The Good Tech Companies - Bharath Somu’s Plan on Harnessing Agentic AI to Combat Financial Fraud

Episode Date: June 17, 2025

This story was originally published on HackerNoon at: https://hackernoon.com/bharath-somus-plan-on-harnessing-agentic-ai-to-combat-financial-fraud. Bharath Somu unveils a privacy-preserving, agentic AI framework to combat financial fraud through real-time, collaborative, and adaptive threat detection. Check more stories related to cybersecurity at: https://hackernoon.com/c/cybersecurity. You can also check exclusive content about #agentic-ai-fraud-detection, #bharath-somu, #federated-learning-finance, #real-time-fraud-prevention, #financial-ai-security, #collaborative-fraud-analytics, #privacy-preserving-ai, #good-company, and more. This story was written by: @jonstojanjournalist. Learn more about this writer by checking @jonstojanjournalist's about page, and for more stories, please visit hackernoon.com. Bharath Somu proposes an AI-driven, multi-agent system to fight financial fraud using federated learning, behavioral analytics, and real-time collaboration. His privacy-centric model enhances threat detection across banks without compromising sensitive data—paving the way for scalable, secure, and ethical digital finance.

This audio is presented by Hacker Noon, where anyone can learn anything about any technology. Bharat Samu's plan on harnessing agentic AI to combat financial fraud, by John Stoyan Journalist. As the digital transformation of the global financial system accelerates, so too does the sophistication of cyber threats. Financial institutions now face a formidable challenge, safeguarding trillions of dollars flowing through increasingly complex and connected banking infrastructures. At the intersection of artificial intelligence, privacy-centric architecture, and advanced risk analytics stands Bharat Somu, a senior engineer and a leading force behind the next generation of fraud prevention systems in digital finance.

Bharat's recently published research, titled Agentic AI Enabled Fraud Prevention, Multi-Agent Collaboration Models for Real-Time Thread Detection and Response in Digital Banking, outlines an ambitious yet practical roadmap forcing AI and distributed intelligence to address modern fraud. As part of a broader academic and industrial mission, Barat's work integrates insights from multi-agent systems, federated learning and behavioral analytics to create fraud detection mechanisms that are not only accurate but privacy-respecting and adaptable. From traditional barriers to I-LED innovation historically, banks relied on rule-based systems

and supervised learning models trained on legacy datasets. While effective against known fraud patterns, these systems have struggled to detect novel or low-frequency attacks, especially those exploiting compromised credentials, synthetic identities, or social engineering. Barad argues that this approach, often centralized and reactive, is no longer adequate. Instead, his research proposes a transition to a decentralized model rooted in agentic AI, a paradigm where autonomous software agents collaborate, learn, and adapt in real-time across distributed environments. These agents are designed to share threat intelligence without exposing sensitive customer data, a feat What sets Bharat's model apart is its emphasis on cross-organizational synergy.

By allowing banks to jointly train fraud detection algorithms without sharing raw transaction data, the model fosters collaborative resilience while preserving institutional autonomy. AGENTIC Intelligence and Collaborative Defense The centerpiece of Bharat's framework is a multi-agent architecture designed to detect fraud across multiple dimensions, behavioral anomalies, device spoofing, and unusual transaction flows, by simulating real-time banking interactions. Each agent specializes in a particular task, monitoring transaction metadata, mapping user behavior trajectories, or validating device authenticity.

When agents detect suspicious patterns, they communicate asynchronously through an encrypted message bus, leveraging trust-weighted consensus algorithms to determine whether a transaction should be flagged for further review. The say-decision pathways are auditable, interpretable, and designed to support real-time response times in high-frequency environments. This model of autonomous agents forming a decentralized consensus aligns well with modern banking's needs. The architecture allows institutions to evolve from passive fraud detection to active fraud

anticipation, creating a system that grows more intelligent as it scales. Federated Learning and Privacy Aware Risk ANALYTICSA recurring theme in Bharat's work is the balance between effectiveness and ethics. Privacy concerns are particularly acute in the financial sector, where sensitive user information is both a target and a liability. To address this, the proposed system avoids traditional data centralization. Instead, each bank locally trains its fraud detection model using proprietary data. These models then share encrypted parameter updates,

not customer records, via a secure federated learning pipeline. As a result, banks can benefit from the collective intelligence of the ecosystem without risking privacy breaches. Further strengthening this foundation is a Bayesian vulnerability modeling approach that accounts for adversarial attempts to mislead the system. This probabilistic framework allows agents to adapt to new attack vectors, shifting the fraud detection model from a static rule set to a living, responsive network. Real-world relevance and future-ready designbaroth's work isn't merely academic. His role at American Express involves developing production-ready AI tools for transaction integrity and regulatory compliance.

From combating synthetic identity fraud to orchestrating resilient cloud-native infrastructures, his solutions bridge cutting-edge research with real-world application. In one of his notable deployments, Bharat's models enabled proactive fraud detection across a hospitality-focused digital banking platform, dynamically adapting to the unique behavior profiles of hotel partners and their corporate clients. This demonstrated the scalability of his approach across industries with complex transactional behaviors. Moreover, the model's integration with infrastructure as code practices and zero trust architecture principles ensures compatibility with modern DevOps and compliance protocols, enabling seamless

deployment within global financial ecosystems. Toward a smarter, safer financial FUTUREA's AI continues to redefine the landscape of digital trust and security, Bharath Samu's research stands out as a beacon of ethical innovation. His agentic, privacy-preserving approach is not just about smarter algorithms, it's about fundamentally rethinking how institutions collaborate, adapt, and build trust in an increasingly hostile digital landscape.

Rather than viewing fraud as an isolated technical problem, Barat situates it within a broader context of systemic resilience, regulatory intelligence, and adaptive ecosystems. In doing so, he offers a compelling blueprint for how financial institutions can thrive in the age of AI, not by working alone, but by working smarter together. In a world where financial fraud evolves by the second, resilience must be intelligent, distributed, and collaborative, Barat notes. With agentic AI, we're building that future, one interaction at a time. Thank you for listening to this Hacker Noon story, read by Artificial Intelligence.

Visit HackerNoon.com to read, write, learn and publish.