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Reimagining Debt Collection: How Artificial Intelligence (AI) and Machine Learning (ML) Technologies Can Enhance the Process and Reduce Deliquencys

Lending is a risky business; what with defaulting, delinquencies, and inefficiencies that co-occur with it. Add to this the bigger challenge of recovering past-due debts and tracking down unpaid debts. For the most part, loan collection strategies have remained complex, obsolete, and ineffective. But in today’s digitally driven world, customers are showing a preference for flexible, automated, and easily-accessible repayment options. Thanks to the Big Data revolution, lenders and debt collection agencies can leverage Artificial Intelligence (AI) and Machine Learning (ML) to improve recovery and resolve other challenges facing the industry.

Up until now, loan collection used to be more or less a call-and-respond business exclusively. However, this process of operation caused a plethora of errors, escalating into legal problems with dire reputational consequences. Advanced AI and ML capabilities are the solution to avert such situations. A well-thought-out loan collection strategy powered by the aforementioned technologies can optimize debt collection, reduce collection costs, and save time. Below, read more on how AI and ML technologies can enhance the loan collection process –

  1. Classification of Borrowers

AI and ML tools have the potential to aid lenders understand their borrowers by giving access to valuable customer data. Traditionally, borrowers were largely classified by industry/ income groups; but data-driven ML solutions can present in-depth customer behaviour and history, which can help categorize them in specific market segments. Using these insights, lenders can also build borrower profiles to determine who is likely to resolve delinquencies and who could need a modified approach, like debt restructuring or alternate repayment facilities. This will help lenders explore new options to optimize their collections, offer customer satisfaction, and boost recovery and profitability.

2. Personalizing Customer Experience

Whether a lender opts for traditional debt collection methods or new-age digital strategies, the ultimate goal is to retain the human element in the collection process, whilst improving customer response. This can be achieved using AI-backed debt collection software that use bots wit human-like voices and extracts customer data from multiple sources. Depending on their preferences and needs, lenders can employ an omnichannel communication strategy, as emails, voice calls, and text messages, whatsapp to optimize the impact of the collection process. The sourced customer data, too, can be put to good use; for instance, if it is found that a customer has not received their salary for two to three months consecutively, implying a job loss or business cost cutting, the debt collectors can proactively offer financial assistance or modified repayment option. Adopting a hyper-personalized approach can help reinforce customer engagement and avert loss of ownership, as well as complete debt default.

3. Predicting Default Probability

One of the key reasons for overdue receivables is the inability of lenders to identify distressed accounts that show signs of default. Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) can source structured and unstructured data and analyze it in real-time to predict potential defaulters. By using these advanced platforms, lenders can form recovery plans, monitor such borrowers, and thereby recover the debt, without affecting the customer experience. In agriculture related  loans like tractors , farm equipment, micro – finance , dairy , crop loans tracking the quality of harvest is equally important to tailor the customer experience and detecting Early Warning Signals ( EWS). In a way, AI and ML deliver a clear warning against high-risk customers to lenders ahead of time

4. Digital Communication

To ensure the business remains ahead of the curve and compliant with the regulatory framework, it is important that lenders stay agile and embrace a digital collection model. Customers also prefer to contact businesses on their preferred communication channels at their preferred time. In this scenario, making digital the primary channel of communication is an ideal choice for lenders. Using an automated, omnichannel communication process, organizations can ease their collection department’s communication efforts and debtors can be engaged through emails, text messages, and automated voice calls.

5. Determining an Effective Compliance Strategy

Most financial institutions apply a common, one-size-fits-all collection strategy. Basis their IT infrastructure, digital expertise, and internally pooled data, complex collection models are used, which may or may not translate to fruitful recovery. Here is where AI and ML tools can help identify the suitable time to initiate digital communication and also the best channels to reach out to the debtor. This approach results in greater potential of a response, higher retention, and improved collection rates, all while eliminating hostile repetitive calls.

As AI and ML continue to disrupt the debt collection industry, both lenders and borrowers can benefit significantly from this modernization. These technologies open doors to new opportunities for introducing advanced tactics to better interact with borrowers, combat default accounts, avert additional penalties, eliminate potential insolvency, and boost debt collections.


(This article is written by  Sidharth Agarwal, Director, Mobicule Technologies, and the views expressed in this article are his own)

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