How One Technologist is Building Self-Healing AI Systems that Could Transform Financial Regulation

Photo courtesy of Pradipta Kishore Chakrabarty

Primary keyword: Pradipta Kishore Chakrabarty

Secondary keyword: regulation, self-healing AI, swarm intelligence, agentic AI, Compliance

Regulatory changes in the financial sector often improve oversight but place heavy burdens on those required to comply. These burdens include continuous updates to policies, adaptations in monitoring practices, and compliance with evolving requirements. Pradipta Kishore Chakrabarty, however, is pioneering AI systems that promise to transform this landscape—creating self-healing AI capable of autonomously adapting to regulatory changes with minimal human intervention. His vision has moved from concept to active development, offering a potential breakthrough in regulatory compliance.

The Challenges of Traditional Compliance

In conventional financial compliance, banks rely on large teams of analysts who scrutinize transactions, identify suspicious activities, and generate alerts. However, this process struggles under immense data volumes and inherent inefficiencies. For example, a major bank might process over 100 million transactions daily, with rule-based systems flagging between 2 and 5 percent of these for review. Due to sheer volume, human review is only partial, leading to "alert fatigue"—an overload of false positives that reduces analysts’ effectiveness and results in some threats going unnoticed.

This challenge is exacerbated by ever-increasing regulation from entities such as the Federal Financial Institutions Examination Council (FFIEC), GDPR authorities, and international sanctions bodies. Compliance teams must constantly update detection frameworks to reflect new guidelines, a time-consuming and error-prone process.

Chakrabarty explains that the techniques adopted in his published research papers leverage emerging agentic AI methodologies to propose novel architecture and techniques for solving complex business problems of cybersecurity and compliance in the regulated financial industry.

Agentic, Self-Healing AI: Moving Beyond Static Rules

Unlike traditional systems that use rigid "if-then" logic, Chakrabarty’s innovative approach incorporates emerging agentic AI methodologies. These AI frameworks act more like adaptive compliance officers who understand the wider context of financial activities. Agentic AI systems autonomously set goals, plan actions, and learn from outcomes—all without continuous human supervision.

Pradipta Kishore Chakrabarty’s research, well-cited and globally read—particularly his seminal papers on adversarial attack resilience and causal inference mechanisms which have accumulated over 300 reads on ResearchGate and substantial citations on Google Scholar—significantly validate the quality and impact of his work in the academic community. This broad recognition highlights his contributions to advancing AI-enabled financial compliance.

Chakrabarty states that his self-healing AI architecture ingests new regulatory data, such as updated sanctions lists, automatically tests its implications against transaction patterns, and adapts detection algorithms accordingly, aiming to reduce manual system updates and extensive testing cycles while maintaining comprehensive audit trails.

Achieving Transparency Alongside Autonomy

Chakrabarty’s work tackles a critical paradox in AI adoption for financial regulation: balancing autonomous operation with the oversight regulators demand. Chakrabarty's concept of 'transparent autonomy' aims to ensure AI decisions can be explained in plain language to compliance officers and regulators to maintain trust and accountability.

This requirement aligns with recent guidance from the Acting Comptroller of the Currency, Rodney E. Hood, who emphasized the necessity of explainable AI systems that deliver traceable and defensible outcomes in finance. Chakrabarty’s architectures generate detailed decision logs akin to analyst case notes, bridging the gap between automated detection and human understanding.

"Proficient in architecting solutions that meticulously adhere to regional regulations, domain-specific guidelines, and industry standards, this expertise ensures compliance with data residency, security protocols, and governance frameworks," Chakrabarty notes, describing the delicate balance between automation and accountability.

This transparency becomes crucial when considering recent regulatory guidance. Acting Comptroller of the Currency Rodney E. Hood has emphasized that AI systems in finance must remain explainable, with outcomes that are traceable and defensible. Chakrabarty's architectures address this requirement by maintaining detailed decision logs that read almost like case notes from an experienced analyst.

Integrating AI with Established Systems

At a time when financial regulation faces disruption by new payment mechanisms, cryptocurrencies, and algorithmic trading, Chakrabarty’s research advocates for a future where AI systems do more than just react—they anticipate complex regulatory requirements.

According to Chakrabarty, his agentic AI frameworks analyze enforcement trends and emerging risk indicators to propose balanced policy responses that safeguard innovation and consumer protection. This represents a shift from the reactive "regulator creates rules, banks comply" model to a dynamic system of AI-enabled compliance intelligence.

Citation Impact and Global Research Influence

Chakrabarty notes that his published works, particularly on adversarial attack resilience and causal inference in AI, have been extensively read and cited globally—evidenced by their strong presence on premier research platforms like ResearchGate and Google Scholar. These metrics not only affirm the academic rigor of his contributions but also highlight their influence on the worldwide AI and financial regulation research communities.

Moreover, his role extends beyond authorship. As a peer reviewer for renowned international technology journals and conferences, including multiple IEEE-sponsored events, he actively shapes future research directions. By evaluating emerging research on novel AI technologies and frameworks, Chakrabarty fosters transparency, collaboration, and innovation within the global community of technology builders without specific mention of individual venues, thus safeguarding impartiality and avoiding conflicts of interest.

Progress and Industry Adoption Hurdles

Chakrabarty claims that his self-healing AI systems have shown promising results, including a reduction in false positive alerts by over 60% and improved detection of genuine threats—the path to widespread adoption remains cautious. Financial institutions and regulators are still navigating how to govern autonomous AI systems effectively. The sector’s risk-averse nature and the complexity of regulatory compliance demand that transparency, accountability, and human oversight remain integral.

Broad Implications Beyond Finance

The challenges Chakrabarty addresses within financial regulation serve as a model for other regulated industries facing similar issues with AI adoption. Autonomous systems inevitably raise questions about explainability, governance, and human roles. His research offers a blueprint for developing AI that balances autonomy with accountability, ensuring that technology innovation complements regulatory integrity.

As financial crimes grow increasingly sophisticated, these advanced AI systems may prove essential to preserving global financial system stability, spurring innovation while upholding compliance and consumer protection.