Credit provision plays a vital role in driving economic growth, but India faces a notable credit gap despite stringent regulations and strong economic fundamentals. India's credit-to-gross domestic product (GDP) ratio of 50 percent is significantly lower than China's 177 percent, highlighting the disparity. Particularly affected are micro, small and medium enterprises (MSMEs) and nano-SMEs borrowers, who often struggle to access banking services due to high operational costs and stringent underwriting procedures.
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Bridging this gap presents a huge opportunity for artificial intelligence (AI) and machine learning (ML) technologies. These innovations can revolutionize how financial institutions extend credit and make decisions, providing solutions across multiple stages of the customer lifecycle.
Recently, the credit sector has shown robust growth of 16% in FY24, driven primarily by demand for small unsecured loans. However, concerns over unsustainable lending practices such as over-indebtedness and poor underwriting have led to regulatory action by the Reserve Bank of India. This tightening of regulations is expected to moderate credit growth to 11-12% in FY25, highlighting the need for effective risk management, especially in the small lending business.
Borrower risk assessment includes the evaluation of ability and willingness to repay, and AI models provide a versatile tool for this purpose, enhancing financial institutions' decision-making process.
1. Credit decision: AI/ML technologies can analyze credit bureau reports to uncover insights into loan repayment behavior, default propensity, income distribution, and assess a borrower’s repayment capability.
2. Fraud detection: By scrutinizing user behavior and data integrity during the loan application and KYC process, AI can flag potential fraud risks and assess a borrower's honesty and willingness to repay.
3. Early warning system: After a loan is made, AI can monitor repayment patterns and identify potential risks early, enabling a proactive collection strategy.
4. Operational Efficiency: AI-driven automation streamlines workflows, reduces processing times, and minimizes errors in operational processes.
5. Collection efficiency:AI models analyze repayment patterns and interactions with borrowers to optimize collection strategies and improve collection rates.
The choice of AI/ML algorithms depends on business needs and the quality of data: unsupervised learning is beneficial for agencies working with unstructured data, while supervised learning enhances decision-making based on established user data.
Going forward, AI/ML technologies will have a significant impact on two specific credit sub-sectors: women borrowers and rural/semi-urban borrowers. Custom AI tools can help mitigate gender bias in underwriting and leverage alternative data sources for more inclusive lending practices.
In conclusion, AI/ML technologies have great potential to transform access to and delivery of credit in India, support inclusive economic growth, and address the unique challenges of various borrower segments.
