How AI detects expense fraud in corporate cards

AI detects expense fraud in corporate cards by reviewing every transaction in real time, while manual audits sample only a fraction of spend. Because manual reviews apply procedures to less than 100% of items by design, issues may not surface until months after the transaction. But continuous review of every swipe is now practical. That’s important, because organizations lose an estimated 5% of annual revenues to fraud, according to a report from the Association of Certified Fraud Examiners (ACFE).

Finance leaders willing to move first have an unusually open field. Just 17% of organizations use AI to combat payments fraud, according to a survey from the Association for Financial Professionals (AFP), even though 76% experienced attempted or actual payments fraud in 2025.

What corporate card fraud is and how AI detection changes it

Corporate card fraud is the misuse of company-issued cards or expense claims for unauthorized gain. It spans deliberate schemes — such as personal purchases filed as business costs, inflated amounts, duplicate claims across reporting periods, and fictitious expenses backed by fake receipts — along with honest mistakes, like an employee who forgets about a prior submission and files the same cab fare twice. AI can change detection by checking every transaction against behavioral baselines and policy rules as spending happens, with continuous review between periodic audits.

Teams need to recognize the recurring schemes before they can detect them, since fraudsters now use the same AI tools finance teams do to concoct schemes designed to dodge detection.

Types of expense fraud companies face

Personal spending is a common expense fraud scheme involving corporate cards, and it persists most easily when the person committing it also controls the approval workflow; in other words, reports detailing the fraud never reach anyone else. Detection needs to operate independently of whoever controls the approval workflow.

Individual schemes vary, but the underlying mechanics rarely do. Familiar tactics include:

• Personal purchases coded as business costs
• Duplicate claims filed across separate reporting periods
• Inflated amounts on otherwise legitimate receipts
• Threshold gaming, or repeatedly submitting just under the approval cap

These patterns can remain hidden when only a fraction of reports get audited. Layered on top of those familiar schemes is a newer one: AI-generated fake receipts.

From reactive audits to proactive prevention

Continuous AI-powered scoring runs against every transaction as it posts, so exceptions can surface the day they occur instead of during a quarterly sampling cycle. The reviewer clears a live queue while transaction details are still fresh. These AI-powered continuous auditing systems “ingest high-volume data streams, flag exceptions as they occur, and dynamically update risk assessments throughout the audit period,” according to The CPA Journal, shifting assurance from retrospective evaluations to near real-time monitoring.

But here’s the problem: Only 7% of anti-fraud professionals say their organizations are more than moderately prepared to detect or prevent AI-fueled fraud, according to the ACFE’s 2026 Anti-Fraud Technology Benchmarking Report. That preparedness gap shows up in practice as detection lag, since schemes keep running for the months it takes a sampling cycle to reach them.

How AI detects expense fraud: core technologies

Five techniques do most of this work: anomaly detection, supervised models, natural language processing, document forensics, and risk scoring. Each covers a different failure mode, including fabricated receipt images and spending patterns that raise suspicion only when looked at and compared against a longer window of submissions. Mature systems run them simultaneously, because each catches schemes the others miss.

Anomaly detection and pattern recognition

Unsupervised learning establishes a spending baseline for each employee and peer group, then flags deviations without needing pre-labeled fraud examples. And unsupervised techniques, including isolation forests and autoencoders, are now standard complements to supervised classifiers in fraud detection pipelines, according to a 2026 survey of machine learning approaches to financial fraud detection. An employee whose meals average $35 in their home city gets flagged when a $180 dinner appears, and an expense location that contradicts an approved itinerary surfaces as a geographic anomaly. That’s why it’s important to capture expense transaction data such as merchant, location, general ledger (GL) codes, cost centers, and attendees, like Navan does. The practice can give models the context to separate anomalies from normal spend.

Supervised learning and neural networks

Supervised models learn from confirmed fraud cases and score new submissions against those patterns, such as duplicate receipts with matching file properties or transactions split just below approval limits. Neural networks weigh many signals at once, which helps them catch coordinated schemes and threshold gaming. Accuracy tends to improve as the models see more cases. Recent research confirms that hybrid approaches combining supervised and unsupervised signals into composite risk scores further enhance adaptability against evolving fraud tactics.

Natural language processing for receipt analysis

Natural language processing reads the full text of a receipt along with the total, so a claim labeled “office supplies” that lists entertainment items gets caught at the line level. Additionally, systems built on large language models can spot prohibited purchases, such as alcohol or personal items, hidden in lengthy receipts that would pass a total-amount check. Navan’s Expense Agent reads every line item, applies the correct GL code based on company policy, and generates clear, compliant transaction descriptions.

Computer vision and metadata forensics

Document forensics identifies altered receipts through pixel inconsistencies and font changes. It can also identify reused images, while metadata analysis flags conflicts between a receipt’s time stamp or location and the corporate card feed. According to TechTimes reporting, once a receipt is photographed or screenshotted, AI models erase its metadata fingerprints, so detection also depends on behavioral patterns across multiple submissions.

Multi-data point analysis and risk scoring

Risk scoring combines transaction metadata, employee spending history, geography, receipt characteristics, and timing into a single fraud probability, so your audit team can work from a prioritized queue. Reviewers start with the highest-risk transactions and skip the spend the system has already cleared. Unified travel and expense (T&E) platforms have an advantage here because they can reconcile a claimed expense against the booking data behind it.

Traditional fraud detection vs. AI-powered systems

Coverage and timing separate the three detection approaches you can choose from today. Manual audits review a sample after reimbursement. Rule-based systems check every transaction against scenarios someone predicted, while AI systems review everything and learn new patterns as they emerge.

Coverage and timing are the two variables worth interrogating in a vendor demo. Rule-based tools sit in the middle: full coverage limited to tactics someone already wrote a rule for.

Agentic AI: the next step in fraud detection

AI agents in finance produce findings and act on them by clearing compliant spend and flagging genuine exceptions. They escalate ambiguous cases to your reviewers with the reasoning attached. These systems are typically designed to fold reviewer decisions back into scoring, which tends to reduce repeat false flags.

Finance leaders see the opportunity, though few have captured it. In fact, only 14% of global finance leaders have fully integrated AI agents, according to Deloitte’s Finance Trends 2026 survey, though respondents named expense management among the leading agentic AI opportunities.

Navan Cognition, an enterprise-grade agentic AI platform, is one working example of this model for enterprise finance teams. Its Audit Agent, built for AI expense fraud detection, reviews every transaction and surfaces only the spend that needs attention, including out-of-policy purchases hidden inside compliant-looking expenses. Your team can focus on receipts that need review. Accuracy matters here because false positives erode employee trust.

Real-time prevention at the point of swipe

Accurate triage still happens after the swipe. The strongest fraud control declines a purchase before funds move. Systems that issue or connect corporate cards evaluate each swipe against policy in real time. They auto-approve compliant spend and send borderline purchases for review, while out-of-policy transactions are declined. Merchant category code (MCC) restrictions extend these corporate card controls by rejecting entire categories, such as gambling or personal retail, at authorization.

Navan enforces policy through point-of-swipe controls such as merchant category restrictions and dynamic spending limits. Because Navan’s real-time expense capture records transaction data automatically as it comes in, card swipes enter the expense record without manual entry. This can help close the fraud window that manual expense reports create.

Operational benefits beyond fraud detection

The same data capture that powers fraud detection can shrink the routine work around it. A Forrester TEI study, commissioned by Navan and based on a composite organization, projected that organizations using Navan saved 40% of the time previously spent on expense auditing and realized $1.2 million in productivity gains from expense automation.

For your controllers, automatic GL coding and auto-populated expense reports can turn month-end close into a confirmation step, with transaction details available throughout the month. Your finance and accounting teams can also gain a continuous record for expense policy compliance, since every transaction carries its receipt and policy status. The record also includes approval history.

Implementation considerations

The workflows around AI fraud detection help determine whether or not it succeeds. Before deployment, document how flagged transactions route to your reviewers, and define who owns escalation for ambiguous cases. Also, define the evidence trail that accompanies each AI decision; auditors will ask for control design and testing documentation.

Explainable output matters for the same reason. Grant Thornton advises that organizations using AI in controls should maintain documentation that covers what happened, why, who approved it, and where the evidence lives. Your reviewers and your employees both need to see why a transaction was flagged. Unexplained flags read as accusations.

Integration determines how much signal the system actually sees. Direct feeds from your card program plus connections to enterprise resource planning (ERP) and accounting systems matter, because fraud evidence often lives in the gaps between tools, such as a receipt time stamp that conflicts with the card feed. Ask how your vendor handles cardholder and receipt data, including role-based access controls that keep reviewers from seeing spend outside their scope.

As AI solutions have matured, trust in them has increased, so it may be easier to convince the C-suite to make a change. The State of Corporate Travel and Expense 2026, a report from Skift and Navan, found that 76% of business travelers surveyed trust AI for straightforward T&E tasks, up from 59% two years before. Pairing the rollout with clear guidance on how to help prevent employee expense fraud helps your employees understand how the system protects company funds and supports compliant spending.

Taking control of corporate spend with AI

Here’s the bottom line: AI may be able to help your fraud program keep more money through full transaction coverage and point-of-swipe enforcement. Audit teams can focus on real exceptions and spend less time on paperwork. A system that clears the compliant majority automatically and puts the risky remainder in front of your people with the evidence attached can review every transaction without adding headcount.