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Financial institutions are being sold generative AI that will transform their work, and agents that will do the work for them. Some of it is useful. Some of it is hype. This primer is the short version of how to tell the difference, in one sitting.
Three chapters carry the spine. How the machinery works: next-token prediction, what training does and doesn't fix, the context window, and retrieval. Where it helps: deciding per feature, the uses that actually work in finance, prompts and prompt injection, and why a chatbot is a system, not a model. Agents, and the checks: the loop and its harness, least privilege, how agents fail across steps, the trajectory, evals, and guardrails - including why a rule you can talk a model out of is not a control.
Written by the person who led AI risk supervision at the Monetary Authority of Singapore and wrote Singapore's AI risk management guidelines for the financial sector. About twenty pages, deliberately boring.
This primer is the gateway to the full book, Agentic & Generative AI for Finance (listed here separately), which works through the machinery and the checks in depth - the training, the retrieval, the prompts, the agent loop, the tests, the runtime controls, and the records. If this primer was useful, pass it along.
Financial institutions are being sold generative AI that will transform their work, and agents that will do the work for them. Some of it is useful. Some of it is hype. This primer is the short version of how to tell the difference, in one sitting.
Three chapters carry the spine. How the machinery works: next-token prediction, what training does and doesn't fix, the context window, and retrieval. Where it helps: deciding per feature, the uses that actually work in finance, prompts and prompt injection, and why a chatbot is a system, not a model. Agents, and the checks: the loop and its harness, least privilege, how agents fail across steps, the trajectory, evals, and guardrails - including why a rule you can talk a model out of is not a control.
Written by the person who led AI risk supervision at the Monetary Authority of Singapore and wrote Singapore's AI risk management guidelines for the financial sector. About twenty pages, deliberately boring.
This primer is the gateway to the full book, Agentic & Generative AI for Finance (listed here separately), which works through the machinery and the checks in depth - the training, the retrieval, the prompts, the agent loop, the tests, the runtime controls, and the records. If this primer was useful, pass it along.