AI in Risk and Antifraud Domain
Discover how Transformer-based AI models are revolutionizing fraud prevention by extracting hidden patterns from payment histories. Madhumitha will reveal how these advanced models move beyond static rules to deliver real-time, intelligent fraud detection that reduces chargebacks and boosts revenue. Learn practical strategies for deploying state-of-the-art NLP architectures in payment workflows and turning your transaction data into a competitive advantage against evolving fraud tactics.
The rise of sophisticated fraud schemes demands equally advanced defenses. Transformer-based models—originally developed for natural language processing—are now proving to be a game-changer in payment fraud prevention. With the wealth of structured and unstructured data embedded in payment histories, these models can detect patterns and correlations that traditional systems miss, enabling proactive identification of fraudulent activity before it results in chargebacks or lost revenue.
This session will detail how to operationalize Transformer architectures for payment intelligence, combining transactional context, behavioral profiling, and historical chargeback data to build a continuously learning fraud defense. We’ll explore practical deployment strategies, model interpretability, and integration into real-time decision pipelines, ensuring that businesses maintain trust, minimize losses, and maximize revenue.
Attendees will learn how to:
Apply Transformer-based architectures to model sequential and contextual payment data.
Detect subtle fraud patterns and anomalies that evade rule-based systems.
Leverage historical chargeback data for risk scoring and proactive blocking.
Integrate real-time fraud detection into payment workflows with minimal latency.
Balance accuracy, scalability, and compliance in production deployments.
By the end of the talk, participants will be equipped with actionable insights to harness Transformers for next-generation fraud prevention.