Self-Directed Intelligence in Finance: Agentic AI Models for Cross-Border Risk and Compliance Automation

Abstract

Intelligent Systems and Advanced Scientific Computing In finance—in fact, in other industries as well—huge amounts of data are collected and processed as a part of operations. With self-directed intelligence capabilities, these operations in finance can lead to a very low cost structure, especially in the case of risk and compliance checks. A cost structure that is much lower than the revenue generated by providing services will lead to very profitable financial institutions. Self-Directed Intelligence—Self-Directed Intelligence (SDI) is a type of intelligence that is independent of human intervention. In finance, SDI is used to detect anomalies or patterns in transactions. Rather than waiting for alerts from other systems, a self-directed intelligence system continuously ingests large amounts of transaction data and generates workflows, reporting or other operational tasks autonomously. SDI is a major pillar of Artificial Intelligence. Artificial Intelligence (AI) is the popular term today for intelligent systems with self-directed intelligence for specific use cases defined for self-directed systems. Self-Directed Systems (SDS) is another umbrella term covering both SDI and AI that includes systems with a lower level of autonomy supported by human experts at all times. Agentic AI focuses on the systems with the highest level of autonomy—agentic and self-directed—that operate independent of human intervention. For the applications described in this paper—detecting anomalies or patterns in transactions and generating workflows, reporting, or other operational tasks autonomously—AI with a low operating threshold is sufficient. Agentic AI is a layered stack that can operate at different levels of autonomy.