How AI Detects Expense Fraud in Corporate Cards

Corporate expense fraud happens. An employee submits a fabricated receipt, say, or inflates a hotel bill. Fraud can also be unintentional, when, perhaps, an employee forgets what they already submitted and files the same cab fare twice across different reports.

Either way, fraud can go undetected for months, because traditional audit methods that review only a small sample of transactions leave most of this activity hidden until significant damage accumulates. By that point, many organizations recover little or nothing.

AI-powered expense management platforms change that dynamic by analyzing 100% of transactions in real time. Instead of discovering fraud months after payment, these platforms enforce policy at the point of swipe and flag or decline non-compliant spending before corporate funds leave accounts. It all happens through specialized detection techniques that understand business context, recognize behavioral patterns across thousands of transactions, and continuously learn from new fraud tactics.

Five AI Techniques That Detect Corporate Expense Fraud

Modern AI fraud detection deploys multiple complementary machine learning techniques simultaneously, each addressing specific fraud patterns that manual auditing can’t identify at scale. These five approaches work in concert to provide thorough detection that adapts as tactics evolve.

1. Anomaly detection using unsupervised learning

Anomaly detection flags transactions that deviate significantly from each employee’s established spending patterns, without requiring pre-labeled fraud examples. The system learns that an employee typically submits meal expenses averaging $35 in their home city, so when a $180 expense for dinner appears, it gets automatically flagged. Or the system could surface a geographic anomaly, which happens when expense locations contradict approved travel itineraries.

This approach is particularly valuable in corporate environments where labeled fraud data is scarce. Navan’s solution can help by applying anomaly detection across more than 130 data points per expense transaction, flagging deviations from established patterns without requiring your finance team to define specific fraud rules.

2. Supervised learning and neural networks

Supervised learning algorithms study past fraud cases and build models that flag similar patterns in new submissions. The system learns what fraudulent behavior looks like: duplicate receipts with matching file properties, vendors that appear repeatedly in confirmed fraud cases, or transactions split just below approval thresholds.

Neural networks take this further by analyzing multiple expense signals at once to catch coordinated fraud and new tactics that don’t resemble past cases. They become more accurate over time, without manual rule updates.

3. Natural language processing for receipt analysis

NLP catches fraud by reading receipt text and spotting things that don’t add up: a receipt labeled “office supplies” that lists entertainment items, a merchant name that doesn’t match any known business in the area, or vague descriptions where specific line items should appear. The technology also detects altered text by comparing receipt content against known vendor databases.

Navan’s Expense Agent, for instance, reads every line item on a receipt, applies the correct GL code based on company policy, and generates compliant transaction descriptions, capturing intent and context that generic OCR systems miss.

4. Multi-data point analysis with confidence scoring

Multi-data point analysis generates fraud risk scores by weighing multiple signals at once: transaction metadata, employee profiles, spending history, geographic data, receipt characteristics, and timing patterns. The result is a prioritized list. Rather than reviewing everything manually, your audit team focuses on the highest-risk transactions first.

The more data points a platform captures, the more accurate its scoring becomes. Navan’s expense management platform, for example, captures 142 unique data elements and connects travel intent with final spend, including merchant details, calendar meeting information, and real-time policy rules.

5. Predictive analytics and adaptive learning

Predictive analytics generates risk scores for upcoming business trips based on employee history, assigns probability ratings to specific merchant categories, and surfaces early warning indicators for emerging fraud patterns. This helps your organization intervene proactively before expenses are submitted.

Adaptive learning addresses the reality that fraud tactics evolve continuously. These systems distinguish seasonal spending variations from genuine anomalies and detect new patterns without extensive retraining. This capability has become critical with the emergence of AI-generated fake receipts that traditional methods miss. Navan’s dynamic policies apply this approach by, among other things, setting fair spend thresholds based on real-time market rates, preventing violations before they occur.

Common Fraud Schemes AI Can Identify Before Reimbursement

AI prevents four primary fraud schemes before reimbursement by analyzing transaction patterns, receipt authenticity, and behavioral anomalies that traditional auditing discovers only after payment.

Mischaracterized personal-to-business expenses Employees submit personal expenses disguised as legitimate business costs (the most common form of expense fraud). AI detects mischaracterization by:

• Comparing expense descriptions against calendar entries to verify claimed business meetings
• Analyzing merchant categories against employee roles
• Flagging geographic inconsistencies when “business” locations match personal addresses

Navan’s integrated platform, for example, automatically cross-references expense transactions with booked travel itineraries and calendar meeting details. A “client dinner” expense gets flagged when no client meeting appears on the employee’s calendar for that date or location.

Inflated and overstated expenses

Employees alter receipt amounts or claim premium services while using budget alternatives. An employee books an economy flight, for example, but submits an expense report for business class and pockets the difference. Each individual transaction may look reasonable in isolation, which is what makes this fraud difficult to catch manually. AI detects inflation by comparing claimed amounts against historical averages for similar transactions, analyzing line-item data for discrepancies, and using metadata forensics to identify altered receipts. These systems can identify both doctored and AI-generated fraudulent receipts, catching alterations your auditors would likely miss during a standard review.

Duplicate submissions across reporting periods

AI catches duplicate submissions by analyzing transaction amounts, dates, merchants, and receipt characteristics across all reports simultaneously, identifying patterns invisible when reviewing individual expense reports. Duplicate expense fraud is particularly difficult to detect in manual tracking systems because employees submit the same receipt across different reports, split expenses across multiple submissions, or route identical claims through different approval channels.

Real-time monitoring captures all transaction data directly from payment networks, so duplicate submissions across different periods surface immediately rather than months later.

Fictitious expenses and fake receipts

Advanced AI systems detect fabricated receipts, including AI-generated fakes, through metadata forensics examining file creation properties, texture and lighting consistency analysis, merchant database validation, and pattern recognition that identifies suspicious submission behaviors. This capability has become critical, as fraudsters now use generative AI tools to create convincing fake receipts that traditional methods miss entirely. If your current audit process relies on visual inspection of receipts, these AI-generated fakes will likely pass undetected.

Real-Time Prevention vs. Post-Transaction Detection

The core difference between modern AI-powered expense platforms and traditional auditing comes down to whether the company prevents fraud before corporate funds are spent or discovers it after payment. Modern platforms address this obstacle across two dimensions that eliminate the recovery problem.

Point-of-swipe authorization and continuous monitoring

Real-time authorization evaluates every transaction against policy rules as employees attempt purchases. When an employee swipes a corporate card at an unauthorized merchant category, the platform can decline the transaction immediately through merchant category restrictions and dynamic spending limits. No corporate funds leave your accounts, no expense report gets submitted, and no recovery process is necessary.

Beyond authorization, AI can provide continuous monitoring that flags anomalous spending within hours of its occurrence. These systems pull detailed transaction data directly from corporate card providers and instantly analyze it against your corporate policy and fraud indicators, generating real-time alerts for any non-compliant or suspicious activity.

Navan’s platform, for example, provides proactive control to flag or decline non-compliant spend at the point of purchase through merchant category restrictions and dynamic spending limits that automatically decline transactions exceeding authorized parameters.

Integrated platforms close the fraud window

With manual expense reports, employees control what gets reported. They decide which receipts to submit, how to categorize expenses, and whether to disclose policy violations. Integrated platforms that combine corporate cards with automated expense tracking remove that control by capturing 100% of transaction data directly from payment networks. Every swipe is recorded automatically. Expense suppression becomes impossible.

The timing difference translates directly to financial outcomes. Organizations using proactive authorization prevent fraud before loss occurs. Reactive post-transaction auditing, by contrast, often discovers fraud weeks after occurrence during monthly reviews, after corporate funds have already been reimbursed. Your finance team should prioritize real-time authorization capabilities over post-transaction detection when comparing platforms.

Implementation Requirements for Effective Fraud Detection

Organizations achieving the highest return from AI fraud detection address three critical requirements beyond technology deployment: human review workflows, audit trails, and full transaction coverage.

Design human review workflows before deployment

AI fraud detection achieves high accuracy on clear policy violations and compliant transactions, but a portion of ambiguous cases still require human judgment. Before launching, establish documented decision frameworks and escalation workflows for these edge cases.

Navan’s multi-agent system, for instance, divides responsibilities: the Expense Agent handles routine processing autonomously while the Audit Agent performs line-item compliance checking and fraud detection, surfacing ambiguous cases through dashboards that give your finance team real-time visibility over exceptions requiring human judgment.

Establish complete audit trails

AI systems must maintain complete audit trails documenting control design, testing procedures, and approval workflows for financial transactions subject to regulatory compliance. Your finance team should help ensure that AI-driven expense controls include:

• Documented policies and defined approval workflows
• Complete transaction logs with full context
• Evidence of regular control effectiveness testing

Configure for 100% automated review

Organizations should implement automated review of all spending rather than samples. Sampling-based auditing, where teams typically review 5% to 20% of transactions, leaves most fraud undiscovered by design. That’s not a detection gap — it’s a design flaw. Leading expense management platforms extend this capability by enforcing policies at the point of swipe, preventing non-compliant transactions before they require approval. When you move from sampling to full coverage, your detection rate increases proportionally.

How to Choose AI-Powered Fraud Detection for Your Organization

Selecting the right AI fraud detection starts with understanding whether your primary need is preventing fraud before it happens or detecting it faster after occurrence. Three questions will help you evaluate platforms effectively. Here are some questions to ask.

Does the platform prevent fraud at point of swipe or detect it after payment?

Real-time point-of-swipe authorization is the most important capability to verify. Platforms that only flag suspicious activity after reimbursement leave your organization in recovery mode. Also, confirm that the system analyzes 100% of transactions rather than sampling, and that it can detect AI-generated fake receipts. Ask about the level of transaction data captured — line-item purchase details matter more than merchant names and totals alone.

Does the platform unify cards, expenses, and travel on one system?

Integration architecture matters more than most finance teams initially recognize. Platforms that unify corporate cards, expense automation, and accounting integration eliminate the fraud window present when employees control information flow through manual expense reports. When you’re evaluating vendors, look for systems where your corporate card data, travel bookings, and expense policies all live on one platform.

A Forrester TEI study commissioned by Navan found that organizations using integrated T&E management save 40% of the time previously spent on expense auditing. Navan’s multi-agent system applies this unified approach through specialized AI agents, including an Expense Agent that recognizes travel context, reads calendar attendees, identifies merchants, and auto-creates expenses with the correct coding and policy compliance.