Agent reliability profiles improve trust in financial AI systems
Agent Reliability Profiles in Financial Services
Computers and SocietyArtificial Intelligence
Summary
Sometimes AI helpers in finance do things they aren’t supposed to, which can cause problems. The authors suggest a way to describe and check how reliable these AI agents are, by creating a special profile for each one. These profiles help show that the AI works safely within its limits, making it easier for banks and regulators to trust and use them. They also describe steps to test and verify these profiles to ensure accuracy and fairness. This approach could speed up the safe use of AI in financial services.
What this means in practice
- •For financial technology teams: Produce standardized reliability profiles to validate deployed AI agents and demonstrate compliance with regulatory expectations.
- •For financial regulators: Use agent reliability profiles to assess and benchmark AI agents consistently across institutions under defined conditions.
Authors
Mike Hsu, Medha Bankhwal, Béatrice Moissinac, Kevin Werbach, Lukasz Szpruch, Bennett Hillenbrand
Abstract
AI agents can take actions. At times, those actions can go beyond what is intended. Agent reliability can be defined as assurance that an agent will stay within intended bounds and operate within limits. Today, there is no shared framework or language for describing, validating, and benchmarking the reliability of agentic deployments in financial services. This makes it difficult for financial institutions, vendors, and regulators to assess and trust agents at scale, thus limiting the pace of development and adoption. A standardized, shared representation of agent reliability would fill the gap. This paper introduces the Agent Reliability Profile, a per-agent unit of assurance evidence for agent deployments in financial services. Each Profile records a bounded, falsifiable claim, this agentic system reliably functions within its operating boundary. We define "operating boundary" as an agent having; (1) a defined autonomy tier, (2) a defined operational design domain, (3) defined classes of action, and (4) a defined control envelope. Production assurance progresses through three levels while the Profile schema remains constant: a Profile Builder compiles a Level 1 Asserted Profile from institutional evidence, a Profile Validator tests the deployment in its own environment to produce a Level 2 Validated Profile, and operation of the same tests by a qualified independent assessor produces a Level 3 Verified Profile. Separately a Benchmarked Profile reports results comparable across institutions under reference conditions. We describe the architecture, the artifact, the assurance ladder, the comparability flag, associated tools, an evaluation methodology, applications for financial institutions and supervisors, limitations, and a staged implementation program.