How Agentic AI Helps with Financial Services
As financial institution tech teams consider just how agentic AI can be helpful to them, the Gartner report breaks down potential applications into four workflows:
- High-volume interactions, transactions and context
- Mature data, metadata, APIs, workflows and process orchestration
- Established authorizations, privacy, trust and security controls
- Experienced users of large language models and generative AI tools
One example, according to Patel, is in anti-money laundering investigations, where traditionally analysts must spend large amounts of time manually pulling extensive data from financial systems, including transactions, customer relationship management systems, sanctions lists, and exposed persons databases. Agentic AI does that now in seconds, generating reports for analysts to approve. “That shifts banks from looking backward to actively defending against financial crime.”
Patel adds, “AI agents are already helping financial institutions make faster decisions, manage risk in real time and deliver better customer experiences, whether that’s automating regulatory reporting at global banks or speeding up underwriting and claims at insurers.”
Overall, the rise in AI allows institutions to move from fragmented, siloed systems and processes to more unified data governance frameworks, which should ultimately be more secure, not less, than previous data environments.
READ MORE: What is artificial intelligence’s role in financial compliance?
Exploring Emerging Trends in Finance
Banks are adopting solutions such as Snowflake’s AI Data Cloud, which gives financial institutions one governed platform to unify their structured and unstructured data, run AI workloads, and securely collaborate with partners and third-party data providers, all without moving data, Patel says. He notes that these integrations break down into three converging trends.
“First, security is becoming data-centric. The perimeter is no longer the network, it’s the data itself,” he says. “Governance, access controls and policies now travel with the data, regardless of where it moves or which agent accesses it, so you can deploy AI across your enterprise without compromising the integrity of your most sensitive information.”
Next, he shares that zero trust is evolving beyond users to include AI agents, including every interaction, such as queries or tool calls, and every decision. Each one is verified, scoped and auditable, which means agents can be “traced, challenged and explained to a regulator” — a must-have for an AI-forward institution.
Finally, Patel says, unified governance frameworks are increasing in importance. Fragmentation is becoming “untenable,” a traditionally common issue as teams, tools and regions are siloed. “The trend is toward a single framework spanning structured and unstructured data, first- and third-party sources, across every cloud and jurisdiction with consistent policies, lineage and auditability,” he says.
A typical mortgage journey is a classic example: “AI agents can analyze applications, financial documents, property data and risk policies in real time, reducing underwriting from days to minutes. But that only works if governance, access controls, lineage and auditability travel with the data itself and enable banks to scale AI safely across the enterprise,” Patel says.
