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AI Agents in Banking

Author

Listed:
  • Ken Huang

    (DistributedApps.ai)

  • Daniel Wu

    (Stanford)

  • Jyoti Ponnapalli

    (Frontier)

  • Grace Huang

    (PIMCO)

Abstract

This chapter explores the transformative impact of AI agents on the banking industry, detailing how their sophisticated, multilayered architecture is revolutionizing operations, customer engagement, and value creation. It examines the key drivers of AI agent adoption, including the exponential growth of data, the need for real-time decision-making, evolving customer expectations, regulatory compliance, cost optimization, and innovation in financial products and services. The chapter provides an in-depth analysis of how AI agents are being applied in critical areas such as credit risk assessment, fraud detection and prevention, customer service, personalized banking, risk management, trading and securities, payments, and regulatory compliance. It also introduces the concept of “digital workers” as the next frontier in banking AI, highlighting their capabilities and providing real-world examples of their implementation. Finally, it addresses the challenges and considerations associated with AI adoption in banking, including data privacy, ethical concerns, regulatory compliance, human–AI collaboration, and the need for explainable AI, while also outlining steps banks should take to prepare for an AI-driven future.

Suggested Citation

  • Ken Huang & Daniel Wu & Jyoti Ponnapalli & Grace Huang, 2025. "AI Agents in Banking," Progress in IS,, Springer.
    • Handle: RePEc:spr:prochp:978-3-031-90026-6_8
    DOI: 10.1007/978-3-031-90026-6_8
    as

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