Traditional rule-based systems, once sufficient for detecting simple patterns of fraud, have been overwhelmed by the scale, ...

Fraud detection is defined by a structural imbalance that has long challenged data-driven systems. Fraudulent transactions typically account for a fraction of a percent of total transaction volume, ...

Overview: AI-powered fraud detection tools are rapidly being adopted by banks and fintechs to block scams and reduce losses.New platforms combine machine learni ...

Overview: AI in financial services uses machine learning and automation to analyze data in real time, improving speed, accuracy, and decision-making across bank ...

Digitalisation has rapidly changed the face of industry and business. Businesses have increasingly integrated modern technologies into their operations to improve real-time activity. However, ...

Explore how Sai Vamsi Kiran Gummadi is transforming financial systems for zero downtime and enhanced security through ...

Proactive monitoring tools, such as a third-party hotline platform and data analytics, coupled with employee engagement and a ...

Srinubabu Kilaru said Bringing version control and CI/CD into data pipelines changed how quickly we could respond to policy ...

Others leverage AI to monitor customer journeys, identify pain points, and provide seamless virtual assistance. These ...

Fraud detection is no longer enough to protect today’s financial ecosystem. As digital transactions increase, banks require systems that can assess risk with precision.