AI-driven Debt Recovery Approach To Accelerate the Efficiency Rate

Enabling an AI and ML-enabled approach in the debt recovery process increases the efficacy of debt recovery processes.                               

FREMONT, CA: Technology-driven transformations have become a mere demand for businesses in varied arenas, especially in recent years, where debt collection is no exception. That is, a tendency towards new technologies in banking and financial services is likely reshaping the domain in the APAC space. APAC’s fintech sector has contributed indispensable growth to enterprise value in 2022 and is anticipated to soar furthermore in the future.

In the previous fiscal year, nearly 60 per cent of loans were disbursed by non-banking financial companies (NBFC) via digital lending applications (DLA), following the artificial intelligence (AI), machine learning (ML), automation, and data analytics-based approach in the sector. These very technologies hold long-term potential for transformation in the financial services ecosystem.

Inconsiderate of the downturns caused by the advent of the pandemic, the APAC arena has emerged as the fastest-growing economy globally, making significant processes in the financial space. However, credit penetration in households and banks may likely elevate in the region, holding a crucial role in the financial growth of the region. With inefficiency in the debt recovery process increasing critically, a demand for a smooth and potential debt recovery process has risen in the arena.

AI has emerged as a breakthrough innovation in the fintech space owing to its capability to explore new opportunities in the sector, inconsiderate of the challenges protracting in the arena. One such paradigmatic approach is a radical shift among lenders and borrowers from manual follow-ups and crude tactics to a more automated and borrower-centric approach in the debt recovery process.

Deploying AI in debt recovery has enabled increased customer segmentation and personalised and effective communication, thereby enabling lenders to establish contact with borrowers based on their delinquent traits and take a preemptive approach to check on their delinquencies. Similarly, leveraging machine learning capabilities has also enabled an efficient usage of theories and learnings like voice bots rather than relying on live agents as a communicator between customers and organisations.

For instance, borrowers find it highly convenient to share their financial concerns with multilingual bots that also aim at resolving their queries, in addition to triggering texts and several other forms of communication. It enables smooth repayment as an assistant, where reminders are often automated, requiring less or no human intervention in the debt recovery process. Wherein, an automated debt recovery process has proven to be highly productive with increased efficiency, unlike the manual approach, and has eliminated the risk of errors in the process.