Fraud Detection

Fraud costs companies worldwide over 5% of annual revenue — according to the Association of Certified Fraud Examiners. Rule-based systems only detect known patterns. AI finds the unknown too.

Anomaly Detection

How AI Finds Anomalies

Instead of rigid rules ("alert for transactions over €10,000"), AI learns what's normal — and flags anything that deviates:

Statistical anomalies: Transactions outside the expected distribution
Behavioral anomalies: An employee suddenly showing unusual booking patterns
Network anomalies: Unusual relationships between suppliers, employees, and accounts

Unsupervised Learning

The key advantage: AI doesn't need labeled fraud cases. Unsupervised models (Isolation Forest, Autoencoder, DBSCAN) learn normal data distribution and identify outliers automatically.

Advantage: Detects new, previously unknown fraud patterns — so-called zero-day fraud.

Real-Time Monitoring

From Batch to Streaming

Traditional audits happen monthly or quarterly — fraud is discovered weeks later. AI real-time monitoring checks every transaction in milliseconds:

Transaction scoring: Each booking receives a risk score (0–100)
Threshold alerts: Score above 80 → automatic warning to compliance
Adaptive thresholds: AI adjusts thresholds based on context (year-end bookings are naturally higher)
Investigation queue: Prioritized list of suspicious transactions for manual review

Use Cases in Corporate Context

Expense fraud: Duplicate receipts, fictitious expenses, splitting below approval limits
Supplier fraud: Phantom invoices, inflated prices, kickback arrangements
Procurement fraud: Favoritism toward specific suppliers without tendering
Payroll fraud: Ghost employees, unauthorized salary changes

ML Models in Practice

Model Selection

Proven approaches for fraud detection:

Model	Strength	Use Case
Random Forest	Robust, explainable	Transaction classification
XGBoost	Highest accuracy	Risk scoring
Autoencoder	Detects unknown	Anomaly detection
Graph Neural Networks	Network analysis	Supplier fraud

Feature Engineering

Critical features for fraud models:

Temporal patterns: Time of day, day of week, month-end
Amount distribution: Deviation from personal average
Frequency: How often does this employee/supplier post?
Counterparty risk: Score of recipient/supplier
Sequence patterns: Unusual sequence of bookings

Managing False Positives

The biggest practical problem: Too many false alarms. If 95% of alerts are harmless, the compliance team eventually ignores them.

Solution: Feedback loop — compliance marks alerts as "fraud confirmed" or "false positive," AI learns and continuously reduces false alarms.

Critical: Fraud detection is a cat-and-mouse game. Fraudsters adapt. That's why your model needs continuous retraining — not once a year, but monthly.