Presentation by Shamindra Peiris, Visa
Wednesday, August 5, 2026 at 11:20 AM to 11:40 AM · 20 min. (US/Pacific)
Palazzo Ballroom L
Solo Talk
Product Management
Information
This talk will discuss how to leverage generative AI techniques to enhance fraud detection models.
Main challenge faced by Fraud Detection models is the class imbalance where legitimate transactions vastly outnumber fraudulent ones. Creating synthetic data will accommodate robust testing and modeling while minimizing Data Privacy concerns.
Secondly, this talk will discuss how to incorporate generative AI into the machine learning strategy to introduce novel fraud detection methods and improve predictive accuracy.
Integrating generative AI into ML strategy will shift the focus from static, rule-based detection to generating behavioral intelligence. Unlike traditional systems, generative models learn and adapt in real-time, evolving to recognize new fraud types as they emerge without requiring manual model updates.
Further, it will contribute in enhancing accuracy of the model while limiting the False Positives. At the same time, Generative AI techniques can be used to analyze unstructured data such as texts, notes etc rather than limiting to structured data points.
Lastly, this talk will discuss how to utilize generative AI to simulate potential fraud scenarios, aiding in the development and refinement of fraud prevention solutions.
Main challenge faced by Fraud Detection models is the class imbalance where legitimate transactions vastly outnumber fraudulent ones. Creating synthetic data will accommodate robust testing and modeling while minimizing Data Privacy concerns.
Secondly, this talk will discuss how to incorporate generative AI into the machine learning strategy to introduce novel fraud detection methods and improve predictive accuracy.
Integrating generative AI into ML strategy will shift the focus from static, rule-based detection to generating behavioral intelligence. Unlike traditional systems, generative models learn and adapt in real-time, evolving to recognize new fraud types as they emerge without requiring manual model updates.
Further, it will contribute in enhancing accuracy of the model while limiting the False Positives. At the same time, Generative AI techniques can be used to analyze unstructured data such as texts, notes etc rather than limiting to structured data points.
Lastly, this talk will discuss how to utilize generative AI to simulate potential fraud scenarios, aiding in the development and refinement of fraud prevention solutions.
Job Function Tracks
Product Management
