The financial services industry has reached a crossroads at which the distribution of credit products in being revolutionized by multiple emerging technologies including alternative data and artificial intelligence. The traditional credit assessment model, based largely on long histories of financial data such as credit scores and income statements, is being redefined. As the Indian economy expands and diversifies, especially in unserved and underserved markets, the story is no longer simple and must ensure credit penetration in a more nuanced and dynamic manner. Harnessing these cutting-edge technologies deepens the outreach which leads towards customized financial solutions for customers.
The Power of Alternative Data
Alternative data is a non-conventional source of information wherein one gauges creditworthiness. The usage of a mobile phone, the history of payments by utilities, the way one uses social media, e-commerce transactions, and geo-location are some examples. When you look at Tier II and III cities, rural areas, and semi-formal sectors, alternative data becomes crucial in the Indian context because traditional credit scores may not be able to portray a good or bad picture of the financial health of a potential borrower.
By providing such data, financial institutions will have a more comprehensive view of the creditworthiness of consumers and their ability to pay loans. For example, a person’s frequent mobile recharge or timely utility bill payments can show good credit behavior, even though income cannot be officially verified in some cases.
Alternative data, therefore, can provide a strong supplementary layer beyond the traditional financial information as illustrated, especially for industries as small business lending and personal loans where the credit histories of the clients may be wanting. This opens an opportunity of lending to underserved populations like MSMEs, Gig economy workers, and informal workers.
AI-Driven Credit Scoring Models
Where is the real engine, of course, in Artificial Intelligence (AI) and Machine Learning (ML)? It is where the critical analysis of alternative data comes into play. Alternative data provides the richer pool of information.
For instance, ML algorithms trained to have a higher hit rate in predicting credit risk can be designed based on spending patterns, transaction history, social networking behavior, and other lifestyle indicators. They continually learn and adapt over time through refinements of their models to adjust to real-world borrower behavior and changing economic conditions.
With AI-based tooling that combines both traditional and alternative data it gives us far more precise risk estimates than what we could have achieved through the earlier systems alone and has yielded a much better loan approval rate. At the same time, we see better-performing loan portfolios. AI models are data-processing machines in real time and therefore offer instantaneous credit decisions that improve the experience of customers while ensuring sound risk management.
Increasing Financial Inclusion
Driving financial inclusion is one of the most promising applications of AI and alternative data in credit distribution. Traditional banking models usually tend to exclude large portions of the population because of lack of financial records. Now through alternative data, individuals without formal credit histories are included in the fold.
It is highly relevant to India, where a large population remains either unbanked or underbanked and use of AI models with alternate credit data help equip us better to serve such demographics with customized loan products with the appropriate kind of risk-pricing mechanisms in place. Digital channels and mobile banking are supporting greater outreach for credit products beyond traditional geographical and logistical constraints.
Operational Efficiency and Risk Management
Credit processes that utilize AI tools offer significant benefits for operational efficiency and risk management. Traditional underwriting processes are often time-consuming and resource-intensive while scaling various customer segments, and the use of automated systems powered by AI processes helps speed up the processes and reduce the amount of manual interventions, lower operational costs, and allow financial institutions to expand rapidly.
In addition, AI-based systems have better capabilities in risk management. Predictive analytics in such a system can alert potential risks early enough to ensure proactive measures are taken. For example, when a borrower shows signs of distress, repayment options or financial counseling will be offered to him before he reaches default stage. Thus, these become not only safeguards for the lender’s interest but also assist borrowers better in managing their financial health.
Future of lending
This is much more than just a temporary trend, the future of lending, indeed: the role of alternative data and AI in credit distribution. As AI models advance and as access to alternative data expands, customization in credit products will grow, management of risk will improve, and more persons will gain access to credit. In embracing innovations that deliver financial solutions to the diverse needs of our customers, we envision ourselves becoming the best financiers in the private sectors of India.
It integrates alternative data and AI, thus offering an unparalleled opportunity to deepen credit distribution, especially in underserved markets. By using these technologies, financial institutions can catalyze more inclusive, efficient, and robust credit ecosystems better aligned with India’s larger goals of economic growth and financial empowerment.
Written By – YS Chakravarti, MD & CEO, Shriram Finance
Disclaimer:The views expressed in this article are those of the author and do not represent the stand of this publication.
