Incentives & Compensation Not Tied to Loan Production

Incentives & Compensation Tied to Loan Production

Incentives & Compensation Not Tied to Loan Pricing and/or Fees

Incentives & Compensation Tied to Loan Pricing and/or Fees


Source Score

-Marketing Activities (1-5)

Discretion Permitted (1-5)

Exception Handling (1-5)

Use of Third Parties (1-5)

Incentives & Compensation (1-5)

TOTAL SCORE (5-25)

RISK LEVEL (Low to High)

Low Medium-Low Medium Medium-High High

<=7 8-12 13-16 17-21 >=22

What is your total score and risk level?

Risk Level (Based on Total Score):



Discrepancies by Prohibited Basis Groups

=HIGH RISK!!!

Does statistical analysis show higher rates/fees being charged to minorities

or other prohibited basis groups?

Is compensation tied to higher priced loans?

Can loan officers or other decision makers set rates

and fees?

Agenda

The Basics


Key Fair Lending Analysis & Reporting

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Self Evaluation vs. Self TestA self evaluation or a self test may provide for a streamlined exam.

Self Evaluation Self Test

Essentially any fair lending analysis of loan and application data that is not a “self test”.

Examples: -Comparative File Reviews-Statistical Audit/Reports

Any voluntary program, practice, or study that is designed and specifically used to assess an institution’s compliance with fair lending laws and creates data not available or derived from loan, application, or other records related to a credit transaction.

Examples-Paired Testing-Surveys

“Likely” violations require “Appropriate Corrective Action”

Appropriate Self-Testing can create legal privilege.

Consult your legal counsel!

Fair Lending Basic Building Blocks

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Demographics of your Institution’s “Lending Area”.

Demographics of the Census Tract where each applicant/borrower resides.

Applicant/Borrower data such credit score, DTI, PTI, length of employment, length at residence, etc. Also, race, gender, and age of borrower as appropriate.

Application/Loan data such as channel used, decision makers involved , status, rate, term, amounts, etc.

Collateral information such as type and original value.

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Fair Lending Advanced Building Blocks

1 Expected Race

Estimating Expected Race (Bayesian Improved Surname Geocoding-BISG):Step 1. Match Borrower Address to Census Tract

Step 2. Calculate Baseline Expected Race/Ethnicity Using Census Data

Step 3. Match Borrower Surname to Name/Race Database

Step 4. Apply Bayesian Statistics to Update Baseline Expected Race

P(Ethnicity|Given Surname) = P(Given Surname|Ethnicity)*P(Ethnicity)P(Given Surname|Ethnicity)*P(Ethnicity) + P(Given Surname|Not Ethnicity)*P(Not Ethnicity)


2 Expected Gender

Estimating Expected Gender:-Match Borrower First Name to Gender Name Database

An Example

2.14(Hispanic)

The 4 Key Fair Lending Ratios & Reports

% of PBG in Lending Area / % of PBG in Portfolio

% of PBG in Product /% of Portfolio

% of PBG Denied /Expected % of Denied

Avg. Rate of PBG /Expected Rate of PBG

Redlining Ratio Steering RatioExpected Denial

RatioExpected Pricing

Ratio

2.78(Female)

*PBG is a Prohibited Basis Group such as Race, Gender, Age, Etc.

1.93(HELOC-

Subprime)

1.22(Black)

2.14(Hispanic)

% of PBG in Lending Area / % of PBG in Portfolio

Redlining Ratio


Percent of Applications

(or Loans) Percent of Lending

Area Redlining RatioAsian/Pacific

Islander3.0% 2.5% 0.83

Black 7.0% 7.5% 1.07

Hispanic 14.0% 30.0% 2.14

Native American 1.0% 1.0% 1.00

White 70.0% 55.0% 0.79

Other/Not Reported

5.0% 4.0% 0.80

% of PBG in Product /% of PBG in Portfolio

Steering Ratio

1.93(HELOC-

Subprime)


Asian/Pacific Islander Black Hispanic

Native American White Other

First Mortgage 2.0% 7.5% 8.0% N/A 80.5% 2.0%Second Mortgage 1.2% 3.4% 12.2% N/A 82.2% 1.0%

HELOC (Prime) 1.4% 4.5% 5.5% N/A 87.1% 1.5%HELOC (Sub Prime) 1.6% 14.2% 10.6% N/A 72.4% 1.3%

Other 1.7% 0.5% 2.5% N/A 93.6% 1.7%Total 2.0% 7.4% 7.9% N/A 81.3% 1.8%

Asian/Pacific Islander Black Hispanic

Native American White Other

First Mortgage 1.00 1.02 1.01 N/A 0.99 1.10Second Mortgage 0.59 0.46 1.54 N/A 1.01 0.55

HELOC (Prime) 0.69 0.61 0.69 N/A 1.07 0.83HELOC (Sub Prime) 0.78 1.93 1.34 N/A 0.89 0.69

Other 0.83 0.07 0.32 N/A 1.15 0.94


Expected Acceptance Rate

Estimating Expected Acceptance Rate (Multiple Regression Analysis):1. Sample Construction

Similar loan types, similar origination periods, large sample size

2. Regression Estimation

Independent Variables: Credit Score, Reference Rate, Loan-to-Value, Etc.

Estimate Variable Coefficients

3. Apply Resulting Coefficients to each loan to estimate Expected Interest Rate

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% of PBG Denied /Expected % PBG Denied

Expected Denial Ratio Gender

All Application

Count

Denied Application

Count% Denied Expected

% DeniedExpected

Denial Ratio

Male 400 10 2.5% 2.7% 0.93

Female 200 50 25.0% 9.0% 2.78

Joint 300 30 10.0% 9.0% 1.1

Unknown 50 0 0% 0% N/A

Not Reported 50 0 0% 0% N/A

TOTAL/OVERALL 1000 90 9.0% 9.0% 1.0

2.78(Female)


Expected Interest Rate

Estimating Expected Interest Rate (Multiple Regression Analysis):1. Sample Construction

Similar loan types, similar origination periods, large sample size

2. Regression Estimation

Independent Variables: Credit Score, Reference Rate, Loan-to-Value, Etc.

Estimate Variable Coefficients

3. Apply Resulting Coefficients to each loan to estimate Expected Interest Rate

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Race/Ethnicity Average Rate Average Expected Rate Difference Expected Pricing

Ratio

Asian/Pacific Islander 6.8% 0.97

Black 12.8% 1.22

Hispanic 11.5% 1.02

White 6.0% 1.00

Other 7.0% 1.00

Not Reported 0% 1.00Avg. Rate of PBG /Expected Rate of PBG

Expected Pricing Ratio

*PBG is a Prohibited Basis Group such as Race, Gender, Age, Etc.-Show by: Race, Gender, and Age-Significant differences between average rates and expected rates are an indication of potential disparate treatment-You can also compare “Buy Rates” to “Contract Rates” to see dealer markups

7% 0.2%

10.5% 2.3%

11.3% 0.2%

6.1% 0.1%

7.0% 0.0%

0% 0%

1.22(Black)

Additional Fair Lending Reports

Description Applications Loans

Volumes/Concentrations

Approval/Denial Rates

Disposition (of Applications)

Exception Tracking

Decisionmaker Analysis

Mod/Default Tracking

Volumes/Concentrations

Race All Application Count % of All Applications

Asian/Pacific Islander 50 5%

Black 200 20%

Hispanic 10 0%

White 500 50%

Not Reported 50 5%

-Show by: Race, Gender, and Age-A low volume of applicants/loans may be an indicator of Pre-screening, Redlining, and/or Policies with Disparate Impact

Approval/Denial Rates & Denial Disparity Ratio

Gender All Application Count

Denied ApplicationCount Denial Ratio Denial Disparity Ratio

(by Total*)

Male 400 10 2.5% 0.28

Female 200 50 25% 2.78

Joint 300 30 10% 1.1

Unknown 50 0 0% N/A

Not Reported 50 0 0% N/A

TOTAL/OVERALL 1000 90 9.0% 1.0

-Show by: Race, Gender, and Age-Significant variations in acceptance/denial ratios may be an indicator of disparate treatment

*The Denial Disparity Ratio can also be calculated by comparing to a control group (i.e. Male) instead of the total.

Disposition (of Applications)

AgeAll

Application Count

Approved-Funded

ApplicationCount

Approved-Not Funded

Application Count

Denial Application Count

Withdrawn Application

CountOther

<=20 50 20 0 30 0 0

21-30 100 55 5 40 0 0

31-40 300 290 5 5 0 0

41-50 300 200 20 70 10 0

51-60 125 100 5 12 3 5

>60 100 50 25 0 25 0

-Show by: Race, Gender, and Age-high levels of withdrawn and/or approved-not funded applications may be an indicator of disparate treatment

Exception Tracking

Description% of Asian/Pacific Islander

% of Black % of Hispanic % of White % of Other % of Not Reported

Exception Code A 2% 3% 1% 92% 0% 2%

Exception Code B 1% 4% 3% 90% 0% 2%

Exception Code C 2% 6% 3% 87% 0% 2%

Exception Code D 2% 3% 2% 91% 0% 2%

Exception Code E 2% 3% 5% 90% 0% 0%

Etc. 2% 3% 5% 88% 0% 2%

-Show by: Exception Codes, Descriptions, or Yes/No-Comparatively high concentrations in control groups in certain exception categories are an indication of potential disparate treatment

Decisonmaker Analysis



Loan Officer A 2% 13% 15% 68% 0% 2%

Loan Officer B 2.5% 12.5% 14% 70% 0% 1%

Loan Officer C 1% 13% 16% 69% 0% 1%

Loan Officer D 1% 1% 1% 95% 0% 2%

Loan Officer E 3% 12% 17% 65% 0% 3%

Etc. 2% 13% 11% 70% 0% 4%

-Show by: Loan Officer, Underwriter, Branch, Etc.-Basically any of the above reports (volumes, denial rates, steering, pricing, exception tracking, etc.) stratified by a decision maker (loan officer, underwriter, etc.)

Modifications Tracking

-Modification Rates by Race, Gender, Age, Marital Status, Etc.

-High % of Modification’s Denied are an indication of potential disparate treatment



Modification Granted 2% 1.3% 1.5% 93.2% 0% 2%Modification Denied 2.5% 94.3% 2.2% 2.5% 0% 1%

Tone at The Top

Policies & Procedures

Assessing Fair Lending

Risk

Training

PerformanceMonitoring

Correction

Fair Lending is cultural and reinforced at the highest levels and throughout the organization in both written and verbal forms.

Policies and Procedures provide clear guidance and are free of overt discrimination.

Assess and mitigate, as appropriate, heighted fair lending risk areas.

Training occurs regularly and is well documented. Trainees include top management, board members, and new employees.

Any weaknesses or violations are promptly corrected , including

appropriate restitution.

Fair lending compliance and performance is regularly

reported and monitored.

Bringing it all together…

Phone:888-409-1560

Email:[email protected]

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Documents

Fair Lending Analysis Made Easy - CUNA Councils...The lawsuit alleges that SunTrust's policies promoted racial discrimination by giving loan officers and mortgage brokers significant