Axis Finance has launched its new ABC Scorecards, a data-driven credit evaluation system built in partnership with TransUnion CIBIL, aiming to significantly enhance the precision, speed and reliability of credit risk management across lending operations. The scorecards leverage CIBIL’s advanced analytics, behavioural insights and bureau data to help Axis Finance make faster and more accurate borrower assessments at scale.
According to the company, the ABC Scorecards integrate multiple layers of credit information, including repayment history, vintage, utilisation patterns and probability-of-default indicators. By combining these insights with internal risk parameters, the tool provides a more holistic and predictive view of borrower performance. Executives noted that the system reduces manual underwriting workloads and minimises subjectivity in decision-making, enabling more consistent credit outcomes.
The initiative aligns with the growing industry push toward algorithm-driven lending models, particularly as financial institutions face rising default risks and must manage portfolios with greater prudence. Axis Finance stated that the scorecards will help strengthen portfolio quality, improve early-warning detection and support more granular customer segmentation. This, in turn, is expected to optimise risk-based pricing and expand lending opportunities for credit-worthy customers.
TransUnion CIBIL highlighted that the collaboration underscores the importance of analytics-enabled credit frameworks in India’s evolving financial landscape. With lenders increasingly relying on AI and data intelligence, tools such as the ABC Scorecards can accelerate onboarding, expand credit access and reduce non-performing asset pressures.
Overall, the launch reflects a broader shift in the lending ecosystem toward sophisticated risk analytics and automation. As customer behaviour and macro-economic conditions continue to shift, institutions adopting such predictive technologies are likely to gain a competitive edge in portfolio resilience and growth.
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