Making Debt Recovery Smarter and Easier

Credit lending models have gone through a tremendous shake-up in the last few years. With an expanding mobile-first customer base, high demand for credit and flexible regulations, digital lending is growing exponentially. The overall customer engagement transaction has seen a growth in the average from 12.75% to 14.9% in Developed Asia, and from 6.0 to 8.1 in Emerging Asia, as published in the report by McKinsey. This is a direct indicator of an increase in the usage of smartphones to perform financial transactions. With this tremendous growth, however, comes the challenge of ensuring compliant debt collections – without compromising the lending experience.

Modernising debt collection: The new mandate

Financial institutions are increasingly moving away from aggressive collection tactics to recover debts. Traditional practices deployed to monitor potential defaulters and collect overdue amounts often lead to a failure on both the customer experience as well as the recovery front. The recovery success rate of following up with customers on phones, sending emails, and recommending payment plans is 20%, at best. Today’s savvy customers also demand personalised experiences on a digital platform that affords privacy and convenience. This is forcing companies to rethink their default management practices by adopting innovative technologies to streamline the collection process to minimise charge-offs, loss of accounts and delinquencies.

How AI is revolutionising debt collection

As the lending industry becomes increasingly data-driven and customer-centric, emerging technologies such as artificial intelligence (AI), machine learning (ML) and predictive analytics help drive personalisation, enhancing customer experience, reducing risks and increasing revenues.

Ways AI is redefining debt collection:

Identifying potential defaulters

AI is already being deployed by financial institutions to assess loan applications for credit risks at the credit scoring stage. A growing number of Fintech companies and financial institutions are using AI to create alternate lending data scores for over 80% of the Indian population who have no credit scores. Debt collectors are also using AI to gather deeper insights from the data collected on customers’ accounts and their online activities. Such insights are then used for running a predictive analysis to identify potential default cases and take proactive measures such as prioritising calling lists.

Defining a collection strategy and improving compliance

Financial institutions design their collection strategies based on their specific business goals and IT infrastructure. Depending on their digital maturity, they either leverage in-house data and resources or use third-party assistance to develop and implement more complex recovery models. Regardless of the approach they use, AI-based collection platforms help identify the best times and channels to reach customers on, resulting in higher engagement and repayment levels as compared to repeated phone calls. Acima, a provider of leases for online shoppers uses an AI platform that identifies which customers are past due, those that are most likely to pay, the best day and time to call a customer and so on – by analyzing previous call history. The platform also continuously self-learns to get smarter over time.

Personalising customer interaction

Sourcing and monitoring customer data from various external sources is an extremely tedious task. Nonetheless, this is crucial to effective debt collection. For instance, a sudden discontinuation of salary transfer in a customer’s bank account is indicative of a job transfer or even a loss of job. The collection department can use such information to reach out to the customer and provide proactive financial assistance. Financial institutions can also offer loss mitigation strategies to specific customers such as delinquent mortgage or motor vehicle customers to prevent loss of ownership by using ML to carry out a customer profile analysis and figure the best suitable mitigation strategy. Intelligent bots can then be deployed to execute the selected strategy. According to the CEO of Brighterion, a MasterCard company, effective use of AI can help reduce delinquency rates by about 76%.

Maximising debt collection performance with AI

Forward-looking financial institutions are recognizing the immense potential of AI in debt collection and are already implementing AI and ML models to revamp their default management systems. Even as its adoption increases, it’s important to remember that AI is a tool and not an end solution – it is best used to empower agents to enhance customer experiences and customers to repay their debts.