A new Experian study conducted by Forrester Consulting reveals that artificial intelligence (AI) and machine learning (ML) are transforming credit decisions in India. This technology helps financial institutions expand access to credit and improve portfolio quality. The findings, based on input from 109 senior credit risk decision makers in India, show that ML is becoming a strategic asset for growth in a rapidly digitizing lending environment.
ML drive financial inclusion and reduce bad debts: report
This study highlights the dual impact of ML adoption: enhancing financial inclusion while enhancing risk management.High approval rates: 93% of ML users focused on vehicle loans report high approval rates.Reducing bad debts: 90% of lenders using ML for credit cards are successful in reducing bad debts.New segments: 79% of ML adopters agree that the technology allows them to responsibly serve new customer segments, such as thin-file consumers and credit novices, who were often excluded by traditional scorecards.Improved profitability: 71% of respondents reported that ML improved overall profitability by improving risk prediction.
Automation and GenAI emerge as top benefits
Financial institutions are leveraging ML to accelerate digital processes, with efficiency and speed being the main benefits.Operational efficiency: Almost 68% of ML users cite improved risk prediction accuracy and operational efficiency as key benefits.Increased automation: 71% agree that ML can automate more credit decisions and reduce manual workload.Looking ahead: Looking ahead, 78% of respondents believe the majority of credit decisions will be fully automated within five years.The report also said that generative AI (GenAI) is emerging as a powerful productivity tool, with 84% of respondents believing that GenAI can significantly reduce the time and effort required to develop and implement new credit risk models. Meanwhile, 70% agree that the biggest benefit of GenAI is streamlining regulatory documentation, thereby accelerating validation cycles.
What are the barriers to AI and ML adoption?
Despite the proven benefits, the report found that many organizations remain cautious, leading to persistent barriers to widespread ML adoption, including cost concerns, lack of understanding, compliance, and infrastructure.
