Examples of Agentic AI Use Cases in Financial Services: 10 Practical Workflows

Your financial services teams likely depend on multiple systems to get their work done. Chances are they regularly use platforms like ServiceNow, SAP, Workday, and Coupa to handle everything from approvals to compliance checks.

The problem is that whenever their workloads cross system boundaries, performance slows. Often, teams then spend more time chasing status than getting things done.

Agentic AI can help, not by replacing your systems, but by orchestrating actions across them. It’s estimated that AI agents can help save enterprises 30%–50% on operating costs by automating time-consuming tasks.

Below, we'll outline 10 real-world agentic AI workflows built for financial services. We’ll cover trigger points, systems touched, controls, audit artifacts, and KPIs — giving you a practical evaluation framework you can follow.

What agentic AI is (and why it’s different from generative AI)

Agentic AI uses automated agents that can plan and take action toward a goal across tools and data sources with human oversight. Generative AI (genAI) technology relies on user input to generate an output — text, images, audio, video, or code.

The key difference between these technologies lies in the self-driven nature of the actions they perform. GenAI tools can be useful for knowledge retrieval and basic content creation, but can only react to user input. AI agents are designed to autonomously plan and execute multi-step workflows across systems with minimal manual intervention.

For financial services IT leaders, this distinction changes things operationally. Agents can be set up to trigger approvals, maintain accurate logs, support identity checks, and execute across systems. This provides information-retrieval convenience and can also impact productivity levels across the board.

How agents plan, take actions across systems, and validate outcomes

Let's say an employee requests access to your Bloomberg Terminal. In theory, you could use traditional AI or rule-based automation to do this, assuming the process is consistent and repeatable. 

But what happens if the request needs to reference a cost center in SAP or verify a policy in Workday? This is typically where rules break down, and tool maintenance issues happen.

AI agents are designed to handle those subtasks. They can:

• Plan each step in sequence.
• Check necessary policies.
• Gather attributes.
• Open the approval.
• Provision access.
• Confirm the entitlement applied.
• Log the outcome.

Why financial services teams are adopting agentic workflows now

Many financial services leaders are well aware of their bottlenecks. These typically include:

• Identity and access queues that sit open for days
• Compliance inboxes nobody owns
• ERP handoffs requiring manual re-entry
• Context switching between different systems to complete one task

These aren't new problems, but their cost is becoming harder to justify. They tend to follow one of three predictable patterns:

• Access → approval → provisioning: Stalls at manager approval, then again at entitlement verification
• Invoice → match → approval → posting: Breaks when PO data doesn't align across Coupa and SAP
• Onboarding → KYC checks → account setup: Delays when Know Your Customer documentation is incomplete or misrouted

In regulated environments, governance requirements can sometimes constrain AI adoption. However, in many instances, they’re a driver. When audit trails, access controls, and exception routing are built into the workflow by design, agentic AI systems can help reduce compliance burden rather than add to it.

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Top 10 agentic AI use cases in financial services

1. IT and access request automation (bankers and analysts)

For bankers and analysts, waiting days for system access can directly impact productivity. This may manifest as extended IT support queues, manual policy checks, or fragmented approval chains. 

An agentic workflow can help reduce those delays by handling the provisioning sequence without requiring human intervention at every step.

Trigger: Employee requests access to a trading platform, research tool, or internal financial system

Systems touched: Identity provider, ServiceNow, and app-specific access tools

Agent actions:

• Retrieves employee role and entitlement policy
• Checks access permissions against segregation of duty (SoD) rules
• Routes to the correct approver based on role and system sensitivity
• Provisions access upon approval
• Confirms entitlement applied correctly

Control points:

• A manager approval gate validates the request before provisioning begins
• A policy compliance check confirms whether the request violates entitlement rules
• SoD verification minimizes the chance of access issues

Audit artifacts: Request timestamp, approver identity, policy check log, and provisioning confirmation

KPIs:

• Time-to-provision
• Approval cycle time
• IT ticket volume reduction

2. Compliance query resolution (policy and regulatory Q&A)

Compliance teams often handle a high volume of routine policy and regulatory questions. The challenge is that most answers to these questions already live in company documentation, but employees don't know where to find them.

An agentic workflow can help address this issue by surfacing company insights for compliance teams. Conversational AI assistants are able to search approved policies and provide helpful answers with citations and permission controls.

Trigger: Employee or compliance officer submits a policy question or needs to verify certain governance steps

Systems touched: Internal knowledge bases, policy repositories, and ServiceNow Knowledge

Agent actions:

• Identifies the query type and relevant policy domain
• Searches approved internal sources for a matching response
• Surfaces the answer with source citations attached
• Confirms the user has permission to access the referenced content
• Escalates unresolved queries to a qualified reviewer

Control points:

• Permissions check limits responses to sources the requester is authorized to access
• A citation requirement helps tie answers back to approved internal documentation
• Escalation gates route unresolved or ambiguous queries to a qualified reviewer

Audit artifacts: Query log, source citations, permission verification, and escalation record, if applicable

KPIs:

• Time-to-answer on internal policy requests
• Compliance inbox volume reduction
• SLA adherence for internal policy requests

3. Invoice and payment status lookup

Looking for invoice status isn't always easy, especially when working in multiple complex systems. Instead of requiring employees to open a support ticket or send an email to get an answer, agentic AI can help the business create a self-service support solution. This allows teams to quickly locate invoice statuses and payment confirmations while reducing inbound AP requests.

Trigger: Employee or vendor submits an invoice status or payment confirmation request

Systems touched: SAP, Oracle Fusion, and ERP/AP platforms

Agent actions:

• Locates the invoice or payment record across relevant ERP systems
• Pulls current status and payment confirmation details
• Identifies exceptions or mismatches and flags for review
• Routes exception cases to the appropriate AP team member

Control points:

• Role-based access check that limits invoice data visibility to authorized requesters only
• An exception flag that triggers manual intervention before disputed invoices process
• Routing rule that directs unresolved cases to the correct AP owner automatically

Audit artifacts: Inquiry log, invoice status retrieved, exception flags raised, and routing record

KPIs:

• Reduction in inbound AP inquiries
• Invoice cycle time
• Time-to-answer for payment status requests

4. Purchase order and requisition approvals

PO and PR approvals are a frequent source of bottlenecks. Requests can sit in queues for weeks, leaving teams to spend their time on manual follow-ups instead of higher-value tasks. Having an agentic workflow in place can help streamline PO and PR creation, approval routing, and status tracking directly within chat dialogues.

Micron, a global semiconductor and memory solutions company, used AI agents to power its procurement workflow. With the agents, they were able to handle 1,600+ calls per month, achieve 30% tool adoption, and save 2,000+ executive hours annually.

Trigger: Employee submits a purchase requisition or requests a PO status update

Systems touched: SAP Ariba, Coupa, and ServiceNow

Agent actions:

• Validates requisition details against procurement policy
• Determines the correct approval tier based on spend threshold and department
• Sends real-time status updates to the requester throughout the process
• Initiates PO creation upon approval

Control points:

• A spend threshold check routes requests automatically to the appropriate approval tier
• Policy validation steps confirm that requisitions align with procurement guidelines before routing begins
• An escalation path directs exceptions to a procurement manager for manual review

Audit artifacts: Requisition log, approval chain record, policy validation confirmation, and PO creation timestamp

KPIs:

• Approval cycle time
• PO and PR volume handled autonomously
• Executive hours reclaimed

5. Expense approvals, policies, and risk-based triage

Expense management can generate a disproportionate volume of back-and-forths in an enterprise. Employees might be unsure of policy limits, or finance teams might start fielding the same questions repeatedly. AI agents can help guide submissions, answer policy questions, and flag exceptions automatically.

Unilever leveraged its own AI agents and saw 17,000+ uses of its tool across expense approvals, vendor onboarding, and invoice lookups.

Trigger: Employee submits an expense report or requests clarification on expense policy

Systems touched: SAP Concur, Coupa, Navan, and Expensify

Agent actions:

• Retrieves applicable expense policy based on employee role and region
• Guides the employee through compliant submission
• Scores the expense report for policy exceptions or high-risk transactions
• Routes flagged items for human review before approval proceeds

Control points:

• Policy checks that can help confirm when submitted expenses fall within approved limits before routing
• Risk-based triage steps that flag outliers for human expertise before approval proceeds
• An exception routing rule that identifies non-compliant submissions to a finance reviewer rather than auto-approving

Audit artifacts: Submission log, policy check record, exception flags raised, and approval confirmation

KPIs:

• Expense policy compliance rate
• Reimbursement cycle time
• Reduction in finance support tickets

6. Accounts receivable and collections acceleration

Even though ERP systems contain relevant financial data, locating specific items like overdue payments or outstanding invoices can be time-consuming. Agentic workflows can help surface AR data while freeing collections teams to focus on outreach rather than data collection.

Trigger: AR team member requests invoice status, payment history, or overdue account details

Systems touched: ERP/AR platform and CRM

Agent actions:

• Pulls invoice status and payment history by customer
• Identifies overdue accounts based on configured thresholds
• Surfaces relevant account context for collections outreach preparation
• Holds billing adjustment requests for approver review before processing

Control points:

• Threshold limits that trigger automated overdue alerts
• Approval processes that hold billing adjustments for review before processing
• Escalation paths route higher-value disputes to a senior AR team member

Audit artifacts: Account lookup log, overdue flags raised, adjustment requests, and approval record

KPIs:

• Days sales outstanding (DSO) trend
• Time-to-prepare for collections outreach
• Dispute resolution cycle time

7. Payroll inquiries and finance answers

Payroll questions are among the highest-volume employee-driven questions to their finance teams. Employees can use agentic AI to get faster answers about their payslip details, year-to-date earnings, direct deposit changes, or company payroll policies without having to submit a ticket.

Trigger: Employee submits a payroll question or requests access to pay-related information

Systems touched: Workday, SAP, and HRIS platforms

Agent actions:

• Identifies the request type and retrieves the relevant payroll record
• Surfaces payslip details or YTD earnings based on employee identity
• Validates direct deposit change requests against security and policy requirements
• Routes policy questions to approved HR or finance documentation
• Escalates sensitive or complex requests to a human reviewer

Control points:

• Identity verification confirms that the requester can access only their own payroll data
• Direct deposit changes require a secondary authentication step before the AI agents continue
• Requests flagged as sensitive get held for human oversight rather than being auto-resolved

Audit artifacts: Request log, data accessed, authentication record, and escalation notes if applicable

KPIs:

• HR and finance case deflection rate
• Time-to-answer on payroll inquiries
• Employee satisfaction signals

8. HR and benefits support for financial services employees

Questions about benefits eligibility, leave balances, and onboarding tasks are easy enough to answer even without submitting an HR ticket. A conversational interface connected to an HRIS can often handle these requests without adding unnecessary work to HR teams.

Trigger: Employee submits an HR request related to onboarding, leave, benefits eligibility, or policy

Systems touched: Workday and HRIS platforms

Agent actions:

• Identifies the request type and pulls the relevant employee record
• Surfaces benefits eligibility, leave balances, or onboarding task status
• Routes change requests to the appropriate HR owner for review
• Answers policy questions from approved HR documentation

Control points:

• Role-based access limits what employee data the agent can surface per request
• Compensation or certain benefits-related inquiries require HR approval before processing

Audit artifacts: Request log, data accessed, routing record, and resolution status

KPIs:

• HR case deflection rate
• Resolution time
• Employee satisfaction signals

9. Claims processing acceleration (insurance operations)

Claims adjusters spend a significant portion of their day pulling information from multiple systems. AI agents can help locate policy details, underwriting guidelines, and prior claim histories.

Trigger: Adjuster or claims team member initiates a claim review or requests supporting documentation

Systems touched: Policy administration system, knowledge base, and case management platform

Agent actions:

• Pulls relevant policy details and coverage terms for the claim in question
• Retrieves prior claim history and flags any patterns worth reviewing
• Surfaces applicable underwriting guidelines based on the claim type
• Routes exception cases to the appropriate adjuster for manual review

Control points:

• Coverage determinations remain with a licensed adjuster instead of being automated
• Exception flags surface when claim details fall outside standard policy parameters
• Complex claims trigger an automatic escalation to a senior reviewer

Audit artifacts: Claim lookup log, documents retrieved, exception flags raised, and adjuster routing record

KPIs:

• Time-to-decision on claims review
• Average handle time per claim
• Rework rate on processed claims

10. Regulatory reporting support (evidence packaging and anomaly flagging)

Evidence gathering during regulatory reporting cycles can be time-consuming. Agentic workflows can help by pulling documentation and organizing evidence packages, so compliance teams can focus on their review processes.

Trigger: Compliance or finance team member initiates a reporting cycle or responds to an audit request

Systems touched: Data sources, documentation repositories, and reporting workflows

Agent actions:

• Identifies the reporting requirement and relevant data sources
• Pulls supporting documentation and organizes it into a structured evidence package
• Flags anomalies or gaps in the data for human review
• Surfaces prior reporting cycles for reference and comparison

Control points:

• Evidence packages don't move forward without a named compliance reviewer assigned to them
• Anomalies above a defined materiality threshold trigger a mandatory hold until resolved
• AI agents operate in a read-only capacity — locating data, but not modifying it

Audit artifacts: Evidence package log, anomalies flagged, reviewer sign-off record, and report submission timestamp

KPIs:

• Time to compile evidence packages
• Exception identification time
• Audit request turnaround time

Implementation blueprint: Integrate, measure, and scale safely

Before deploying your own agentic workflows, confirm the following:

• Data grounding: Confirm agents are pulling from accurate, governed data sources.
• Integrations: Connect relevant systems (Workday, SAP, ServiceNow, and Coupa) before go-live.
• Permissions: Configure role-based access so agents filter results based on what each user is authorized to see.
• Approval mapping: Define control points and escalation paths before the agent goes live.
• Logging: Capture every agent action for audit purposes from day one.
• Rollback: Establish a clear path to intervene or revert if something goes wrong.

When choosing KPIs, start with what you can measure consistently. Common options include ticket deflection, cycle time, and approval turnaround time. 

Keep in mind that agentic AI tends to perform best when deployed where people already work. Consider implementation with applications like Teams, Slack, or other web interfaces where underlying systems are already governed.

Data readiness, integration to systems of record, and KPIs

AI agents are only as reliable as the data they use. Be sure to establish approved data sources, permission-aware retrieval, and clear boundaries on what the agent can access to reduce the risk of hallucinations or outdated information from surfacing.

For integration, prioritize the systems already in your stack. Your workflows should route across them seamlessly, not around them.

Once your workflows are properly configured, track these metrics regularly to measure impact:

• Cycle time reduction
• Cost-to-serve
• Ticket and case volume
• Compliance throughput
• Exception rate

Activate your financial services workflows with agentic AI

While we've covered a wide range of agentic workflow use cases in financial services, it's important not to try to implement too many at once. A practical starting point is picking one or two where the problems are well-defined, the systems are already governed, and the metrics are easy to track.

When you're ready to build, Moveworks can support you, providing a conversational front door for employees across IT, finance, HR, and compliance. Teams can build and extend agents across systems without starting from scratch, even as you scale.

Companies like Micron and Unilever have already used Moveworks to drive measurable impact for their enterprise — from 2,000+ executive hours saved annually on procurement workflows to 17,000+ quarterly interactions across expense approvals, vendor onboarding, and invoice lookups.

What makes those outcomes possible is cross-system orchestration with governance-friendly control points built in from the start. This, combined with an AI Agent Marketplace offering prebuilt templates, can make Moveworks a strong fit for financial services teams ready to seamlessly move from evaluation to execution.

Ready to start modernizing your financial services workflows with agentic AI? Explore Moveworks for Financial Services today.