Debt levels are rising and borrowers are increasingly unable to pay off the debts. The Covid-19 pandemic has not helped and there is a high risk of delinquency when it comes to types of credit, from business loans to mortgages. Traditional strategies are no longer enough to collect debts and improve receivables.

During the last decade or so, AI and machine learning are disrupting debt collection. Companies are using advanced analytics, machine learning and behavioral science to fully automate their debt collection strategies. According to stats, the share of AI in FinTech alone is expected to reach about $35.4 billion in value by 2025.

Historical debt collection

Historically, debt collection has been reactive. Lenders try to recoup their losses after a borrower becomes delinquent. The risk models that are in use now don’t allow for early delinquency warnings as they are based on a limited set of data. They do not rely on numerical logic to develop a solution.

According to essay experts for an online assignment help, one of the main blockers to improving the efficiency of collections is using obsolete processes. Methods used for the collection are often intrusive and create a negative impact. Even though using emails and SMSs instead of phone calls to collect debts may be more aligned to reaching debtors, there is still a need to customize the process.

Debt collection has to go beyond asking customers to repay overdue installments and suggest a way out of the crisis. This is where AI and machine learning can come into play.

Timely warning for delinquency

AI and ML technologies can analyze a great quantity of data from many different sources. It is possible to process call time, the value of certain accounts, collection rates, call effectiveness and much more.

Machine learning is now enabling the lenders to easily identify at-risk borrowers before they reach a point where they are unable to make payments. Machine learning accuracy constantly improves through retaining as new information comes to light and reveals new insights about delinquency risks.

Machine learning can recognize patterns that provide financial institutions a robust way of evaluating risks. This goes beyond the usual credit scores and other rough indicators. It can collate new data and update the metrics in real-time when conditions change, such as during a pandemic. This is not possible when using the risk analysis based on traditional methods.

Focus on at-risk clients

With a…

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