Agentic AI in Finance: Three Use Cases Delivering Value

At IIA, we advise enterprise data and analytics leaders to resist the urge to deploy agentic AI horizontally across the organization. In our view, the technology is not mature enough to function as a broad enterprise capability. The programs producing results concentrate on specific operational domains where workflows are stable, data structures are understood, and business outcomes can be measured clearly.  

Finance has proven to be a productive environment for early agentic applications. Financial operations run on defined processes that organizations have refined for decades. Invoices must reconcile with purchase orders, reports must reflect operational performance, and transactions must withstand scrutiny from audit and compliance teams. These constraints make finance an ideal proving ground for agent-based systems. When agents operate inside these controlled workflows, organizations gain efficiency and visibility without introducing unacceptable risk.

Many D&A leaders we serve are asking where they should begin to exploit agentic techniques in the finance space. In particular, teams experimenting with agents inside ERP environments want to understand where others are seeing measurable progress and what guardrails matter as these systems move closer to production. Across large enterprises, we’ve observed three agentic applications inside finance organizations begin to take hold. Each sits inside an existing workflow. Each delivers measurable operational improvement. And together they illustrate why vertical AI deployments outperform broad enterprise experimentation at this stage of the technology. 

Agentic AI Finance Solution #1: Accounts Payable

One of the clearest early applications of agentic AI in finance sits inside accounts payable. Most organizations still rely on the same core process: a vendor submits an invoice, finance checks the invoice against the purchase order, and the team confirms that the goods or services were delivered. If those three records align, the payment proceeds. When they do not, the process slows down as finance teams investigate discrepancies and contact vendors for clarification.

Agentic systems fit directly into this workflow because the rules already exist. An agent can monitor incoming invoices, retrieve the relevant purchase order from the ERP system, and verify delivery confirmation automatically. When a mismatch appears—an invoice for $1,500 against a $1,000 purchase order, for example—the agent initiates the first step of resolution by contacting the vendor and requesting clarification. Many discrepancies resolve quickly once the vendor responds, allowing the transaction to move forward without manual follow-up from the finance team.

Governance determines how far this automation can extend. In highly regulated environments, agents operate with read access and escalate the final decision to a finance professional who confirms the update before the system records the transaction. Other organizations allow the agent to complete more of the process once the reconciliation logic proves reliable. In both cases, finance teams spend less time chasing invoice discrepancies and more time focusing on the exceptions that require human judgment.

Agentic AI Finance Solution #2: Financial Reporting

The second agentic application gaining traction in finance sits inside financial reporting. Most finance organizations operate on a familiar cadence of monthly business reviews, quarterly performance updates, and executive reporting cycles. Historically, the majority of the effort in those cycles has gone into assembling the reports themselves, such as pulling numbers from systems, formatting spreadsheets, and preparing the material leadership will review. Only a small portion of the team’s time remains for interpreting what the numbers mean.

Agentic systems shift that balance. Agents can gather financial data from reporting systems, assemble the reporting packages, and surface the key changes automatically. What once required hours of manual preparation now happens as part of the workflow. The finance team no longer spends most of its time building the report. It spends its time analyzing it.

This is a significant shift because executives rarely struggle to read the numbers. If a region shows revenue declining by ten percent, everyone in the room can see it. The real question leadership asks is why. Finance teams add the most value when they explain the drivers behind performance changes—whether an operational shift, a new digital channel, or a change in customer behavior influenced the results. Agentic reporting systems create the space for that work by reducing the mechanical effort required to produce the reports in the first place.

Agentic AI Finance Solution #3: Fraud Detection  

The third agentic application generating significant impact inside finance focuses on fraud detection. Finance organizations have long relied on periodic audits and sampling to identify irregular activity. That approach leaves gaps. Agents change the model by monitoring transactions continuously and scanning the full stream of financial activity rather than a small subset. This shifts fraud detection away from retrospective audits and toward continuous, real-time monitoring.

Internal fraud gives us a clear example. Many organizations experience a pattern sometimes referred to as “penny licking.” Fraudulent invoices appear just below internal approval thresholds—often in narrow ranges like $350 to $490—and are coded to routine service categories that rarely attract scrutiny. Individually these invoices look harmless. Over time they create meaningful financial leakage. Agentic systems can detect these patterns quickly by analyzing invoice ranges, vendor behavior, and cost center activity across the full transaction stream. Instead of reviewing a small sample of purchase orders, the system evaluates every invoice entering the organization and flags anomalies for investigation.

External fraud shows up in a slightly different way, but agents help here as well. Financial institutions and payment companies now combine transaction monitoring with outside reference data to identify suspicious merchant activity earlier in the lifecycle. Agents can compare merchant names, addresses, and vendor credentials against third-party registries while scanning transaction patterns for unusual behavior. Organizations also connect internal data sources like payroll records or employee address data to detect conflicts that signal potential fraud. When these signals align, the system flags the transaction or restricts the account before losses accumulate. For many finance organizations, this continuous monitoring model produces the largest financial return among current agentic applications.

Retrieval-Based Model Design Key Considerations

As organizations deploy more agentic applications inside finance, governance becomes the next challenge. One practical technique many teams are adopting is retrieval-based model design. Instead of giving an AI system broad access to enterprise data, organizations restrict the information the model can use to make decisions. The model retrieves only the documents and records relevant to the task it is performing.

In practice, this means feeding the system a narrow set of trusted inputs. A finance agent evaluating invoices might receive access to company finance policies, purchase orders, and the invoice itself—nothing more. By limiting the information available to the model, organizations reduce the risk of incorrect inferences and prevent the system from generating responses outside the boundaries of the workflow. The system stays reliable while still operating within the control standards finance teams expect.

Many organizations extend this model further by combining internal financial records with verified external reference data. Merchant addresses, phone numbers, and business registry data can all help confirm whether a vendor or transaction appears legitimate. When the agent compares internal transaction activity with these external signals, it becomes far easier to detect suspicious patterns before losses accumulate. For data and analytics leaders, agentic systems perform best when they operate within tightly controlled data environments rather than broad, unrestricted access to enterprise information.

Measures of Success and Doing More with Less

As organizations move from pilots to production, we’re seeing the most effective teams evaluate agentic projects through a simple lens: Does the system save time, or does it improve margin? Time savings always matter, but margin improvement usually proves easier to explain to leadership and easier to sustain once the program scales. Leaders who anchor their AI efforts to clear economic outcomes build far stronger internal support than teams that position these initiatives as technology experiments.

Also, we’re seeing many agentic projects flame out because the culture of the company resists operational change. Teams hesitate to adjust workflows or question long-standing processes. The programs that move forward have a visible internal champion and realistic expectations about timing. Agentic systems require tuning, governance adjustments, and operational learning. Most organizations need several months to stabilize a deployment and prove the value before expanding it. The D&A leaders having most success here resist boiling the ocean. They choose a single region, business unit, or workflow, demonstrate the outcome, and then expand gradually once the organization sees the results.

Related, many of our clients are under pressure to do more with less. Across large enterprises, finance organizations continue to face headcount constraints while maintaining the same level of operations. In that environment, agentic systems gain traction where they address capacity problems. Procurement finance provides a strong example. Many organizations discover they have accumulated dozens of vendors providing similar services across different business units. Agentic systems can analyze procurement contracts alongside ERP transaction data to identify duplication, compare contract terms, and reveal opportunities to consolidate suppliers. In several cases, organizations have reduced large vendor pools to a smaller group of strategic partners once the data made the fragmentation visible.

Finance teams also see clear gains in invoice intake and processing. In many companies, staff members still receive invoices, enter them manually into financial systems, and reconcile them during standard working hours. Agentic systems change that operating model. Agents process invoices continuously, validate the information against ERP records, and flag only the exceptions that require human review. When the finance team returns the next day, they focus on resolving discrepancies rather than processing the full backlog of transactions. In our advisory work, organizations have reported 50 to 70 percent improvement in throughput once the workflow stabilizes.  

Across these examples, the same pattern keeps showing up. Agentic AI produces the strongest results when it attaches to specific operational workflows with measurable economic impact. Finance provides many of those opportunities because the processes are structured and the outcomes are visible in operational efficiency, margin protection, and financial control. Organizations that start small, prove the value, and expand deliberately will move far faster than those attempting to deploy agentic AI across the entire enterprise at once.