Agentic AI in financial services now touches fraud, AML, onboarding, investment suitability, and servicing. But with 76% of firms planning to implement agentic AI within the next year, the hard part is not the models themselves; it’s ensuring that the data, controls, and integration patterns with existing systems are fit for purpose. This white paper focuses on how to move from isolated agent pilots to production systems that operate against real customer data, under real regulatory constraints, across complex technology estates.
Download the full paper for integration and deployment frameworks, and practical solutions for financial services-specific data and governance concerns. Below we provide a sneak peek of the core topics covered in the paper.
Why agentic AI risk rises in production
Once agents can call tools, write code, and move money, familiar risks compound: bad or incomplete data, opaque reasoning, over-privileged access, and brittle integrations with KYC, core banking, trading, and document systems. You’ll learn about the concrete risks of agentic AI in financial services, including:
- Agent sprawl;
- Unsynchronized versions;
- Silent failures in long-running workflows; and
- Limited ability to reconstruct decisions for regulators and auditors.
AI governance in financial services
AI governance in financial services is an engineering and an architecture problem, not just a policy topic. The paper explores controls such as agent lifecycle management, non-human identities with least-privilege access, policy-as-code guardrails, and immutable decision logs that preserve full decision trails over time.
Data, runtime, and integration foundations
On the data-centric AI side, the guide covers active metadata, observability, access control, and unification of structured and unstructured data so agents can consume trustworthy, contextual information at scale. Modern data foundations enable agents to make reliable decisions, accelerate workflows, reduce operational risk, and deliver value across functions from AML to customer service. For example, in wealth and asset management, an AI agent determining investment suitability uses risk tolerance questionnaires, liquidity needs, income stability, portfolio objectives, KYC information, and historical product performance. If metadata isn’t accurate or current, the agent could recommend a product that no longer matches a client’s profile. Active metadata and observability ensure every suitability input is fully tracked, updated, and auditable, so agents recommend products that align with regulations, internal policy, and client needs.
At the runtime and integration layer, the paper explains durable execution, sandboxed code execution, model context standards such as MCP, agent-to-agent protocols, and OAuth-style delegated access as enablers for real agentic AI applications in financial services and repeatable agentic AI use cases in financial services.
The outcome: New operating models
Financial institutions that build the right foundations on a robust multi-agent orchestration platform get autonomous regulatory workflows, self-correcting fraud and AML processes, continuous onboarding and suitability assessments, proactive compliance monitoring, high-velocity customer servicing, and automated operations across the front, middle, and back office.
Download the white paper for a concrete blueprint: how to instrument data, governance, security, and runtime so agentic systems in finance are auditable, recoverable, and fit for regulated production environments.
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