Machine learning expands access to credit in India

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 new research commissioned by Experian and conducted by Forrester Consulting.

The study, based on responses from 109 senior credit-risk decision-makers, highlights how ML-based models are reshaping lending workflows across product categories. Nearly 93% of lenders that apply ML to vehicle loans reported higher approval rates, while 90% noted a decline in bad-debt ratios in credit cards. Overall, 79% of institutions indicated that ML is allowing them to serve customer groups previously excluded by traditional scorecards.

India’s lending market has expanded rapidly with the growth of new-to-credit borrowers, digitised underwriting processes, and increased consumption. Within this context, ML is viewed as an enabler of sharper risk prediction and faster onboarding. More than two-thirds of respondents cited improved risk accuracy and operational efficiency as core benefits, and 71% said ML is allowing greater automation of decisions. A majority 78% believe most credit decisions will become fully automated within five years.

Financial inclusion is emerging as a central area of impact. Respondents pointed to ML-based models’ ability to draw on alternative datasets and behavioural indicators, offering more nuanced assessments of thin-file consumers. According to the report, this expansion into underserved segments is occurring alongside better portfolio quality, with 71% reporting improved profitability linked to reduced bad debt.

Generative AI is also gaining traction, primarily as a productivity tool in risk analytics. 84% of lenders believe GenAI can accelerate model development and deployment, while about 70% see its strongest advantage in regulatory documentation and validation cycles, traditionally time-intensive areas for risk and compliance teams.

However, adoption is not uniform and institutional barriers remain. 65% of non-adopters consider implementation cost too high relative to perceived benefit, while 44% said they do not fully understand ML’s potential value. Concerns over explainability and compliance persist, with more than half citing transparency issues and regulatory uncertainty. Legacy technology systems and siloed 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 shift,” said Manish Jain, Country Managing Director of Experian in India. “ML enables lenders to sharpen risk prediction, approve the right customers faster, and extend credit to millions who were previously excluded from the formal financial system.”

Mariana Pinheiro, CEO, Experian EMEA & APAC, said “ML is becoming core to financial system design rather than simply a risk-modelling tactic. Machine learning is unlocking access to financial services for millions who have historically been excluded. By leveraging alternative data and more advanced risk models, ML enables lenders to make fairer, more accurate decisions, especially for consumers with limited financial histories.”

The findings underscore a phase in which lending organisations see automation, risk precision, and inclusion converging. As credit markets grow more competitive, early adopters of ML and GenAI are expected to gain operational speed, mitigate losses, and widen their addressable customer base.