SBI Cards and Payment Services Ltd. expects digitalisation, artificial intelligence and machine learning to play a bigger role in customer acquisition, risk management and fraud prevention as digital payments continue to reshape India’s financial ecosystem.
“Digitalisation remains central to customers’ evolution towards greater participation in the formal economy,” said Chairman Challa Sreenivasulu Setty in a letter to shareholders.
Asserting India’s payment habits are being transformed by UPI, real-time payments, smartphone-led access and app-based behaviour, he said these shifts have changed how customers discover, use and engage with financial products.
Digital transformation
For SBI Card, digital is a strategic catalyst. It improves acquisition, servicing, personalisation, risk management and customer protection.
Artificial intelligence and machine learning are becoming increasingly embedded in SBI Card’s technology-led operating model.
“These capabilities support more personalised mobile and in-app communication based on customer needs and behaviour. They also enable deeper insights into customer preferences,” he said.
AI-led risk management
According to him, AI-driven models are strengthening credit decisioning, underwriting and early warning mechanisms. This helps the company manage portfolio quality with greater precision.
AI and ML have also been strategically embedded across critical functions, including servicing, fraud management and other business processes.
“These capabilities enhance threat detection, strengthen security, improve operational resilience and drive better business outcomes,” he said.
Cybersecurity focus
Cybersecurity remained central to strengthening trust across the company’s digital ecosystem. This was supported by continued investments in advanced security controls during the year.
Fraud prevention remained a critical area of focus as digital transaction volumes and customer interactions continued to expand.
Advanced fraud detection
The company enhanced its fraud risk management framework through stronger analytical capabilities, advanced models and AI-led monitoring.
“These measures enabled real-time fraud detection, dynamic limit management and more responsive intervention,” he said.