We’re in an era where everything is either data-driven or old school. To avoid being the latter, the uses of artificial intelligence (AI), machine learning (ML) and predictive analytics (PA) in banking and other financial services are being recognized. More than being just buzzwords, AI, ML, and PA have proven, practical applications at banks in lowering expenses, enhancing customer experience and generating higher revenue.
How AI, ML, and PA aid debt collection
In the US, over 30 million people have at least one debt in collection. Adding to this data Federal Reserve Bank says that the total American debt is close $13.5 trillion. The enormity of the ‘debt collection scenario’ triggers questions like, ‘How many people will be able to repay the debt?’ and ‘How are the FIs going to collect such a huge volume of outstanding debt?’ There are added problems like customer complaints on insensitive debt collection procedures, non-compliance, low agent productivity, and high overhead cost. The ideal solution is a proportionate blend of AI, ML and PA added to the debt collection process.
Artificial Intelligence in Debt Collection
Uses of artificial intelligence in debt collection extends from sending automatic reminders to consumers to reducing the risk of compliance. Artificial Intelligence has already created a constructive disruption in the banking industry. It can be used to get the pulse of a consumer by tracking the number of parameters such as their response to communications (for example, email open rates, and click through rates) and browsing patterns.
Machine Learning in Debt Collection
An AI Bot can provide solutions to sensitive banking problems like debt collection by personalizing the experience for each consumer. This is only possible with the help of Machine Learning techniques. ML technology guides a debt collection agency in assessing the creditworthiness of a customer, making accurate assumptions about how, when and where to reach the consumer, thereby improving the productivity of a financial institution.
Predictive Analytics in Debt Collection
Predictive analytics or data-driven debt collection process is considered to be revolutionary. It can be the ultimate game-changer in the debt collection process. Several letters and emails are being sent to a customer in order to increase the likelihood of repayment. By utilizing predictive analytics, banks can simplify the collection process by providing different repayment options, based on a consumer’s financial state, that increases the probability of repayment.
Millennials prefer AI as a debt collection agent over humans
Millennials are constantly looking for new and improved banking solutions and are more open to opting for loans. They are also easily dissatisfied and more outspoken about their negative experiences with financial institutions when it comes to debt collection methods.
Since 2011, the Bureau of Consumer Financial Protection has received 400,500 complaints related to debt collections methods and procedures from consumers in the 21-30 age group. This is 27% of the total complaints received. The complaints relate to inappropriate communication by a debt collection agents, non-intimation of due dates, gaps in communication, inability to express problems and issues causing payment delays, the agents’ inability to provide workaround solutions or flexible payment options.
At interface, we are in the process of implementing an AI debt collection solution at a large bank in the USA. We are also engaged with other banks and credit unions who have expressed interest in using AI to overcome many of the challenges in debt collection. Our clients tell us that their consumers prefer talking to an AI bot over a human agent when it comes to discussing their repayment options. The reasons this growing preference are many and interface strongly recommends every financial institution to explore the benefits of AI in their debt collection process. AI is guaranteed to increase your debt collection rates while improving the customer experience associated with debt collection.
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