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Governing Generative AI Across Financial Institutions: A Framework for Generative AI Risk Control

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Listed:
  • Dennis Mao
  • Alessandra Lin
  • Yixin Kang
  • Yiqing Wang

Abstract

Generative artificial intelligence is moving from general-purpose experimentation toward specialized applications across banking, capital markets, insurance, payments, and wealth management. Its main contribution is not limited to conversational interfaces. Modern generative systems can synthesize large document collections, extract information from unstructured data, generate software and analytical code, create scenario narratives, support research workflows, and coordinate multi-step tasks. These capabilities make generative AI especially relevant to finance, where decisions often depend on combining quantitative data with contracts, policies,filings, news, customer communications, and expert judgment. This paper presents an application-oriented view of generative AI in finance. It organizes potential uses around five capability patterns, including knowledge synthesis, content generation, analytical assistance, interaction, and workflow orchestration, and maps them to major financia functions. Representative applications include investment research, customer service, lending support, fraud investigation, financial reporting, operations automation, software development, and personalized financial guidance. The paper also discusses common technical architectures, such as retrieval-augmented generation, tool-using assistants, multimodal models, and agentic workflows, and identifies practical factors that shape business value. The resulting landscape provides a foundation for researchers and practitioners seeking to understand where generative AI may produce the greatest operational and analytical impact in financial services

Suggested Citation

  • Dennis Mao & Alessandra Lin & Yixin Kang & Yiqing Wang, 2026. "Governing Generative AI Across Financial Institutions: A Framework for Generative AI Risk Control," Papers 2607.04103, arXiv.org, revised Jul 2026.
    • Handle: RePEc:arx:papers:2607.04103
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    References listed on IDEAS

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    1. David Kuo Chuen Lee & Chong Guan & Yinghui Yu & Qinxu Ding, 2024. "A Comprehensive Review of Generative AI in Finance," FinTech, MDPI, vol. 3(3), pages 1-19, September.
    2. Muhammed Golec & Maha AlabdulJalil, 2025. "Interpretable LLMs for Credit Risk: A Systematic Review and Taxonomy," Papers 2506.04290, arXiv.org, revised Jun 2025.
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