Fair Lending Analysis Made Easy Presented by: Ian Dunn CEO,
Agenda 1 The Basics 2 Assessing Your Potential Fair Lending Risk Exposure 3 Key Fair Lending Analysis & Reporting
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 Handicap Familial Status Race/Color Gender Age Marital Status Source of Income Religion National Origin FH Act ECOA Both
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.
The Basics- Prohibited Practices 1 Fail to provide or provide different information or services regarding any aspect of the lending process 2 3 4 5 6 7 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 Applies to: Applicants Associations Occupants Neighborhood
The Basics- Fair Lending touches every part of Lending Process Pre-Application Activities -Advertising & Market Selection -Channels -Responding to Inquiries Underwriting/Closing -Approval Criteria -Final Terms & Conditions -Appraisal Practices Application Activities -Level of Assistance -Use of 3 rd Parties -Initial Terms & Conditions 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 debtto-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 Borrower National Origin Number of Home Purchase Loans Average Interest Rate Hispanic 125 12.5% Non Hispanic White 150 8.5% Discrimination? -Potentially Prohibited Basis: -National Origin Discrimination Type: -Disparate Treatment: Comparative Evidence Prohibited Practice: -Vary the terms of credit offered
The Basics-Example 3 The 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 4 Discrimination? -Potentially Prohibited Basis: -Race, Familial or Marital Status Discrimination Type: -Disparate Impact Prohibited Practice: -Fail to provide services, discourage applications Notes: -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 1 The Basics 2 Assessing Your Potential Fair Lending Risk Exposure 3 Key Fair Lending Analysis & Reporting
Assessing Your Potential Fair Lending Risk Exposure Marketing Activities Discretion Permitted Exception Handling Use of Third Parties Incentives & Compensation 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. Compensation tied to loan production can be a great motivator for a tough job, but it can also lead to significant fair lending issues.
Marketing Activities 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. Figure 1. Home Owner s Loan Corporation, Philadelphia, PA., 1936 Reverse Redlining Reverse redlining is the deliberate targeting of residents of such neighborhoods with less advantageous or potentially predatory products. 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.
Marketing Activities 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.)
Marketing Activities 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.
Marketing Activities (Score 1 to 5) 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 Product Complexity: Traditional Mix New Products Reviewed for Fair Lending Compliance Multiple, Processes vary, Subprime Subsidiaries/Affiliates Complex and Non-traditional Mix New Products Not Reviewed Advertising: Limited, No Recent Changes, Broad Based Extensive, Constantly Changing, Targeted Score:
Discretion Permitted SunTrust Mortgage Inc. has agreed to pay $21 million Settlement 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.
Discretion Permitted (Score from 1 to 5) 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 Discretion Criteria is Clear Discretion Does Not Impact Compensation Broad and Variable Discretion Discretion Criteria is Broad or Non-existent Discretion Does Impact Compensation Score:
Exception Handling
Exception Handling (Score from 1 to 5) 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 Few in Number Exceptions are Subjective and Not Well Documented Exceptions are Many Score:
Use of Third Parties Lender Consumer 1. Consumer submits application to Dealer Dealer 6. Dealer sales the contract to the chosen Lender 2. Dealer submits application to Lender(s) Dealer Lender(s) Lender(s) Lender(s) 5. Consumer and Dealer close the sale Q: Where is a major source of Fair Lending Risk in this process? Consumer Dealer 4. Dealer sets actual rate to consumer 3. Lender(s) submit buy rate and dealer compensation to dealer
Use of Third Parties (Score from 1 to 5) 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 Due Diligence Performed Written Agreements/Accountability Defined Receive Regular Reporting Regular Fair Lending Training Extensive Use of Third Parties No Due Diligence Performed No Written Agreements/Accountability Not Defined Receive No Reporting No Fair Lending Training Score:
Incentives & Compensation 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 https://www.youtube.com/watch?v=t-w_iljqt4a
Incentives & Compensation (Score from 1 to 5) 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 Not Tied to Loan Pricing and/or Fees Incentives & Compensation Tied to Loan Production Incentives & Compensation Tied to Loan Pricing and/or Fees Score:
Assessing Your Potential Fair Lending Risk Exposure What is your total score and risk level? 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) Risk Level (Based on Total Score): Low Medium-Low Medium Medium-High High <=7 8-12 13-16 17-21 >=22
Can loan officers or other decision makers set rates and fees? Discretion Permitted Incentives & Compensation Is compensation tied to higher priced loans? Discrepancies by Prohibited Basis Groups Does statistical analysis show higher rates/fees being charged to minorities or other prohibited basis groups? = HIGH RISK!!!
Agenda 1 The Basics 2 Assessing Your Potential Fair Lending Risk Exposure 3 Key Fair Lending Analysis & Reporting
Self Evaluation vs. Self Test A self evaluation or a self test may provide for a streamlined exam. Self Evaluation 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. Self Test 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 1 Applicant/Borrower data such credit score, DTI, PTI, length of employment, length at residence, etc. Also, race, gender, and age of borrower as appropriate. 2 Application/Loan data such as channel used, decision makers involved, status, rate, term, amounts, etc. 3 Collateral information such as type and original value. 4 Demographics of the Census Tract where each applicant/borrower resides. 5 Demographics of your Institution s Lending Area.
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)
Fair Lending Advanced Building Blocks 2 Expected Gender Estimating Expected Gender: -Match Borrower First Name to Gender Name Database
An Example
The 4 Key Fair Lending Ratios & Reports Redlining Ratio Steering Ratio Expected Denial Ratio Expected Pricing Ratio 2.14 (Hispanic) 1.93 (HELOC- Subprime) 2.78 (Female) 1.22 (Black) % 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 *PBG is a Prohibited Basis Group such as Race, Gender, Age, Etc.
Redlining Ratio Asian/Pacific Islander Percent of Applications (or Loans) Percent of Lending Area Redlining Ratio 3.0% 2.5% 0.83 2.14 (Hispanic) % of PBG in Lending Area / % of PBG in Portfolio 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 *PBG is a Prohibited Basis Group such as Race, Gender, Age, Etc.
Steering Ratio 1.93 (HELOC- Subprime) % of PBG in Product / % of PBG in Portfolio 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 1.10 Second 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 0.83 HELOC (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 *PBG is a Prohibited Basis Group such as Race, Gender, Age, Etc.
Fair Lending Advanced Building Blocks 3 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
Expected Denial Ratio 2.78 (Female) Gender All Application Count Denied Application Count % Denied Expected % Denied Expected 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 % of PBG Denied / Expected % PBG Denied TOTAL/OVERALL 1000 90 9.0% 9.0% 1.0 *PBG is a Prohibited Basis Group such as Race, Gender, Age, Etc.
Fair Lending Advanced Building Blocks 4 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
Expected Pricing Ratio 1.22 (Black) Avg. Rate of PBG / Expected Rate of PBG Race/Ethnicity Average Rate Average Expected Rate Difference Expected Pricing Ratio Asian/Pacific Islander 6.8% 7% 0.2% 0.97 Black 12.8% 10.5% 2.3% 1.22 Hispanic 11.5% 11.3% 0.2% 1.02 White 6.0% 6.1% 0.1% 1.00 Other 7.0% 7.0% 0.0% 1.00 Not Reported 0% 0% 0% 1.00 *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
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 Application Count 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) Age All Application Count Approved- Funded Application Count Approved-Not Funded Application Count Denial Application Count Withdrawn Application Count <=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 Other
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 Description % of Asian/Pacific Islander % of Black % of Hispanic % of White % of Other % of Not Reported 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 Description % of Asian/Pacific Islander % of Black % of Hispanic % of White % of Other % of Not Reported Modification Granted 2% 1.3% 1.5% 93.2% 0% 2% Modification Denied 2.5% 94.3% 2.2% 2.5% 0% 1% -Modification Rates by Race, Gender, Age, Marital Status, Etc. -High % of Modification s Denied are an indication of potential disparate treatment
Bringing it all together Any weaknesses or violations are promptly corrected, including appropriate restitution. Tone at The Top Fair Lending is cultural and reinforced at the highest levels and throughout the organization in both written and verbal forms. Correction Policies & Procedures Fair lending compliance and performance is regularly reported and monitored. Policies and Procedures provide clear guidance and are free of overt discrimination. Performance Monitoring Assessing Fair Lending Risk Training occurs regularly and is well documented. Trainees include top management, board members, and new employees. Training Assess and mitigate, as appropriate, heighted fair lending risk areas.
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