A large U.S-based issuer of branded credit cards incurred nearly $1 billion in consumer credit debt every year. Employing thousands of agents to recover debt from consumers in default increased the company’s collections costs to more than $30 million annually. It also led to an agent turnover rate of 40%.
The company needed a more sophisticated predictive technology to improve debt collections, and turned to Cognizant's banking technology expertise for help improving its debt collection strategies and maximizing its debt collection revenue.
We created an automated process to formulate comma-separated values files as usable information. Using a “white-box” artificial intelligence engine, we helped our client fully understand the behavior of consumers who default on their credit card debt and the likelihood of collecting on those debts.
Adopting a hypothesis as an outcome, our Artificial intelligence based causality engine (derived from information theory) can determine variables that are most relevant to the given outcome.
We applied our causality engine to large volumes of monthly data on accounts already in default to identify which of the company’s collections strategies (for example, method of communication and time of day) would best determine the likelihood of repayment. The engine also helped identify a third category of debtor—consumers who will generate revenue if encouraged to pay down their outstanding debt.
Cognizant designed a model to review voluminous data on both slow-and non-paying credit customers. Using our AI-based causality engine helped our client identify factors that determine consumer payment behavior. It showed the company that focusing its collections agents’ activities on the subgroup of consumers who are more likely to repay their debts would increase revenue $5 to $7 million while also saving a forecasted $10 million annually. Furthermore, these higher return collections are expected to increase employee commission compensation, which should lead to significant decreases in agent turnover rates, hiring expenses and training costs.
forecasted call-center savings
to review voluminous data on slow- and no-paying credit customers
factors determinative of consumers’ payment behavior
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