Non-performing loans (NPL) have been a major and increasing issue in the Nepalese banking sector, primarily among commercial banks, which has led them to adopt technological solutions. In Nepal, economic uncertainty and economic downturn have raised higher credit risks and default rates. Nepalese commercial banks can use tools like Artificial Intelligence (AI) with catboats and Data Analysis applications to handle, monitor and reduce bad debt perils.

In recent years, the Nepalese banking sector has experienced a massive increase in NPL. According to Nepal Rastra Bank (NRB), the NPL ratio of commercial banks rose significantly from 1.20 per cent in 2021 to 2.98 per cent in mid-2023. The financial condition of the borrowers revealed increased distress due to finance companies' high default ratio, which reached 4.5 per cent. Several experts link this rapid growth in default loans to economic deceleration along with stricter monetary policy with lasting COVID-19 impacts. Credit defaults present a critical issue that forces commercial banks to face difficulties in sustaining their financial liquidity and profitability levels.

Financial institutions worldwide are adopting AI solutions to anticipate default loans as well as debt automation and optimised loan recovery operations. Nepal's commercial banks can use AI-based risk assessment models that aim to reduce the accumulation of non-performing loans. AI systems that monitor loans are said to be able to cut NPL ratios by 30 per cent through better detection of risks and improved loan repayment systems.

The process of approving loans by Nepalese banks historically depended on both past financial information and human evaluation methods. Mistakes occurred regularly when banks attempted to evaluate borrowers' payment capability. The application of AI transforms traditional loan analysis through its behaviour-based examination of financial data patterns from expense patterns and transaction flows alongside internet-based financial behaviour to reveal previously undetected credit risks. The use of AI processes alternative information from social media and e-commerce records and tax filing documentation to forecast borrower reliability. AI systems can perform real-time financial behaviour tracking, which detects loan problems before they become defaults through continuous monitoring.

Ineffective loan recovery efforts of Nepalese banking institutions result from postponed intervention techniques and insufficient debt collection operations. AI-powered automated systems combine with chat boots to deliver efficient repayment communication for borrowers along with customised debt settlement options and automated detection of intentional defaulters followed by required legal procedures for prompt collection. A Reserve Bank of India (RBI) study demonstrated that AI-based collection strategies raised Indian bank recovery numbers by 20-25 per cent. Through AI-driven collection methods Nepalese banks can achieve results comparable to other banking institutions in the country.

Unauthorised lending practices with fake documents cause most of the NPL situations across Nepal. Through facial recognition and digital signature verification, AI detects fraudulent credentials while pattern recognition algorithms detect fraud during transactions. The system also warns about high-risk borrowers for pre-loan disbursement. A recent ADB report 2023 shows that the increasing use of AI among the banks in South Asia helped in fraud detection by stopping 35 per cent fraud loan approval cases. Such technology would generate substantial gains for Nepalese financial institutions.

AI implementation for Nepal's banking system presents both benefits and obstacles when establishing this new technology. Strong implementation expenses create substantial difficulties in adopting AI systems because institutions need to invest in infrastructure while developing proper security protocols and employee training. Because Nepal's present data regulations lack the necessary capabilities to process AI-driven financial analytics, there are serious concerns about improper use of confidential information.

AI has demonstrated successful NPL management capabilities in different countries. The Chinese financial institutions, ICBC together with the Bank of China, have implemented AI-driven chatbots, which combined with predictive analytics in the recovery of bad loans. The State Bank of India (SBI) implemented an AI-based NPL prediction system that decreased bad loan occurrences by an impressive percentage over two years. AI-based loan underwriting implemented by top banks, including JPMorgan Chase and Citibank, helps these financial institutions reduce NPL risks in the USA.

Nepalese NPL management will achieve AI integration success provided that administrators and banking institutions invest capital into AI infrastructure along with regulatory framework development alongside professional banking training programmes. The development of successful AI systems requires government support in addition to partnership between public entities and private institutions. The NRB should establish AI regulations regarding ethical data practices while bank professionals must receive AI training.

The increasing number of NPL creates financial instability across Nepal, which requires immediate adoption of innovative solutions by commercial banking institutions. The application of AI provides banks with an opportunity to evaluate risks better while recovering loans and minimising default occurrences. Through financial implementation based on AI, the banking system can establish itself as a more resilient, efficient and transparent structure.

Gautam is with Sanima Bank