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What You'll Learn
Key takeaways
- Agentic AI pursues goals — not prompts — with autonomy, adaptability, and coordination across systems.
- Lending, compliance, and advisory work are where results are already visible in financial services.
- Most programs stall not on ambition but on data: agents make confident decisions on bad information.
- A composable stack — governance, unified data, and integration — is what separates pilots from production.
There’s no denying that the finance industry runs on decisions made under pressure. Approve or decline, flag or clear, advise or escalate, often against a regulatory clock. For several years, the work that mattered most in this industry, like underwriting, servicing calls, advisor conversations, never fit cleanly into a click-through interface. A form can capture data. It can’t exercise judgment across three systems in real time.
Agentic AI is built for that gap. It plans, acts, and adjusts across multi-step workflows on its own, of course within limits an institution defines. Read on.
What agentic AI means in financial services
Agentic AI refers to systems that pursue a goal rather than answer a prompt.
Three capabilities set it apart from chatbots and copilots:
Autonomy
Acting on a task end-to-end without a human triggering each step
Adaptability
Adjusting its approach as new data, exceptions, or edge cases appear
Coordination
Working across APIs, systems of record, and other agents to complete a workflow
The practical difference shows up fastest in onboarding and KYC. What used to be a ten-step click-through form is becoming a governed conversation. An agent can now ingest identity documents, trigger AML screening, and apply risk rules, resolving what it can resolve on its own and escalating only the genuinely ambiguous cases to a compliance officer.
The interface has changed from a form to a conversation. But what didn’t change, and can’t change, is that every step still needs to trace back to a system of record with a clear audit trail.
A faster front end on an ungoverned process isn’t progress in a regulated industry. It’s a new kind of risk.
Where agentic AI is already delivering results: Key benefits
Where agentic AI is already delivering results: Key benefits
Lending is where agentic AI’s gains are most visible. Simple. Because the workflow is naturally sequential. Application, verification, decisioning, and then funding. Agentic credit decisioning lets agents evaluate creditworthiness against a wider set of data points, automate approvals within policy, and route only genuine exceptions to a human underwriter. This turns a process that used to take days into one measured in minutes.
Compliance, fraud, & risk monitoring
Agentic systems continuously reconcile transaction activity against AML and KYC rules. They flag irregularities as they occur, and auto-generate the audit trail a regulator will eventually ask for. This matters more than it sounds. An institution’s exposure isn’t just fraud it misses; it’s fraud it can’t later prove it monitored for correctly.
Advisory & wealth management
Agentic AI is also reshaping work that was never about data entry in the first place. New advisor-facing tools now prepare meeting context automatically, capture and summarize client conversations, and surface at-risk client signals. They run on a human-in-the-loop model where the advisor stays the final decision-maker. The effect isn’t replacing the advisory relationship. It’s clearing everything that used to compete with it for the advisor’s time.
Key statistics explaining the agentic shift in finance
Financial services is not experimenting at the edges. Salesforce’s 2026 Agentic Enterprise Index found the industry now generates roughly 10% of all agent activity on its platform, comparable to the volume typical of high-transaction consumer sectors, despite operating under some of the heaviest compliance requirements of any industry.
McKinsey’s Global Banking Annual Review 2025 puts a number on where this is heading. AI could bring gross cost reductions of up to 70% in certain categories, with a net effect of a 15 to 20% decrease across banks’ aggregate cost base.
The same review describes an operating model in which a single employee eventually supervises 20 – 30 AI agents running complex, end-to-end workflows autonomously.
Why most agentic AI programs stall
Ambition is not the bottleneck. Forrester’s 2026 research on enterprise agentic AI found that roughly three-quarters of enterprise leaders report adopting the technology, yet only a small minority are running it in meaningful production beyond simple, chatbot-like use cases.
The technology has reached technical viability. Enterprise readiness needed to run agents safely at scale has not caught up to it yet.
That gap is a data problem wearing an AI costume. Key point to note here – An agent is only as trustworthy as the record it’s acting on.
If “customer,” “account,” or “risk tier” mean something different in the three systems an agent touches during a single workflow, the agent isn’t making a bad decision. It’s making a confident one on bad information.
GLBA, state privacy statutes, and fair-lending rules don’t care whether the actor was a person or an agent. And an institution has to be able to show, after the fact, exactly what data an agent used and why it acted the way it did.
The composable stack underneath a working agent
The institutions closing the pilot-to-production gap share a common architecture. Each layer is built by a specialist, working together.
A pattern is emerging across financial services deployments:
Salesforce — logic & governance
Data 360 & Snowflake — context
Data 360 and Snowflake supply context. Unified customer, account, and transaction data that agents query rather than infer. This way, “customer” and “account balance” resolve to the same meaning everywhere an agent looks.
Integration layer — reuse & scale
An integration layer lets new agentic use cases reuse existing governed connections across channels, instead of each new agent requiring its own bespoke pipeline.
Institutions that build all three layers well are the ones scaling past one or two successful agents to run agentic AI across the entire enterprise.
How LumenData builds the trusted data foundation for agentic success
An agent is only good if the data it acts on is good. Every institution moving from pilot to production runs into the same wall. Agents that execute a workflow flawlessly still produce the wrong outcome if the customer record they queried was incomplete, duplicated, or defined differently across systems.
Now that’s a data foundation problem, and it has to be solved before an agent can be trusted with a real decision. Here’s how we help finance enterprises:
LumenData builds that data foundation. We design the master data management and data quality frameworks, built on Informatica and Salesforce Data 360, that deduplicate and standardize customer, account, and transaction data. This way, every system and every agent is working from the same trusted source.
We build lineage and audit trails from the start. So when a regulator asks what data drove a given decision, the answer already exists instead of needing to be reconstructed.
And we build the MuleSoft-based integration layer that connects that trusted data to the platforms running the actual workflows, including Agentforce, in real time, so a new agentic use case can be governed and deployed in weeks, not rebuilt from scratch every time.
Ready to build the data foundation your agentic AI strategy needs?
Connect today.
About LumenData
LumenData is a leading provider of Enterprise Data Management, Cloud and Analytics solutions and helps businesses handle data silos, discover their potential, and prepare for end-to-end digital transformation. Founded in 2008, the company is headquartered in Santa Clara, California, with locations in India.
With 150+ Technical and Functional Consultants, LumenData forms strong client partnerships to drive high-quality outcomes. Their work across multiple industries and with prestigious clients like Versant Health, Boston Consulting Group, FDA, Department of Labor, Kroger, Nissan, Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, Weight Watchers, KAO, HealthEdge, Amylyx, Brinks, Clara Analytics, and Royal Caribbean Group, speaks to their capabilities.
For media inquiries, please contact: marketing@lumendata.com.
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