The global economy has been impacted by several challenges across the world. Political conflicts lead to changes in trade policies, tariffs, and thus political uncertainty have a significant influence on the global economy and financial systems. During these global headwinds, sustaining resilience in the financial system continues to be the foremost objective for any global economy.
During such times, credit serves as a key pillar in this journey. It is not only the backbone of financial assistance but also supports consumption demand, business growth, and investment. While regulators and government policies take required measures to constrain the impact of these emerging global trends, it is equally important for lenders to manage portfolios and control risk across different segments within their portfolios. Identifying resilient borrowers and lending to stable segments is critical to maintaining a balance between risk and growth.
Managing risk in a rapidly evolving lending landscape
Lenders have become more cautious and have built robust risk models and intelligence in their risk assessment processes to identify such borrowers. However, in the evolving lending industry, newer risks continue to emerge, particularly in unsecured digital lending, micro-finance, and MSME lending. The unprecedented growth in unsecured lending has led to concentration risk for lenders, increased borrower leverage, and multiple loans being extended to the same set of customers. Credit risk in lending has thus become extremely critical.
Unsecured loans, including personal loans, credit cards, and consumer loans, are generally provided without collateral and are often the first to experience defaults during financial stress. Similarly, small businesses and microfinance customers are typically the first to feel the impact of economic shocks. The solution is not to become overly conservative in lending, but to use borrower data, credit history, and other behavioural indicators to segment customers more effectively.
Using data and analytics for early risk detection
Lenders also need to closely monitor existing borrower portfolios and identify early warning signs. Applying analytics on Credit Bureau data can enable lenders to segment high-risk customers and also predict future behaviour. Additionally, with the use of other alternate forms of data available, lenders can build early warning systems to identify vulnerable segments. Thus,identifying deserving and resilient borrowers while avoiding over leveraged applicants. This approach of portfolio management can assist in building a resilient and balanced portfolio.
AI and advanced models strengthening credit decisions
AI-led machine learning models help identify outliers and emerging trends, enhancing risk management capabilities. These models enable continuous evolution of underwriting processes by incorporating emerging risk trends into credit risk models. Building robust probability of default (PD), loss given default (LGD), and stress testing frameworks can help lenders proactively identify and mitigate risky scenarios resulting in sustainable business
While risks will continue to emerge as the economy evolves, a resilient lending system will not only protect lenders and borrowers but also contribute to the long-term financial stability of the economy.
Disclaimer: The information provided in this article is for informational purposes only and does not constitute financial, legal, or professional advice. While every effort has been made to ensure accuracy, readers should verify details independently and consult relevant professionals before making financial decisions. The views expressed are based on current industry trends and regulatory frameworks, which may change over time. Neither the author nor the publisher is responsible for any decisions based on this content.
Sachin Seth, Regional Managing Director, CRIF India & South Asia
