Financial Digital Twins: AI and Simulation-Based Risk Management for Banking Systems

Abstract

The increased volatility in the financial world, alongside the dynamics that characterize the global economy, have motivated the concept of Financial Digital Twins (FDTs) as AI-based, virtual models of the banking systems aimed to optimize risk management, fraud detection, and operational performance. With real-time data, operational research for risk assessment, and artificial intelligence systems, FDTs are useful in helping financial institutions anticipate risks, manage their decision systems, and design a better model for compliance with regulations. In the following paper, a literature review will look into using AI and ML to enhance the effective development of Financial Digital Twins. It discusses some modelling approaches that can be used in the industry, including Monte Carlo simulations, agent-based modelling, and the scenario approach, which help financial institutions model the market risk and credit performance alongside conditions in an economic downturn. Moreover, it reveals that AI can benefit credit scoring, fraud detection, and stress testing to increase the evaluation of risks. Still, when it comes to the drawbacks of the FDT, they are all associated with data protection, data processing, scalability, compliance issues, and AI model quality, respectively. This paper also covers new technologies like quantum computing, blockchain integration, and decentralized finance (DeFi), which have enhanced the existing dimensions of risk management in finance. Financial institutions must meet these challenges and adapt high technologies to advance banking systems to be financially sound, more informed, and more protective of banking systems