In 2024, personal loans grew by 14.4% in India, surpassing ₹55.3 lakh crore, indicating the rising demand for credit. However, Indian banks have highlighted a concerning trend wherein non-performing assets (NPAs) increased by a whopping 51% to ₹11,210 crore in June 24 against ₹7,422 in March 23.
These figures indicate that despite a growing demand for credit, lenders, including banks, haven’t been able to maintain steady personal loan portfolios.
It is also a wake-up call for lenders to revisit their credit underwriting and scoring techniques and improve them by leveraging modern and advanced technologies— machine learning (ML, artificial intelligence (AI), and larger datasets.
This article examines the future of credit scoring and how predictive analytics and other technologies will reshape it in the upcoming years.
What is Credit Underwriting?
Credit underwriting is the end-to-end process used by lenders, including financial institutions, banks, and NBFCs, to determine a borrower’s ability to repay the loan.
Typically, lenders evaluate the borrower’s creditworthiness based on different factors, such as employment status, financial stability, debt-to-income ratio, credit history, and income.
What is a Credit Score?
A credit score is an important parameter that shows the consistency of a borrower in repaying loans. The credit score is determined by licensed credit information companies—The Credit Information Bureau (India) Limited (CIBIL), Equifax, Highmark, and Experian.
That said, the CIBIL score is the most popular credit score among the aforementioned credit information agencies.
Credit Underwriting and Scoring: Future Outlook
The increasing number of NPAs, particularly in the home loan segment, along with the significant number of underbanked population, should serve as an eye-opener for lenders who are looking to secure their presence in the financial ecosystem.
Here are a few worthy alternatives to ensure accurate credit underwriting and risk assessment:
Use of Alternate Data
Despite recording the best financial metrics over the past decade, Indian banks continue to find solutions to tackle a long-standing challenge – financial inclusion.
Nearly 16 crore Indians were classified as “credit underserved” in 2021 and are referred to as “no file” or “thin file” customers by lenders.
This lack of data from conventional credit sources has compelled financial institutions to examine other data points to enhance their credit decision-making capabilities.
Experian notes that around 62% of financial institutions are leveraging alternative data, such as transactional data, social network data, rent payments, and digital behavioural data.
This allows lenders to provide credit opportunities to a wider audience and conduct tailored risk assessments.
In short, alternative data can help lenders reach potential borrowers who have been sidelined due to a lack of credit history and limited access to conventional credit services.
Credit Assessments with Machine Learning
Nearly 67% of India’s population, or 938 million people, have taken a personal loan in their lifetime for various reasons, including purchasing a home, travelling, and renovating.
Traditional credit assessment and scoring techniques limit the lender’s ability to collect, analyse, and make data-driven credit decisions. In this scenario, machine learning algorithms are transforming credit scoring by offering accurate creditworthiness assessments.
They can also handle large volumes of non-conventional data such as social media and online behaviour, utility payments, and rent and utility payments to uncover key trends and patterns, paving the way for informed lending decisions.
Another significant advantage of using machine learning algorithms for credit scoring is their unmatched efficiency and ability to analyse large datasets within seconds, which allows for instantaneous credit scoring decisions.
Many digital lenders are also pushing for paperless loan underwriting and accelerating the data collection process by sourcing data from open banking sources, including APIs.
Improved Predictive Accuracy and Fairness
Apart from providing faster credit decisions, AI-based credit scoring systems consider a combination of real-time and historical data to improve a lender’s predictive capabilities.
The combination of AI-powered predictive analytics and traditional credit assessment techniques allows lenders to establish relationships between different variables such as spending patterns, geographic location, and repayment history to gain deeper insights into a borrower’s financial behaviour, leading to better and more accurate credit predictions.
This approach helps lenders make credit accessible to people whom traditional credit scoring systems sidelined and who lack formal lending institutions in remote areas of India.
Further, since AI has access to a broader range of data points, it plays a key role in minimising biases that are typically present in credit scoring systems that primarily rely on conventional data sources, such as collateral, income, and credit history, which do not take into account the unbanked or underserved population.
Parting Notes
It is clear that AI and machine learning are revolutionising the credit-scoring ecosystem across lending institutions in India. These lay a solid foundation for fair, accurate, and efficient credit assessments by leveraging the power of alternative data and real-time credit evaluation.
Lenders who invest in AI and embrace technology to improve credit scoring will be in a better position to enhance financial inclusion and manage risk.
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