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Interest income is the primary source of revenue for any retail or commercial financial institution. In maximizing the spread between the interest generated from loans offered to borrowers and the interest paid for deposits, there are few financial operations as detrimental as credit risk management. A financial institution’s competitive advantage weighs on their ability to adequately assess a borrower’s risk of becoming delinquent. Unfortunately, about 8% of credit cards balances alone are in default or delinquency 90 days after their payment due dates.
In addition to the AI use cases that can help financial institutions manage risk, a separate component of reducing charge offs is to optimize debt collection efforts. For many third-party debt collection agencies, keeping meticulous track of borrower contact information and creating solutions to restructuring debt are all effective strategies to reduce delinquencies. However, debt collectors are currently only equipped with historical information that do not help them optimize collections in the future.
By learning from vast amounts of historical data on the characteristics of borrowers where debt collection efforts were successful and unsuccessful, AI enables your debt collectors to predict in advance which borrowers are likely to pay back before even reaching out. This allows you to intelligently maximize your efforts by triaging your outreach based on the size of delinquency and likelihood of success. In addition to providing the likelihood of payment, AI will also inform your debt collectors on the top statistical reasons as to why each individual borrower is likely to respond positively or negatively. With this information, debt collectors can personalize their approach to understanding the needs and circumstances of every borrower. Alongside reducing delinquencies, this helps your debt collectors maintain personalized and constructive lines of communication with the borrowers they are looking to assist.
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