The adoption of machine learning (ML) is enabling more Indian consumers to access formal credit, while helping lenders reduce risk and streamline decision-making, according to a new study conducted by Forrester Consulting and commissioned by Experian.
The study is based on responses from 109 senior credit risk decision makers and highlights how ML-based models are reshaping lending workflows across product categories. Almost 93% of lenders applying ML to auto loans reported an increase in approval rates, and 90% noted a decrease in credit card bad debt ratios. Overall, 79% of educational institutions said ML has enabled them to serve customer groups that were previously excluded from traditional scorecards.
India's lending market has expanded rapidly due to an increase in new borrowers, digitization of the underwriting process, and increased consumption. In this context, ML is seen as enabling more accurate risk prediction and faster onboarding. More than two-thirds of respondents cited increased risk accuracy and operational efficiency as key benefits, and 71% said ML will further automate decision-making. A majority (78%) believe that most credit decisions will be fully automated within five years.
Financial inclusion is emerging as a central impact area. Respondents noted the ability of ML-based models to leverage alternative datasets and behavioral metrics to provide a more nuanced assessment of thin-file consumers. According to the report, this expansion into underserved segments has occurred in parallel with improvements in portfolio quality, with 71% reporting improved profitability related to lower non-performing loans.
Generative AI is also gaining traction as a productivity tool, primarily in risk analysis. 84% of lenders believe GenAI can accelerate model development and deployment, while approximately 70% see GenAI's greatest benefits in regulatory documentation and validation cycles, traditionally time-consuming areas for risk and compliance teams.
However, adoption is not uniform and institutional barriers remain. 65% of non-adopters believe implementation costs are too high compared to perceived benefits, and 44% say they do not fully understand the potential value of ML. Concerns about explainability and compliance remain strong, with more than half citing transparency issues and regulatory uncertainty. Legacy technology systems and siled data architecture were also cited as constraints.
“India's lending ecosystem is undergoing a fundamental transformation and machine learning is at the heart of this transformation,” he said. Manish Jain, Country Managing Director, Experian, India. “ML enables lenders to make more accurate risk predictions, approve the right customers faster, and extend lending to millions of people previously excluded from the formal financial system.”
Mariana Pinheiro, Experian EMEA & APAC CEO; “More than just a risk modeling tactic, ML is becoming a core part of financial system design,” he said. “Machine learning is unlocking access to financial services for millions of people who were previously excluded. By leveraging alternative data and more sophisticated risk models, ML can help lenders make fairer and more accurate decisions, especially for consumers with limited financial backgrounds.”
The findings highlight the stage at which lending organizations believe automation, risk accuracy and comprehensiveness are becoming integrated. As the credit market becomes more competitive, early adopters of ML and GenAI are expected to increase operational velocity, reduce losses, and expand their addressable customer base.
