Fraud & Risk Detection

Find patterns humans miss, stop threats before they scale

Fraud & Risk Detection

€50B

lost to VAT fraud alone in the EU every year — Europol

Gigabytes

of sensitive financial data processed locally — no cloud, no exposure

100%

data sovereignty — runs entirely on your own infrastructure

Why Rules-Based Systems Fail

Fraudsters know the rules. They stay below thresholds, use aged accounts, and rotate tactics faster than any compliance team can update conditions. The result: investigators buried in false positives, genuine fraud slipping through the gaps, and a system that gets slower and more expensive to maintain as rules multiply. The bigger problem: fraud doesn't happen in isolation. It hides in networks — shell companies, related accounts, cross-border transactions that look innocent individually but tell a different story together. No set of rules can see that.

What's changed is the technology. Specialised open source language models — not frontier models from the big cloud providers, but smaller models purpose-built for financial investigation — can now run entirely on-premise. Work that used to require a team of investigators can be done in minutes. The bottleneck is no longer processing power or analyst hours. It's having the right system in place.

How AI Detects What Rules Miss

1

Pattern Recognition

AI identifies complex multi-dimensional patterns across millions of transactions simultaneously. Catches coordinated fraud rings, velocity patterns, and behavioral anomalies that rules can't see.

2

Network Analysis

Map relationships between entities — accounts, devices, IP addresses, beneficiaries. Expose hidden connections between seemingly unrelated actors. Detect money mule networks, synthetic identity rings, and collusion schemes.

3

Adaptive Learning

Fraud patterns evolve. Rule-based systems stay static. AI continuously learns from new fraud cases, adapts to emerging schemes, and updates detection models without manual intervention.

4

Precision Alerting

95% reduction in false positives means your team investigates real fraud, not noise. AI prioritizes cases by risk score, provides evidence summaries, and routes alerts to the right team automatically.

What We Detect

Transaction Fraud

Payment fraud, card fraud, account takeover, authorized push payment scams. Real-time detection across digital and traditional channels.

Insurance Fraud

Claims fraud, application fraud, provider fraud. Pattern matching across historical claims, network analysis of linked parties, anomaly detection in claims behavior.

Procurement Fraud

Vendor fraud, bid rigging, invoice manipulation, conflict of interest detection. AI analysis of procurement patterns, supplier relationships, and approval workflows.

Internal Fraud

Employee fraud, data theft, unauthorized access, expense fraud. Behavioral analytics, access pattern monitoring, and anomaly detection on internal systems.

95%

reduction in false positive alerts

70%

increase in fraud detection rate

Real-time

detection and alerting

Ready to stop fraud before it scales?

Speak with our Fraud & Risk Detection director to learn how AI can protect your organization from evolving threats.

Johan Tornborg CEO, Fraud & Risk Detection
Get in Touch