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
Race/Color
Gender
Age
Handicap
Marital Status
Familial Status
Religion
 FH Act
 ECOA
Source of Income
National Origin
 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
Discourage or selectively encourage individuals who inquire or apply for credit
3
Refuse to extend credit or use different standards in determining whether to extend credit
4
Vary the terms of credit offered
5
Use different standards to evaluate collateral
6
Treat a borrower differently in servicing a loan or making invoking default remedies
7
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
Application Activities
-Level of Assistance
-Use of 3rd Parties
-Initial Terms & Conditions
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 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
How and where you offer
credit products are not just
fundamental business
decisions, they have fair
lending implications as
well.
Discretion
Permitted
The level of individual
discretion permitted in
certain activities such as
approvals or pricing
increases an institution’s
fair lending risk.
Exception
Handling
Exceptions should be made
consistently for similar
reasons for similarly situated
applicants or borrowers.
Use of
Third Parties
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.
Marketing Activities
Figure 1. Home Owner’s Loan Corporation, Philadelphia, PA., 1936
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.
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
Market Demographics:
Stable, Low Competition, Low Diversity
Delivery Channels:
Limited, Processes don’t vary, No Subprime Subsidiaries/Affiliates
Product Complexity:
Traditional Mix
New Products Reviewed for Fair Lending Compliance
Advertising:
Limited, No Recent Changes, Broad Based
3
4
5 (High)
Evolving, High Competition, High Diversity
Multiple, Processes vary, Subprime
Subsidiaries/Affiliates
Complex and Non-traditional Mix
New Products Not Reviewed
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
Limited and Consistent Discretion
Discretion Criteria is Clear
Discretion Does Not Impact Compensation
3
4
5 (High)
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
Exceptions are Objective and Well Documented
Exceptions are Few in Number
3
4
5 (High)
Exceptions are Subjective and Not Well Documented
Exceptions are Many
Score:
Use of
Third
Parties
Consumer
1. Consumer submits application to Dealer
Lender
Dealer
2. Dealer submits application
to Lender(s)
6. Dealer sales the contract
to the chosen Lender
Lender(s)
Dealer
5. Consumer and Dealer
close the sale
Consumer
Q: Where is a major source of
Fair Lending Risk in this process?
Dealer
Lender(s)
Lender(s)
3. Lender(s) submit “buy rate”
and dealer compensation to
dealer
4. Dealer sets actual rate to consumer
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
Little or No Use of Third Parties
Due Diligence Performed
Written Agreements/Accountability Defined
Receive Regular Reporting
Regular Fair Lending Training
3
4
5 (High)
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
Incentives & Compensation Not Tied to Loan
Production
Incentives & Compensation Not Tied to Loan Pricing
and/or Fees
3
4
5 (High)
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)
Risk Level (Based on Total Score):
TOTAL SCORE (5-25)
Low
Medium-Low
Medium
Medium-High
High
RISK LEVEL (Low to 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
Self Test
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
Applicant/Borrower data such credit score, DTI, PTI, length of employment, length at
residence, etc. Also, race, gender, and age of borrower as appropriate.
1
2
3
Collateral information such as type and original value.
4
5
Application/Loan data such as channel used, decision makers involved , status, rate, term,
amounts, etc.
Demographics of the Census Tract where each applicant/borrower resides.
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
2.14
Steering Ratio
1.93
(Hispanic)
(HELOCSubprime)
% of PBG in Lending Area /
% of PBG in Portfolio
% of PBG in Product /
% of Portfolio
*PBG is a Prohibited Basis Group such as Race, Gender, Age, Etc.
Expected Denial
Ratio
Expected Pricing
Ratio
2.78
1.22
(Female)
% of PBG Denied /
Expected % of Denied
(Black)
Avg. Rate of PBG /
Expected Rate of PBG
Redlining Ratio
2.14
(Hispanic)
% of PBG in Lending Area /
% of PBG in Portfolio
Percent of
Applications
(or Loans)
Percent of Lending
Area
Redlining Ratio
Asian/Pacific
Islander
3.0%
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
*PBG is a Prohibited Basis Group such as Race, Gender, Age, Etc.
Steering Ratio
1.93
(HELOCSubprime)
% of PBG in Product /
% of PBG in Portfolio
First Mortgage
Second Mortgage
HELOC (Prime)
HELOC (Sub Prime)
Other
Total
Asian/Pacific
Islander
2.0%
1.2%
1.4%
1.6%
1.7%
2.0%
First Mortgage
Second Mortgage
HELOC (Prime)
HELOC (Sub Prime)
Other
Asian/Pacific
Islander
1.00
0.59
0.69
0.78
0.83
*PBG is a Prohibited Basis Group such as Race, Gender, Age, Etc.
Black
7.5%
3.4%
4.5%
14.2%
0.5%
7.4%
Native
Hispanic American
8.0%
N/A
12.2%
N/A
5.5%
N/A
10.6%
N/A
2.5%
N/A
7.9%
N/A
White
80.5%
82.2%
87.1%
72.4%
93.6%
81.3%
Other
2.0%
1.0%
1.5%
1.3%
1.7%
1.8%
Black
1.02
0.46
0.61
1.93
0.07
Native
Hispanic American
1.01
N/A
1.54
N/A
0.69
N/A
1.34
N/A
0.32
N/A
White
0.99
1.01
1.07
0.89
1.15
Other
1.10
0.55
0.83
0.69
0.94
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)
% of PBG Denied /
Expected % PBG Denied
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
1000
90
9.0%
9.0%
1.0
Gender
TOTAL/OVERALL
*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
Average Expected
Rate
Difference
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
0%
0%
1.00
Race/Ethnicity
Asian/Pacific Islander
1.22
(Black)
Avg. Rate of PBG /
Expected Rate of PBG
Not Reported
Average Rate
0%
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
Additional Fair Lending Reports
Description
Volumes/Concentrations
Approval/Denial Rates
Disposition (of Applications)
Exception Tracking
Decisionmaker Analysis
Mod/Default Tracking
Applications
Loans
Volumes/Concentrations
Race
Asian/Pacific Islander
Black
Hispanic
White
Not Reported
All Application
Count
% of All Applications
50
5%
200
20%
10
0%
500
50%
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
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
1000
90
9.0%
1.0
Gender
TOTAL/OVERALL
-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
ApprovedFunded
Application
Count
Approved-Not
Funded
Application Count
Denial Application
Count
Withdrawn
Application
Count
Other
<=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
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
Modification Granted
Modification
Denied
% of
Asian/Pacific
Islander
% of White
% of Other
% of Not
Reported
% of Black
% of Hispanic
2%
1.3%
1.5%
93.2%
0%
2%
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.
Policies &
Procedures
Correction
Policies and Procedures provide
clear guidance and are free of
overt discrimination.
Fair lending compliance and
performance is regularly
reported and monitored.
Assessing
Fair Lending
Risk
Performance
Monitoring
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.
THANK YOU!
Phone:
888-409-1560
https://www.linkedin.com/company/visible-equity
facebook.com/visibleequity
Email:
info@visibleequity.com
twitter.com/visibleequity