3 Working with generative AI in finance
This chapter covers
- Understanding the paradigm shift from model building to AI engineering
- Architecting a secure and compliant generative AI system for finance
- Making pragmatic model selections beyond generic benchmarks
- Mastering core techniques like RAG, PEFT, and guarded AI agents
- Developing deployment strategies for managing cost, risk, and governance
Finance has always gravitated toward data-driven innovation, yet the debut of generative AI marks a decisive turning point. Until recently, most machine learning in banks and asset management firms focused on structured data analytics, like credit scoring. Then came the proliferation of Large Language Models (LLMs)—tools capable of generating human-like text, writing code, or summarizing dense regulatory documents in seconds. This leap has triggered a gold rush in financial services, with institutions like Morgan Stanley embedding chat-driven experiences into their advisory platforms.
The impact goes far beyond automating back-office forms. These conversation-driven, generative tools inspire both excitement and caution, demanding a new AI lens—one that considers not only the technology but also the guardrails needed to align it with business strategy and regulation.