Non-Mortgage Products

Transcription

1 Non-Mortgage Products Hot Issues in Non-Mortgage Lending Melanie Brody Partner Mayer Brown Brian Clark Senior Manager Ernst & Young

2 Speakers Melanie Brody Partner Mayer Brown Brian Clark Senior Manager Ernst & Young 2

3 Overview Fair lending risks across the product lifecycle Non-HMDA race coding Indirect auto Credit cards Small business Student lending 3

4 Fair Lending Risks Across the Product Lifecycle New Product Design and Approval Target markets Product terms High cost features Marketing Advertising Targeted populations Products targeted to vulnerable consumers Customer Interaction Disclosures Steering Lack of fairness training Originations Level of discretion Lack of disclosures Compensation structures Product Closeout Servicing New Product Development Fair Lending Ongoing Monitoring Marketing Customer Interaction Originations All of these risks are relevant for non-mortgage products too Ongoing Monitoring Lack of periodic product monitoring Product complaints Servicing Inconsistency in product account servicing procedures Use of third third-party contractors Organic or acquired origin: Acquired third parties pose problems with assimilation Product closeout Ease of consumer ability to closeout product 4

5 Non-HMDA Race Coding Lenders may only collect information on race, ethnicity, and gender for applications for home purchase loans, home improvement loans, and refinances pursuant to HMDA. Therefore, when conducting fair lending analysis on non-hmda data, the applicant s probability of being identified as each customer group is calculated and used as a proxy. Bayesian Improved Surname Geocoding (BISG), which is the methodology used by the CFPB, uses a combination of the surname and census block group to achieve a conditional probability of the applicant belonging to a particular race or ethnicity class. Person A Probability that Person A belongs to each race or ethnicity class 10% Hispanic 10% Asian/Pacific Islander 15% American Indian 40% White Non-Hispanic *The gender proxies are determined using the first name of the applicant. 25% Black or African American *Note: Multiracial is another BISG classification not depicted here 5

6 Indirect Auto Larger participants rule: In June 2015, the CFPB published a rule defining larger participants in the auto finance market, giving itself supervisory authority over non-bank auto finance companies that make, acquire or refinance 10,000 or more loans or leases in a year. Auto finance exam procedures: The CFPB simultaneously updated its exam manual to include guidance on bank and non-bank auto finance supervision. The auto finance manual includes compliance management systems, marketing and disclosures, credit bureau reporting, and debt collection. 6

7 Indirect Auto Dealer participation CFPB and DOJ continue to incentivize limitations on dealer participation. Buy rates Recent settlements require auto finance companies to (i) eliminate dealer discretion, (ii) cap dealer discretion and monitor for compliance, or (iii) cap dealer discretion, establish a standard dealer participation rate, and allow downward exceptions under specified conditions. CFPB inquiries regarding flat rate. The CFPB has indicated that its auto finance targeted ECOA reviews include an examination of buy rates. October 2015 ECOA Baseline Review Modules include module on credit models. 7

8 Indirect Auto Key recommendations Consider evaluating adequacy of compliance management system Consider evaluating controls on dealer participation Consider evaluating buy rate models Consider evaluating sales and marketing, particularly regarding add-on products Consider evaluating debt collection and credit reporting practices 8

9 Indirect Auto Focus on indirect auto Indirect Auto focus areas include: underwriting decisions, buy rate determination, allowance and amount of dealer markup, compensation structure for dealers in the bank s network, program for addressing findings and determination of remediation (if unexplained disparities arise). The Ally settlement in 2013 sparked an emphasis on indirect auto fair lending practices. As part of the consent order, Ally agreed to monitor at the dealer level at least quarterly, and at the portfolio level quarterly and annually. Subsequent settlements with Honda, Toyota, and Fifth Third have seen suggestions to continue dealer participation with monitoring, set fixed levels of participation and monitor any exceptions, or set a flat fee. Two types of monitoring to perform 1 2 Dealer level Portfolio level Identify dealers with statistically significant differences in markup across borrower groups. Institutions often do business with a large number of dealers, and a given dealer may submit only a handful of loans to a given lender in an analysis period. For meaningful statistics, set minimum number of deals per dealer. Consider using overlapping analysis periods (e.g., quarterly analysis using the past six months). Repeated slicing of data by dealer can result in false positives using the conventional statistical significance level of 95% implies that we accept the risk of drawing the wrong conclusion in 5 out of 100 tests. Consider repeated pattern over time rather than a single instance. High-pricers, i.e., dealers who consistently charge high markups from all customers and serve a predominantly minority clientele, may present portfolio-level risk. Consider identifying these dealers along with those with pricing differences. 9

10 Indirect Auto Challenges Statistical analyses The lack of defined explanatory factors for dealer markup makes modeling differences difficult. Other potentially discriminatory practices Analyses of final outcome may not provide insights into other potentially discriminatory practices, for example, add-on products (e.g., adding a sunroof or adding a vehicle warranty), bundling packages, providing price reductions differentially, etc. However, it is unclear whether each of these other potentially discriminatory practices would be considered under ECOA. Race data Race data cannot be recorded, therefore the statistical analyses rely on the use of proxies. Negotiation The applicant s ability to negotiate plays a fairly significant role in the final outcomes. 10

11 Credit Cards Add-on products CFPB has brought 11 credit card enforcement actions related to marketing and administration of add-on products and services. Recent examples: July 2015 $700 Million CFPB Consent Order: National bank and its subsidiaries allegedly engaged in deceptive marketing, billing, and administration of debt protection and credit monitoring add-on products. Subsidiary allegedly deceptively charged expedited payment fees during collection calls. September 2015 $3 Million Consent Order: Regional bank allegedly deceptively marketed debt protection products. 11

12 Credit Cards Add-on products OCC also brought enforcement actions related to add-on products. Recent examples: November 2015 Consent Order Bank and its vendors allegedly unfairly billed consumers for identity protection products even though they did not receive all the benefits. Bank deceptively failed to credit fees when consumers cancelled products within review period. April 2016 Consent Order Banks vendors unfairly billed and retained fees for a credit monitoring product even though some customers did not receive it. 12

13 Credit Cards Fair lending supervision: CFPB has indicated that credit cards continue to be a priority market for fair lending supervision and enforcement. We have focused in particular on the quality of fair lending compliance management systems and on fair lending risks in underwriting, line assignment and servicing. CFPB Fair Lending Report (April 2016) Pricing? Servicing issues? 13

14 Credit Cards Key recommendations: Consider implementing a credit card fair lending compliance management system. Consider evaluating marketing and administration of add-on products for UDAAP risk. Consider performing fair lending monitoring on credit card underwriting, and line assignments/adjustments. If pricing is 100% automated, consider evaluating pricing model variables vs. testing for disparities. Use caution when considering differentiating between Englishspeaking and Spanish-speaking customers. 14

15 Credit Cards Prospecting/ marketing Underwriting Pricing Account management Lifecycle component Invitations to apply. Pre-approvals/firm offers. POS applications. Approval/denial. Initial line assignment. Line increase requests (reactive). Product allocation. Introductory rates. Standard rates. Line increases/decreases (proactive). Account closures (proactive). Late fees/fee waivers. 15 Fair lending risks Offers typically generated based on statistical models or screens, less on judgment. Factors considered in the process can result in disparate impact, or increase the risk of redlining. Judgment used at POS regarding which card product to offer may result in concerns about disparate treatment. Discretionary component of decisioning (e.g., overrides, exceptions) may result in disparate treatment. Automated underwriting can result in disparate impact, or increase the risk of redlining. Possibility for steering risk. Similar risks to those in underwriting. Statistical models/screens may be used to review portfolios for riskier accounts to reduce lines or to increase lines for good accounts, and the factors considered in the models/screens may cause disparate impact. Discretion may play a role, raising the potential for disparate treatment. A risk-based prioritization can be used to determine areas of focus for fair lending testing. Analyses may incorporate testing for disparate impact and/or disparate treatment.

16 Small Business Treasury Department s May 2016 Online Marketplace Lending Whitepaper emphasized need for more regulation of small business lending by all types of institutions. American small businesses created 2/3 net new jobs over the past two decades. Small businesses borrowers may only receive protection under contract law or ECOA. CFPB has declared that small business lending is a fair-lending priority....many small businesses are sole proprietorships where the owner s personal credit... may be on the line. CFPB Fair Lending Report (April 2016) Approximately 72% of all small businesses are sole proprietorships. Small business lending market is over $1 trillion dollars and serves over 28 million businesses....research suggests that significant discrimination against minorities may exist in the small business lending market. CFPB Policy Priorities (February 2016) 16

17 Small Business CFPB has begun targeted ECOA reviews of small business lending, focusing on the quality of fair lending compliance management systems, underwriting, pricing, and redlining. CFPB Fair Lending Report (April 2016) Because the CFPB does not have supervisory authority over small businesses, presumably these reviews are of banks. If feasible, the CFPB will build the infrastructure to intake and analyze small business lending complaints. CFPB Policy Priorities (February 2016) The CFPB hired an Assistant Director for the Office of Small Business Lending Markets. The Assistant Director will lead the CFPB s effort to issue the rule implementing the Dodd-Frank Act s Section 1071 for small business lending data collection requirements. 17

18 Small Business Key recommendations Consider establishing a small business lending fair lending compliance management systems (especially banks subject to CFPB supervisory authority). Consider testing for disparities in small business lending underwriting, pricing, and market penetration. Consider developing a small business lending complaint tracking and response process. 18

19 Small Business Credit Cards, Lines, and Loans What is a small business loan? Automated vs. judgmental Relationship decision vs. clear guidelines Data recording Redlining Currently, the CFPB has not released a concrete definition of what constitutes a small business. The definition of a small business is therefore defined individually, and potentially differently, by each institution. What is the right distinction between small business loans and commercial loans? The decisioning process for a small business dictates the type of fair-lending risks the institution is exposed to; Fair lending testing focuses on the disparate impact for automated decisions and disparate treatment for judgmental decisions. The ability to replicate the decision is contingent on clear bank policy/procedures/ guidelines. The lack of clear guidelines or decisions based on relationships, makes fair-lending modeling difficult. The bank s accurate recoding of both accepted loan and declined loan characteristics play a key role in developing fair lending testing. Redlining is a risk that has been talked about in the small business context. Redlining is challenging for small business loans because not every person is a business owner somehow the amount of small businesses would need to be considered. 19

20 Student Lending The CFPB has supervisory authority over private student lenders and larger participants in the student loan servicing market. CFPB student loan examinations have primarily focused on whether entities have engaged in UDAAPs. Issues include: Unfair payment allocation among multiple student loans in a borrower s account Unfair payment processing practices Unfair auto-default processes 20

21 Student Lending Unfair forbearance practices Unfair practices during loan conversion process Information furnishing to CRAs Deceptive statements regarding discharging loans in bankruptcy Deceptive misrepresentations about late fees September 2015 CFPB Student Loan Servicing Report CFPB, Department of Education, and Department of Treasury issued: Joint Statement of Principles on Student Loan Servicing 21

22 Student Lending February 2016 CFPB policy priorities: The Bureau sees consistent signs of consumer harm from student loan servicing examinations, investigations, and consumer complaint data. Servicers lack sufficient incentives to change harmful practices. The Bureau will hold servicers accountable through supervisory and enforcement activity in coordination with its law enforcement partners. Key recommendations: Consider comprehensive review of servicing practices for UDAAP risks. 22

23 Student Loans Deferment Fair lending risks may arise anywhere that a decision point is made. One such instance is the decision of deferment schedules. There are three generally accepted types of deferment: Interest only Fixed pay $0 After entering payment, a borrower can re-enter deferment if they choose to go back to school. Analytics considerations Most applicants are co-signed with a parent, therefore when conducting analyses it is important to capture the credit characteristics of both the applicant and the coapplicant. Students may use his or her school address on the loan application, decreasing the accuracy of racial and ethnic proxies. Scorecard factors used in underwriting Unique factors used to underwrite student loans may have a disproportionate adverse impact on applicants in a protected group (e.g., school default rate). 23

24 APPENDIX

25 Non-HMDA Race Coding Continuous Probabilities Every individual in the analysis population will be assigned a probability of belonging to each demographic group based on their surname and geographical location following the BISG method. Race/ethnicity probabilities using BISG (illustrative examples) Population Counts With each individual applicant s probabilities calculated, the full population s demographic distribution can be expressed as the sum of these probabilities. 25

26 QUESTIONS

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