Every invoice line, every reconciled transaction, every reported exposure is a number someone had to arrive at and then be able to defend. Rule-based systems break on the variety of real documents; people do not scale to the volume. This book asks what an autonomous agent would need before that work could be handed to it.

Across ten studies it builds the parts. Retrieval that weighs numeric precision against regulatory coverage and temporal order instead of similarity alone. Memory that adapts when a data schema shifts underneath it. Task decomposition an SMT solver can certify against written compliance rules. Fraud detection that learns from transaction graphs without ever seeing a labelled fraud. And adversarial stress-testing that breaks the whole arrangement on purpose, before real capital does.

It is written for researchers, architects and finance technologists who are past being impressed by a chatbot that can read a 10-K, and are asking the harder question: how do you build a system a compliance officer, an auditor and a CFO will all put their name to?