Lending is a risky business. Concomitant defaults, delinquencies and inefficiencies. Added to this is the greater challenge of collecting overdue debt and tracking outstanding debt. In most cases, loan collection strategies are complex, outdated and ineffective. However, in today’s digitally-driven world, customers prefer flexible, automated, and easily accessible repayment options. Thanks to the big data revolution, lenders and debt collectors can leverage artificial intelligence (AI) and machine learning (ML) to improve recovery and solve other challenges facing the industry.
Historically, loan collection has been more or less limited to call-and-response businesses. However, this operational process led to many errors, which escalated into legal issues and severely impacted its reputation. Advanced AI and ML capabilities are the solution to avoid such situations. A well-thought-out debt collection strategy leveraging the aforementioned technologies can optimize debt collection, reduce collection costs, and save time. Below we detail how AI and ML technologies can enhance the loan collection process.
- Borrower classification
AI and ML tools have the potential to help lenders understand their borrowers by providing access to valuable customer data. Traditionally, borrowers were categorized primarily by industry/income group. However, data-driven ML solutions can present detailed customer behavior and history, helping to segment customers into specific market segments. Using these insights, lenders can also profile borrowers to determine who are more likely to resolve their arrears and who need modified approaches such as debt restructuring or alternative repayment facilities. can. This enables lenders to optimize collections, increase customer satisfaction, and explore new options to increase collections and profitability.
2. Personalize the customer experience
Whether lenders choose traditional debt collection methods or new-age digital strategies, the ultimate goal is to improve customer responsiveness while maintaining the human element in the collection process. This can be achieved using AI-powered debt collection software that uses bots with human-like voices and extracts customer data from multiple sources. Depending on their preferences and needs, lenders can employ omnichannel communication strategies such as email, voice call, text her message, whatsapp to optimize the impact of the collections process. You can also make effective use of customer data as a source. For example, if a customer finds that she has not received a salary for 2-3 months in a row, implying a loss of employment or a reduction in business costs for her, the debt collector will actively seek financial assistance or We can offer modified repayment options. Adopt a highly personalized approach to enhance customer engagement and avoid loss of ownership or outright default.
3. Estimation of probability of default
One of the main reasons receivables are delinquent is the lender’s inability to identify distressed accounts that show signs of default. Artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) can take structured and unstructured data and analyze it in real time to predict potential defaulters. These advanced platforms enable lenders to develop collection plans, monitor such borrowers, and collect debts without impacting the customer experience. In agriculture-related loans such as tractors, farm equipment, microfinance, dairy and crop loans, tracking harvest quality is equally important to tailor customer experience and detect early warning signals (EWS). is. In a way, AI and ML give lenders clear warnings in advance of high-risk customers.
4. Digital communication
To ensure that businesses stay ahead of the curve and comply with regulatory frameworks, it is critical that lenders remain agile and adopt digital collection models. Customers also prefer to contact businesses on their preferred communication channel at any time. In this scenario, making digital the primary communication channel is the ideal option for lenders. By using an automated omnichannel communication process, the organization facilitates the communication efforts of the collections department, allowing debtors to engage through email, text her messages, and automated voice calls.
5. Determining an Effective Compliance Strategy
Most financial institutions apply common uniform collection strategies. Complex collection models are used based on IT infrastructure, digital expertise and internally pooled data. This is where AI and ML tools can help identify the right time to initiate digital communications and the best channels to reach out to debtors. This approach eliminates repeat and hostile calls while increasing the likelihood of response, increasing retention and improving recovery.
As AI and ML continue to disrupt the debt collection industry, both lenders and borrowers can greatly benefit from this modernization. These technologies are new to improving interactions with borrowers, combating default accounts, avoiding additional penalties, eliminating the possibility of bankruptcy, and introducing advanced tactics to facilitate debt collection. Open the door to opportunity.
(This article was written by Sidharth Agarwal, Director of Mobicule Technologies and the views expressed in this article are his own)
