Harnessing AI for Tailored Debt Collections to Improve Customer Experience and Reduce NPAs

India’s economy is on the path to growth and moving beyond the pandemic-induced disruptions. Retail lending, as a key catalyst, has continued to rise in recent months and has exceeded pre-pandemic numbers. Collections, which are a crucial part of the loan lifecycle, are one area that must be ready to support this path of profitable growth. The renewed focus on considering debt collections for borrowers has highlighted the need for an overhaul. Recent regulatory guidelines on digital lending and FinTechs highlight the cautious approach to managing future default risk and protecting the interests of the borrower. Together, these challenges fuel the need to automate and revolutionize the industry’s approach to debt collection for the future.

In most cases, debt collection strategies remain complex, inefficient and outdated. The insufficiency of the traditional methodology to anticipate which debts would be collected and which strategy is the most effective, places lenders behind the evolution curve. Using a single remedy without changing tactics depending on the results resulted in a lower recovery rate and huge costs to recover. With an increasing focus on improving the customer experience, reducing operational costs and scaling, banks and other financial institutions are looking to improve the efficiency and effectiveness of their debt collections.

Using technology platforms powered by ML and AI, banks and other non-bank financial companies are now recreating customer experiences in collections and reimagining their operations that support customers and lead to better resolutions. and faster. Deeper insights into default risk and how to manage accounts at risk are used to take a tailored approach.

A modern customer-centric approach to collections

Traditionally, empathy and debt collection have not worked in tandem for a variety of reasons. But that is changing very quickly as lenders realize the growing need to transform their collections to support growth. Using data analytics and AI, and designing algorithms with predictive models in innovative ways, collection managers are rethinking their end-to-end customer engagement strategies.

AI-powered collection technology platforms bring together the power of intelligence, automation, and data digitization to provide a long-term sustainable solution. They not only allow lenders to take a nudge-based support approach, but also help contextualize and personalize engagements. Sophisticated algorithms can help banks and other non-bank financial firms determine customer preferences, challenges, and behavior patterns to align collections strategies accordingly. This is a huge step up from the traditional approach, which is primarily focused on the lender’s urgency to recover without giving due importance to the customer experience.

Holistic digital operations enablement

Digital communication channels today are very mature, armed with full intelligence in terms of reach, integrability, personalization and flexibility to meet the needs of all stakeholders. For lenders, a holistic communication strategy that encompasses multiple channels, including WhatsApp, SMS, Voicebots, Chatbots, IVR, and email, is a great place to start.

ML models enable deeper customer segmentation, recommend the best-fit communication strategy, and provide the insights needed to adjust actions in real time. Communications are personalized with relevant borrower information and vernacular adaptation for better response. Lenders receive the latest level information to sharply refine and optimize their collection approach. A comprehensive digital-first collection model has proven to be extremely efficient, cost-effective, and agile for lenders.

Leverage AI for a support approach

AI-powered intelligent conversational virtual assistants improve customer experience, reduce collection time, increase collection rate, and reduce costs of dealing with delinquent customers. These conversational AI bots, once ready, regularly engage borrowers, assist with payments, and automate reminder notifications without a human agent calling unless absolutely essential.

Pre-scheduled, intelligent Voicebot calls are superior because they allow borrowers to understand the situation, choose when and how to repay. Based on the borrower’s response to the Voicebot call, the next communication is automatically triggered with digital payment links, while the bot stays online to help complete the transaction. This leads to a more human approach to customer engagement compared to the traditional mode of hostile calls and harassment.

Intelligence-driven strategy for collections

Artificial intelligence has the ability to help make smarter choices about when and how to contact customers, based on their behaviors. It is now possible to design algorithms with predictive models using insights gained from data and machine learning. Debt collection strategies can be prioritized and applied based on data derived from risk segmentation, optimal channel prediction based on past communication behavior and intent to pay. Data-driven decisions transform the overall approach to collections with dynamic segmentation, real-time insights, and personalized communications.

Predictive analytics provides lenders with early warning signals about likely defaults and helps modify strategy based on data discoveries. Based on real-time data, lenders can also take appropriate actions throughout the lifecycle, including pre-maturity stage, collections filed, litigation strategies, and track the performance of the teams involved.

Time to reinvent collections

The growing use of AI and ML in lending opens up new areas of interest for banks and non-bank financial companies. In debt collection, they enable capabilities such as early prediction of likely defaults, improved borrower categorization techniques, and contextualized customer engagements to reduce defaults, accelerate collections, and transform customer experiences. An augmented AI-based approach when applied to debt collection, maximizes the potential that technology can have across the entire debt lifecycle, recreating personalized experiences for clients, accelerating resolutions and reducing NPAs.

(The author is Mr. Anand Agrawal, Co-Founder and CTO, Credgenics and the opinions expressed in this article are his own)

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