Artificial Intelligence and machine learning in credit risk assessment

Credit provision plays a pivotal role in driving economic growth, yet India faces a pronounced credit gap despite stringent regulations and strong economic fundamentals. The credit-to-Gross Domestic Product (GDP) ratio in India stands at 50%, significantly lower than China’s 177%, highlighting this disparity. Particularly affected are micro, small, and medium enterprises (MSMEs) and nano-SME borrowers, as they often struggle to access banking services due to high operational costs and challenging underwriting processes.

Addressing this gap presents a prime opportunity for Artificial Intelligence (AI) and machine learning (ML) technologies. These innovations can revolutionize credit provision and decision-making by financial institutions, offering solutions across various stages of the customer lifecycle.

Recently, the credit sector has shown a robust growth of 16% in FY 2024, predominantly driven by the demand for unsecured loans of smaller amounts. However, concerns over unsustainable lending practices, such as excessive indebtedness and inadequate underwriting, have prompted regulatory measures by the Reserve Bank of India. This regulatory tightening is expected to moderate credit growth to 11-12% in FY 2025, underscoring the need for effective risk management, particularly in small-scale lending operations.

Assessing borrower risk involves evaluating their ability and willingness to repay. AI models provide versatile tools for this purpose, enhancing decision-making processes in financial institutions:

1.Credit Decisioning: AI/ML techniques can analyze credit bureau reports to uncover insights into loan repayment behaviors, default trends, and income distributions, thereby assessing borrowers’ repayment capacity.

2. Fraud Detection: By scrutinizing user behavior and data integrity during loan applications and KYC processes, AI can flag potential fraud risks and assess borrowers’ integrity and willingness to repay.

3. Early Warning Systems: Post-loan disbursal, AI helps monitor repayment patterns and identify potential risks early, enabling proactive collection strategies.

4. Operational Efficiency: AI-driven automation streamlines workflows, reduces turnaround times, and minimizes errors in operational processes.

5. Collection Efficiency:AI models analyze repayment patterns and borrower interactions to optimize collection strategies and improve recovery rates.

The choice of AI/ML algorithms depends on business needs and data quality. Unsupervised learning is valuable for institutions dealing with unstructured data, while supervised learning enhances decision-making based on established user data.

Looking ahead, AI/ML technologies are poised to significantly impact two specific credit sub-sectors: women borrowers and rural/semi-urban borrowers. Custom AI tools can help mitigate gender biases in underwriting and leverage alternative data sources for more inclusive lending practices.

In conclusion, AI/ML technologies hold immense potential to transform credit access and delivery in India, supporting inclusive economic growth and addressing the unique challenges of various borrower segments.