Biography
Bidhan Biswas is a Senior AI and Software Engineer specializing in artificial intelligence, agentic AI, financial transaction security, fraud detection, and large-scale enterprise systems. With more than a decade of experience in software engineering and emerging technologies, his work focuses on developing intelligent, scalable, and secure systems for complex financial and retail environments.
Throughout his career, Bidhan has contributed to mission-critical technology initiatives at major organizations, including Walmart and American Express, where his work has involved AI-driven systems, fraud and risk management, secure transaction processing, distributed architectures, and enterprise software modernization.
His current research focuses on applying Agentic AI, machine learning, intelligent software agents, and advanced analytics to financial fraud detection, investigation, and risk mitigation. His research interests include real-time payment security, autonomous fraud-detection systems, AI guardrails, financial cybersecurity, and trustworthy AI.
Bidhan has authored multiple scholarly publications in artificial intelligence and financial technology and continues to conduct research on next-generation AI approaches for securing large-scale financial transaction ecosystems. He also holds professional certifications in cloud computing and artificial intelligence technologies.
He holds a Master of Science in Computer Science from The University of Texas at Tyler and is pursuing a PhD in Artificial Intelligence and Information Technology at the University of the Cumberlands.
Through his research, professional engineering work, and technical contributions, Bidhan seeks to advance secure and responsible AI technologies capable of strengthening financial systems and protecting consumers and organizations from increasingly sophisticated digital fraud and cyber-enabled threats.
Abstract
Title: Agentic AI for Secure Payment Ecosystems: Building Intelligent Systems for Real-Time Fraud Detection and Risk Mitigation
Abstract: The rapid growth of digital payments has transformed the global financial ecosystem while simultaneously creating new opportunities for sophisticated financial fraud, account takeover, identity-based attacks, and cyber-enabled scams. Traditional rule-based and conventional fraud-detection systems, while essential, can face challenges in adapting to rapidly evolving attack patterns and analyzing complex transactional behavior in real time.
This keynote explores how Agentic Artificial Intelligence (Agentic AI) can contribute to the development of more adaptive, intelligent, and secure payment ecosystems. It presents a conceptual architecture in which specialized AI agents collaborate across key stages of payment processing, including transaction monitoring, behavioral and anomaly analysis, identity and authentication risk assessment, fraud investigation, risk scoring, and decision support. By combining AI agents with machine learning, pattern recognition, real-time analytics, and scalable computing infrastructure, such systems can support faster identification and mitigation of emerging financial threats.
The presentation will also examine the security challenges associated with introducing autonomous AI capabilities into sensitive financial environments. Particular attention will be given to AI guardrails, explainability, human oversight, privacy, access control, auditability, adversarial resilience, and responsible AI governance.
Finally, the keynote will discuss a path toward scalable Agentic AI architectures capable of supporting financial institutions, payment processors, fintech platforms, retailers, and other organizations operating large-scale transaction systems. The objective is not to replace existing payment-security mechanisms or human expertise, but to demonstrate how intelligently orchestrated AI agents can augment them—enabling more adaptive fraud detection, efficient investigation, and resilient risk mitigation as financial threats continue to evolve.
