Glacien · FSI · Insurance · Healthcare
Fraud Detection (FWA)

Fraud detection that thinks
faster than the fraudster.

Real-time fraud, waste, and abuse detection powered by continuously-learning agents. Catch sophisticated schemes in milliseconds — without drowning in false positives.

Detection accuracy
99.5%
Response time
<1s
False positives cut
85%
Monitoring
24/7
The problem

Rule engines can't keep up. Fraudsters evolve weekly.

Traditional fraud detection is brittle, slow, and noisy — missing novel patterns while flooding analysts with false positives.

Sophisticated schemes

Fraudsters constantly evolve tactics, making it difficult for rule-based systems to keep pace with new patterns.

Real-time detection

Traditional methods are too slow, allowing fraudulent transactions to complete before they can be stopped.

High false positives

Aggressive rule sets flag legitimate transactions, frustrating customers and burning analyst hours.

What it does

A live defence wired into every transaction.

Adaptive ML, behavioural graphs, and explainable scoring — integrated with your core systems for millisecond decisions.

Machine learning models

Advanced ML algorithms continuously learn from new fraud patterns to improve detection accuracy and adapt to evolving threats.

Real-time analysis

Analyse transactions in milliseconds to detect and prevent fraud before it impacts your business or customers.

Multi-dimensional detection

Examine transactions from behavioural, contextual, and historical angles simultaneously to catch patterns that single-axis rules miss.

Adaptive risk scoring

Dynamic risk scores that adjust based on context, user history, and the current threat landscape — explainable to analysts and auditors.

What changes

Detection rates up, false positives down.

99.5%
Detection accuracy
<1s
Response time
85%
False positives cut
24/7
Monitoring

Stop the next scheme before it posts.

Book a working session. We'll run your historical transactions through our detection harness and show you what rule-based systems miss.