How AI Supports Fraud Detection in Expense Management

AI is giving finance teams a practical way to review every expense transaction as it happens, helping them catch suspicious spend earlier and tighten policy enforcement at scale. That kind of continuous visibility can help turn expense auditing from a periodic check into an always-on control layer.

As expense programs grow more complex, real-time review gives organizations a stronger way to maintain control, spot unusual activity earlier, and enforce policy with more consistency. Traditional controls still play an important role, but they work best when paired with systems that can continuously analyze transactions as they happen, including newer risks like fake receipts and split-transaction schemes.

This article covers how these systems work, what specific fraud patterns they identify, and what finance teams should consider when implementing them.

How Continuous Review Strengthens Expense Controls

Continuous review gives organizations broader coverage, faster visibility, and more consistent policy enforcement across expense activity. Traditional controls still matter, but AI-powered systems can help extend those controls beyond periodic audits.

Three shifts show why continuous review can strengthen your expense controls beyond what traditional reviews typically catch.

The Sampling Problem

Continuous review gives your finance team broader coverage because every transaction can be evaluated against policy and behavior patterns. Manual auditing typically covers only a small percentage of expense transactions, which means the vast majority go unexamined. Even at low fraud rates, small samples miss most violations by definition. The limitation is mathematical, not operational, and more headcount can’t fix it.

The benefit of broader review compounds with time. Industry data consistently shows that the longer fraud goes undetected, the larger the losses grow. Every month of delayed detection can deepen the financial damage, which is why shortening the detection window matters as much as improving detection accuracy.

Slow, Reactive Review Cycles

Real-time review gives your finance team a better chance to act while corrective action is still practical. Most expense audits happen during month-end close, meaning violations may sit undetected for weeks. The State of Corporate Travel and Expense 2026, a report from Skift and Navan, found that 29% of the travel and finance managers surveyed still rely on manual expense processing. That manual cycle can create a backlog where issues pile up and your team spends time chasing receipts rather than analyzing spending patterns.

Evolving Fraud Tactics

Adaptive detection gives organizations a stronger way to keep pace as fraud tactics become easier to produce and harder to spot manually. Generative AI tools have lowered the barrier to creating convincing fake receipts, and sophisticated schemes like threshold gaming or gradual policy drift exploit the predictability of traditional controls. That shift makes adaptive detection methods increasingly necessary if you want your controls to keep up.

How AI Detects What Manual Audits Miss

AI detects hidden fraud by analyzing every submission against behavioral patterns, transaction signals, and business context, not just fixed rules. Instead of reviewing a small sample after the fact, AI-enabled systems can continuously analyze every submission and flag deviations from expected patterns, including fraud types that rule-based checks weren’t designed to find.

In practice, the strongest systems do this with a unified travel and expense (T&E) data core rather than isolated expense records alone. Navan’s platform, for example, connects travel intent, booking data, card activity, receipt contents, and final spend through 130-plus data elements, powered by Navan Cognition, its enterprise-grade agentic AI.

That connected context helps AI distinguish a legitimate exception from a suspicious one.

These five techniques show how broader coverage helps you surface fraud that manual audits often miss.

1. Anomaly Detection Through Behavioral Baselines

Anomaly detection can spot suspicious spend by comparing each transaction against a learned baseline for the employee, department, and role. A hotel stay in an employee’s hometown on a weekend, an expense category inconsistent with job function, or a sudden change in submission timing can all trigger a flag without anyone needing to write a rule for that specific scenario.

Traditional rules can only catch known fraud patterns. Anomaly detection can identify novel behaviors the system has never seen before, which makes it useful against the adaptive tactics described above and gives you a better chance of catching new patterns early.

2. Natural Language Processing for Receipt Analysis

A receipt says “office supplies,” but the merchant is a clothing retailer. A dinner expense lists a restaurant that was closed on the date submitted. These are the kinds of inconsistencies that natural language processing (NLP) catches by reading text for context that simple field matching would miss. NLP parses receipts to spot mismatches such as merchant names that don’t appear in known business directories, vague item descriptions where specific line items should appear, or category labels that contradict the actual purchased items.

3. Computer Vision for Document Verification

Computer vision helps verify whether a receipt was altered, reused, or generated to look legitimate. Computer vision combined with receipt OCR can detect digitally altered receipt amounts, identify when the same receipt image has been submitted with minor modifications, and flag documents that appear manipulated. As generative AI makes fake receipts easier to produce, this capability moves from nice-to-have to essential. Navan’s Audit Agent automates compliance and fraud detection, reviewing every transaction to surface only the spend that needs attention, including out-of-policy purchases hidden within compliant-looking expenses.

4. Neural Networks for Coordinated Fraud

Some fraud schemes involve more than one person, and that’s what makes them hard to catch with single-transaction analysis. Neural networks address this issue by analyzing transaction amounts, merchant categories, timing, geographic location, and submission patterns together to detect coordinated schemes. They can be particularly effective for identifying colluding employees who split fraudulent transactions across multiple submissions, or managers who approve inflated claims from subordinates. Graph-based approaches within neural network architectures can also map relationships between employees, merchants, and approval chains to surface hidden connections, making them particularly effective at detecting collusion rings that appear normal when transactions are reviewed in isolation.

5. Predictive Risk Scoring

Not every expense submission carries the same level of risk. Predictive risk scoring assigns each one a likelihood of fraud or policy violation, then routes it accordingly: low-risk items auto-approve, mid-risk items go to managers, and high-risk items escalate to finance. That triage happens at the point of submission, not during a periodic audit, so your finance team focuses attention where it matters most. The fundamental advantage over rule-based systems is that machine learning models can adapt to new fraud tactics without manual updates, uncovering complex, nonlinear relationships that static rules typically can’t detect.

Real-Time Enforcement vs. Post-Hoc Review

Real-time enforcement gives organizations a chance to stop or route suspicious spend earlier, before it turns into a reconciliation problem. Catching a policy violation at the point of transaction, before corporate funds are spent, is fundamentally different from discovering it during month-end reconciliation. This timing shift can turn fraud detection from a cleanup exercise into a prevention mechanism.

Modern platforms enforce policy at three critical stages, and each one reduces the window for fraud to take hold.

At the Point of Booking

Pre-trip checks can stop out-of-policy spend before any expense is created. During travel booking, AI can run policy checks and surface compliance information before an employee commits to a purchase. If a selected flight or hotel exceeds policy limits, the system can flag the option and suggest compliant alternatives before a booking is confirmed. This pre-trip layer can catch violations before any expense is even created, which gives you a cleaner starting point for downstream review.

At the Point of Swipe

Point-of-swipe validation gives your finance team the strongest opportunity to control spend in real time. When an employee uses a corporate card, the transaction can be validated against policy rules in real time. Navan Expense uses a policy system at the point of swipe to auto-approve, flag for review, or decline transactions..

One participant in the Forrester Consulting Total Economic Impact™ study commissioned by Navan described the behavioral effect: “It’s not like the traditional approach where you would approve or reject. Instead, you just get continuous feedback to train the behavior of the employees.”

This approach can turn enforcement into behavioral guidance rather than punitive restriction, which helps explain why it may also improve adoption. It also gives you feedback closer to the moment of spend, when employees can still correct behavior quickly.

Through Intelligent Escalation

Intelligent escalation helps the right people review the right exceptions without slowing down compliant spend. After a transaction occurs, violations auto-route to the appropriate authority based on configurable rules, such as high-value charges to finance and mid-tier exceptions to managers, while compliant transactions go through without friction. This routing can help eliminate the manual policy interpretation that slows down traditional approval workflows. When Navan’s Audit Agent, for instance, reviews every transaction at the line-item level, escalation can be based on actual risk and context rather than blunt thresholds alone, which gives you more precise review queues and helps your reviewers spend time where human analysis is most needed.

What 100% Transaction Review Looks Like in Practice

Reviewing every transaction is practical when AI handles the data capture, reconciliation, and pattern detection work that manual audits can’t scale. Instead of auditing a sample and hoping it’s representative, your team can operate in an exception-based workflow, focusing only on the items AI has flagged as genuinely suspicious. Three capabilities make continuous review practical at scale for your team.

Automated Receipt Processing and Categorization

Automated receipt processing makes full review manageable by capturing and coding expense details without manual entry. AI-powered expense tools can read every line item on a receipt, apply the correct general ledger code, and generate compliant transaction descriptions automatically. This can help eliminate vague entries (like “lunch”) that obscure policy violations and create a detailed audit trail without manual data entry. Navan’s Expense Agent goes a step further, by linking each expense to calendar events, travel bookings, and project codes, giving your finance and accounting teams the business context they need to evaluate flagged items quickly.

According to a Forrester Consulting Total Economic Impact™ study commissioned by Navan, organizations using Navan can help save employees 24 minutes per expense report, while reducing audit and reconciliation time for finance and accounting teams by 40%.

Cross-System Reconciliation

Cross-system reconciliation strengthens 100% review by tying each charge to the business context behind it. Matching receipts to transactions is only part of the picture. Effective reconciliation connects each charge to the specific business trip, calendar meeting, and project code, not just the credit card statement. This cross-referencing can make it harder for duplicate submissions or mischaracterized expenses to slip through, because the system can verify whether the claimed business context actually exists.

This is where a unified T&E data core becomes especially important. Navan’s platform, for example, cross-references card transactions, travel bookings, and receipt data in a single system, so a duplicate submission or a charge with no matching trip gets flagged automatically rather than slipping through in a separate reconciliation queue. If your team wants fewer false positives, that cross-system visibility can make review more precise and give you more confidence in what gets escalated.

Subtle Pattern Detection

Subtle pattern detection makes continuous review more valuable by surfacing repeat behaviors that look harmless in isolation. Beyond obvious duplicates and inflated claims, AI can identify behavioral patterns across your expense data that manual review typically misses, such as:

• Threshold gaming: Consistently submitting expenses just under the approval limit to avoid review.
• Split transactions: Breaking a single large expense into smaller charges across multiple reports to stay below thresholds.
• Gradual policy drift: Small, repeated violations that individually look harmless but aggregate into significant leakage over time.
• Personal items mixed with business expenses: Out-of-policy purchases buried within otherwise compliant receipts.

These are the kinds of patterns that are easier for you to catch before reimbursement than after the fact, especially when your review happens continuously rather than through periodic sampling.

Implementation Considerations That Determine Success

Implementation success depends on getting data, integrations, and adoption right at the same time. AI fraud detection technology only delivers results if it’s implemented with the same rigor applied to the selection process. Data quality, system integration, and employee adoption all affect whether your organization captures the full value or stalls at the pilot stage.

These three implementation requirements help determine whether detection models perform reliably in day-to-day operations.

Data Quality as a Prerequisite

AI models learn what’s suspicious by studying what’s normal, which means the quality of your historical transaction data directly affects detection accuracy. Inconsistent formats, missing values, and siloed systems can all degrade that baseline. Standardizing data formatting across expense submission channels before deployment gives the model a cleaner foundation and helps avoid training it on incomplete or misleading patterns.

ERP and Accounting System Integration

ERP and accounting system integration helps close blind spots and connect detection to reconciliation. Your fraud detection system needs to connect with your existing ERP and accounting tools to provide complete visibility. Fragmented systems can create blind spots where duplicate submissions or cross-system fraud may hide. Navan, for example, offers direct integrations with NetSuite, QuickBooks, and Xero, helping close the loop between detection and reconciliation.

Change Management and Adoption

Adoption helps determine whether AI controls improve compliance in practice, not just on paper. Even the best fraud detection system underperforms if employees don’t use it. If you frame AI-powered controls as tools that eliminate friction, such as faster reimbursements and fewer rejected reports, employees are more likely to engage with them. Organizations that achieve the highest adoption rates invest as much in training and change management as in technology configuration. If you want consistent usage, your rollout plan matters as much as your model selection, because employees need to understand how the controls affect their daily spend.

From Reactive Cleanup to Proactive Control

If you want tighter control without adding more manual review, the priority is to move fraud detection closer to the moment of spend. When your team can review every transaction in real time, you can shorten the detection window, focus human attention on the exceptions that matter most, and make policy enforcement part of the spending process instead of a month-end correction step.

That shift also changes how finance teams spend their time. Instead of checking a small sample and hoping it represents the whole program, continuous review lets AI surface the exceptions that need judgment. Based on the relationship between detection speed and fraud losses, that timing shift is where organizations may see the biggest financial benefit.

The core decision comes down to timing: Stronger expense controls require visibility and enforcement before issues compound. The technology already exists to continuously review transactions, flag anomalies in real time, and route exceptions to the right people with context. The advantage goes to teams that put those capabilities into practice.