Compliance and Data Analytics: The Risks and Implications of Fair Lending

From internet advertisement to target marketing, from magazines to online, whether in print or digital, data are everywhere. Technology has made it increasingly easy for consumers and businesses to consume and produce data. Financial institutions are no different. Using data to market credit to your audience is now as easy as a click of a mouse. Employing the use of compliance and data analytics is beneficial for the consumers, but it can be costly if improperly managed. To protect the consumers and to ensure a strong system of fair lending, the Consumer Financial Protection Bureau (CFPB) expects financial institutions to monitor their fair lending compliance with robust data governance regarding the collection and use of personal data.

The Basics of Fair Lending

The Equal Credit Opportunity Act (ECOA), and its implementing regulation, Regulation B, prohibits creditors from discrimination in consumer and commercial credit transactions. ECOA prohibits discrimination based on race, color, national origin, religion, sex, marital status, age, receipt of income from public assistance, or exercise of rights under the Consumer Credit Protection Act (CCPA). The Fair Housing Act (FHAct) makes it unlawful for lenders to discriminate in housing-related lending activities based on race, color, national origin, religion, sex, familial status, or handicap or disability. ECOA and FHAct prohibit two kinds of discrimination: disparate treatment and disparate impact. Disparate treatment happens when a creditor treats a consumer differently based on a prohibited basis. Disparate impact happens when a creditor’s policy or practice—albeit applies equally—has a disproportionate adverse impact on a prohibited basis. 

Risks and Implications of Fair Lending

When banks market their products, many rely on data for advertisement, marketing, and promotions. These activities need to comply with fair lending laws as not to exclude consumers of a protected characteristic or to discourage them from applying for credit. For community banks, the collection, mining, and use of data present a growing challenge in how to navigate fair lending water prudently. Statistical data analysis may be prone to produce more problems than solutions. The business model of uncovering fair lending issues is through compliance and data analysis. Comparative analysis of loan data has proven to be successful and beneficial for banks. The adoption of algorithmic techniques has been a boon to banks, but a challenge because it could reveal latent risks and implications. Data are supposed to be impartial and unbiased, but data can also be manipulated and distorted.

What Banks Should Do

Banks are in a precarious position concerning fair ending. In fair lending matters, often, there is a judicious explanation for everything a bank does. However, that may not be evident in the data. To mitigate compliance risks, ­data analysis is what banks should do to find and resolve fair lending issues. Data analysis is where banks can find hidden risks when they are not apparent in policies, procedures, or processes. Data analysis is what banks should also do to scope and investigate improper practices of fair lending. Analyze to see what the data cover and uncover. These processes can lead to discovery that can eliminate errors and headaches. The risks and implications of fair lending are ever-present, and banks must take compliance and data analytic seriously to mitigate, remediate, or eliminate them. In theory, it may sound simple; in execution, it is everything. Because the risks of fair lending violations—whether intentional or otherwise—can be damaging and costly.