Agentic AI in Financial Services

This document examines the impact of agentic artificial intelligence on the financial services sector. These systems are characterised by high autonomy, reasoning capabilities and the ability to plan and execute complex tasks without continuous human input. Unlike traditional or generative models, agentic AI systems can make decisions, interact with tools and data, and manage entire processes independently.

The paper outlines the core components of an AI agent, including the foundation model (typically a large language model), integrated tools, a reasoning and planning layer, and persistent memory. It also describes how these agents function within orchestrated systems, where principal, service and task agents collaborate to solve complex problems.

The report identifies three key areas of opportunity for financial institutions: customer engagement and personalisation, operational efficiency, and software development. Use cases include onboarding automation, fraud detection, regulatory compliance, and enhanced IT operations.

The document also explores specific risks, such as goal misalignment, autonomous decision-making without human validation, misuse of APIs, persona-based bias, outdated knowledge retention, explainability challenges, and potential agent collusion. For each risk, a set of targeted controls is proposed to ensure security, privacy and alignment with institutional values and regulatory standards.

In the regulatory landscape, the paper compares Australian and EU frameworks, underscoring the importance of compliance even when regulations do not yet specifically address autonomous agents. A preventive, ethics-based approach is recommended, including clear oversight, transparency and risk management practices.

Finally, it offers practical steps for safely integrating agentic AI, from identifying suitable use cases to establishing controls and monitoring frameworks that support scalable, compliant deployment.