WesBanco

Aaron Rykowski is a seasoned banking regulatory risk management leader with over 20 years of experience in the banking industry, currently serving as the Executive Vice President and Chief Compliance Officer at WesBanco. His expertise lies in consumer compliance, having held various roles including Chief Compliance Officer at community and regional banks and overseeing compliance for servicing consumer practices at a large super-regional bank. Through this article, Rykowski highlights the importance of using data analytics to proactively identify and mitigate compliance risks in organisations.

In today’s ever-changing regulatory environment, when our world can be turned upside down by something as simple as a blog post, we must use any information we can to identify risks in our organizations. The trouble with compliance is that we may not know we have a problem until it is uncovered by one of the many testing programs in place across all three lines of defense. That is, of course, why we have them. The programs exist to identify deficiencies in our controls that could adversely impact consumers or be detrimental to the company’s reputation and bottom line. The problem with this is that once these programs identify a breakdown in controls, depending on the testing frequency, the problem could have been festering under the surface for months, creating situations where we must tell our customers, “Oops, we messed up.”

But what if we could take existing data that we look at every day and use it to predict where we have the most risk and be able to help laser-focus our testing programs? We have a wealth of information at our fingertips that we, as management, rely on to inform our decisions and business plans. Sales goal tracking, loan delinquency numbers, call centre metrics, production volume, and product enrolment are all gold mines of information we can leverage to predict where we may have compliance risk. Let’s look at some of these numbers and how we can use them to predict where we could have higher risk in the future.

New Product Risk: Every company that relies on a pipeline of new customers to remain viable and vibrant uses sales metrics to determine where they are relative to their goals. The mix of these new relationships guides our testing assessments to get out in front of potential issues. Is the rate environment driving customer demand for adjustable-rate loans? We increase testing of that product to ensure proper caps and disclosure timing. Outliers in production activity influence where we put resources as well. The absence of sales or extremely high numbers are red flags that can indicate UDAAP or redlining risk, which warrants testing to ensure products are being offered fairly and in a clear and concise manner.

 An increase in the standard consumer real estate delinquency rate would indicate that more customers may require assistance, triggering additional loans that fall into the complicated world of default servicing. 

Existing Customer Risk: It’s easy to look at complaints to determine where current clients have issues or questions about their relationship with us. But usually, that only guides us to where an issue has already arisen. While that would need to be remediated, how can we get ahead of where we may have risks with our existing customers? Loan delinquency can be a strong indicator of the health of our loan portfolio and a leading compliance risk indicator. Mortgage servicing is complicated, with numerous regulations and investor requirements intertwined, particularly once loans are in default. An increase in the standard consumer real estate delinquency rate would indicate that more customers may require assistance, triggering additional loans that fall into the complicated world of default servicing. This would require more resources both in the business and in the testing space to ensure compliance with extremely strict and high-risk regulatory processes. Another number that is reviewed for comparison to the budget is fee revenue. We look at this daily, weekly, or monthly, depending on its materiality. Fee types with higher or lower than expected numbers may lead us to uncover issues with how a fee is assessed, systemic breakdowns with charges, or issues with waiver practices. Investigating these differences and determining root causes helps us identify potential issues faster, minimising the impact on the consumer and protecting both them and the bank from long-term problems.

These are only a few areas where data can guide us to areas of risk without waiting for a test to find it months down the road. In each case, the data and metrics were not built FOR compliance to determine risk but were used BY compliance to inform and guide the use of resources. Using existing data to identify risk more quickly is a simple way to strengthen our compliance and risk programs, solidify our relationship with our customers, and protect our company and reputation. My challenge is—what else can you find under your nose?