AL, ML Reducing Bad Debt In India, Building Efficient Credit Journeys: Experian

Artificial intelligence and machine learning are transforming credit decisioning across India, according to research by Experian conducted by Forrester Consulting. Based on inputs from 109 Indian senior decision makers in credit risk, the findings show that ML is helping financial institutions expand access to credit, improve portfolio performance, and accelerate digital decisioning. The study also highlights the challenges that continue to hinder wider adoption across the country.

As lending dynamics continue to evolve, ML is helping lenders to make faster and more accurate credit decisions while proactively managing risk and extending responsible lending to a broader set of customer segments.

ML As A driver Of Financial Inclusion And Sustainable Growth

The report shows that ML is enabling lenders to expand access to financial services for underserved segments, including thin-file and new-to-credit consumers. By leveraging richer data and advanced analytical techniques, ML models help make more accurate and inclusive assessments.

According to the research, 79% of ML adopters in India agree that the technology allows them to responsibly serve new customer segments that traditional scorecards often exclude. At the same time, 71% of respondents report that ML improves profitability by enhancing risk prediction and reducing bad debt. This dual impact, expanding access while strengthening portfolio quality, positions ML as a strategic asset for lenders seeking sustainable, long-term growth in a competitive and fast-evolving market.

Automation, Efficiency, And Cost Savings Are Top ML Benefits

Close to 68% of ML users cite improved risk prediction accuracy and operational efficiency as key benefits. These capabilities enable lenders to confidently increase automation, with 71% agreeing that ML allows them to automate more credit decisions, reducing manual workloads and speeding up time-to-decision.

Looking ahead, 78% of respondents believe that in five years’ time, most credit decisions will be fully automated.

Generative AI Is Emerging As A Powerful Productivity Tool In Credit Risk

Generative AI is emerging as a powerful productivity tool, particularly in traditionally time-consuming areas such as model documentation, reporting and business intelligence. Around 84% of respondents believe that gen AI can significantly reduce the time and effort required to develop and deploy new credit risk decisioning models.

More than two-thirds (70%) agree that gen AI’s biggest advantage lies in streamlining regulatory documentation, enabling faster validation cycles and improving collaboration between risk, analytics, and compliance teams.

Organisational Resistance To ML Adoption Persists

Despite the benefits, many organisations remain cautious. The report reveals that cost, market uncertainty and lack of internal expertise remain significant barriers to ML adoption. Two-thirds (65%) of non-adopters believe the cost of implementation outweighs the perceived benefits, while 44% admit they do not fully understand the value ML can bring.

Concerns around explainability and compliance also persist, with 54% of non-adopters worried about model transparency and 55% fearing regulatory misalignment. These challenges are compounded by legacy IT and data infrastructure, which 39% say is not equipped to support ML deployment.

Commenting on the insights, Manish Jain, country managing director of Experian in India, said, “India’s lending ecosystem is undergoing a fundamental transformation, and machine learning is at the heart of this shift. ML is not just improving acceptance rates and reducing bad debt; it is helping build more transparent, efficient, and inclusive credit journeys. Institutions that invest early in ML and gen AI will be positioned to compete, comply, and innovate.”