Portals & Rails
September 24, 2013
Using Analytics to Improve Credit Quality
With consumer credit products such as mortgages and payday loans occupying headlines, credit card portfolios have been quietly and steadily marching towards improvement in quality over the last three years, according to data released by the Fed’s Board of Governors. As the chart shows, seasonally adjusted charge-off rates are down to 3.9 percent, and delinquency rates are at 2.6 percent for the largest 100 commercial banks in the United States, the lowest rate since the Federal Reserve began tracking this statistic at the start of 1991.
But how have credit card issuers been able to improve the quality and profitability of their card portfolio since the severe economic impact felt by all during the recession? One of the many tools the Board identified—and one cited by portfolio managers—is the increasing use of analytics. Issuers collect and comb vast amounts of data from a variety of sources to ensure that cardholders are equipped to manage their balances.
A brief note about charge-offs: The charge-off rate is the percentage of credit card balances written off and charged against loss reserves, annualized and net of recoveries. Delinquent credit card debt comprises past due balances, 30 days or more, and still accruing interest in addition to balances in nonaccrual status.
Credit issuers use analytics for a variety of purposes, including establishing credit limits, monitoring ongoing credit quality, targeting marketing efforts, and detecting fraud. They perform analytics at the individual cardholder level—looking at credit history and purchasing patterns, for example—as well as at the customer segmentation level to identify correlations between certain data elements and indicators of potential changes in credit quality. The increased power of these analytical tools over the last decade is due primarily to the incredible advancements in data collection and analysis technology. These advances have provided issuers with the ability to run sophisticated "what if" models to determine how changes in various key attributes of cardholders or in the overall economic environment will affect the quality of their portfolio.
Clearly, many of the issuers have taken other proven steps to improve the credit quality of their portfolios: they’ve reduced credit lines and increased payment monitoring management for existing accounts during and after the recession. And they applied more stringent credit policies, making it more difficult for new applicants to be approved (or likelier to be approved at lower credit limits than they would have been before). These are all sound risk management techniques. But data analytics has been a very powerful additional tool, allowing issuers to make huge strides in ensuring ongoing credit quality.
How are you using increased technology capabilities to improve your risk management capabilities?
By David Lott, a retail payments risk expert in the Retail Payments Risk Forum at the Atlanta Fed
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