The Role of artificial intelligence in enhancing loan recovery strategies in Sri Lanka

Abstract

Lankan Licensed commercial banks maintain economic stability and financial inclusion, but struggle with loan recovery which affect their financial stability. The increasing Non-Performing Loan (NPL) ratios shows that the loan recovery process has become inefficient, creating financial risks for sustainability. Despite traditional recovery methods, challenges such as regulatory constraints, operational inefficiencies, and borrower behavior continue to delay loan repayment. Hence, this research addresses a research gap by examining how Artificial Intelligence (AI) can improve loan recovery processes, making them more effective and efficient. This study primarily aimed to identify whether performance expectancy, effort expectancy, and social influence impact the intention to use an AI-based loan recovery system in Sri Lanka. By focusing on these key constructs, the study focuses on providing empirical evidence on how individual perceptions and social factors shape employees’ readiness to adopt AI technologies in the loan recovery process. This research adopts a positive philosophy and a quantitative research strategy. The objective is to be achieved using quantitative analysis from the data collected through structured questionnaires from a sample of 100 loan recovery officers selected using the Purposive sampling method from the population of recovery officers across Western Province branches. The findings indicate that effort expectancy and social influence are significant factors affecting the adoption of AI-based loan recovery, and performance expectancy is not significant. These findings shows that the banks should focus on ease of use, and peer and organizational support, as well as equipping them with sufficient infrastructure and resources to motivate the adoption of AI systems to improve the NPL ratios and financial sustainability of the banking sector in Sri Lanka.