Modernizing Legacy Risk Engines: Transitioning to Real-Time AI-Driven Decision Intelligence

Ajay Kumar Punia; Arun Chaudhary

doi:10.70589/JRTCSE.2025.6.1

Authors

Ajay Kumar Punia Citizens Bank, Phoenix, AZ, USA Author
Arun Chaudhary Credit One Bank, Las Vegas. Nevada, USA Author

DOI:

https://doi.org/10.70589/JRTCSE.2025.6.1

Keywords:

legacy systems, Risk Management, Artificial Intelligence, machine learning, financial services, real-time decision intelligence, digital transformation, regulatory technology

Abstract

The financial services industry stands at a critical juncture where decades-old legacy risk management systems, primarily built on rigid rules-based architectures and mainframe technology, increasingly fail to address the demands of real-time digital finance. This paper examines the imperative for modernizing legacy risk engines through the adoption of artificial intelligence and machine learning technologies. It analyzes the structural limitations of traditional systems—including data silos, operational inefficiencies, and detection gaps—that leave financial institutions vulnerable to sophisticated financial crime and unable to meet evolving customer expectations. Through a comprehensive literature review spanning 1989 to 2022, the paper synthesizes academic research and industry evidence on AI applications in risk management across seven domains. It proposes a structured modernization framework encompassing architectural transition strategies, data integration methodologies, and governance considerations. The paper presents comparative analysis of AI-native versus AI-enhanced approaches, supported by architectural diagrams and implementation tables. Findings indicate that successful modernization requires not merely technological substitution but fundamental redesign of risk operating models, with unified platforms, flexible data integration, and self-service capabilities enabling domain experts to directly implement risk strategies. The paper concludes with implications for financial institutions navigating the transition from legacy systems to real-time AI-driven decision intelligence.

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