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<p>Bringing technical innovations in managing financial risks has been a significant issue for managers in FinTech (financial technologies) organizations. Although FinTech organizations continuously explore new methods of financial risk management (FRM), specifically for achieving smooth governance, common issues exist with time-consuming and labor-intensive processes, and with inadequate computational support. Previous AI (artificial intelligence) driven approaches in FRM do not fully support critical computational provisions for regulatory compliance. To address the issues and with the growing volume of regulatory unstructured policy data, utilizing a design science research paradigm, we design a new innovative generative AI framework called <i>GenRL (Generative Reinforcement Learning) FinTech</i>, an innovative computational FRM model grounded in Reinforcement Learning (RL). The <i>GenRL FinTech</i> artifact is a prototype featuring multiple GenAI agents that autonomously acquire and refine domain-specific expertise in FinTech regulatory compliance. Our evaluation demonstrates that <i>GenRL FinTech</i> enhances the efficiency of compliance officers, particularly in terms of the accuracy of FRM decision-making.</p>

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GenRL FinTech: supporting the risk management process through reinforcement intelligence

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月27日

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  • Rafsun Sheikh,
  • Shah J Miah,
  • James Skinner,
  • Peter Cook

摘要

Bringing technical innovations in managing financial risks has been a significant issue for managers in FinTech (financial technologies) organizations. Although FinTech organizations continuously explore new methods of financial risk management (FRM), specifically for achieving smooth governance, common issues exist with time-consuming and labor-intensive processes, and with inadequate computational support. Previous AI (artificial intelligence) driven approaches in FRM do not fully support critical computational provisions for regulatory compliance. To address the issues and with the growing volume of regulatory unstructured policy data, utilizing a design science research paradigm, we design a new innovative generative AI framework called GenRL (Generative Reinforcement Learning) FinTech, an innovative computational FRM model grounded in Reinforcement Learning (RL). The GenRL FinTech artifact is a prototype featuring multiple GenAI agents that autonomously acquire and refine domain-specific expertise in FinTech regulatory compliance. Our evaluation demonstrates that GenRL FinTech enhances the efficiency of compliance officers, particularly in terms of the accuracy of FRM decision-making.