Rules
Fuzzy logic & rule-based ML. Encodes expert knowledge and policy constraints as a first, explainable line of defense.
Product / SciFraud
Clear,audit-ready AIto reduceoperational costsin financial crime
Minimize false positives and slash review times through advanced scientific AI, built to guarantee regulatory compliance.
Banking / Financial CrimeAML & SanctionsTransactional fraudInternal fraud
How it works
Decoding behavior. Predicting the unseen.
Turning behavioral data into certainty: smarter anomaly detection powered by Bayesian Network AI models that learn each customer's normal and flag what doesn't fit.
Unsupervised
Bayesian networks. Per-customer behavioral fingerprints that surface anomalies without needing labeled fraud.
Supervised
Graph neural networks. Learn from confirmed cases and money-flow structure to sharpen precision on known typologies.
Under the hood · AI stack
A hybrid scientific engine.
UFDM and SFDM working together — unsupervised behavioral scoring plus supervised precision on known fraud.
UFDM · Unsupervised
Learns each customer's normal — flags what doesn't fit. An ensemble of Bayesian Networks builds a per-customer behavioral fingerprint from transaction history, then scores every new event by how likely it is to come from the real user vs. a fraudster.
- No labeled fraud required — adapts to each user.
- Probabilistic risk score per transaction.
- Catches novel and previously unseen fraud patterns.
SFDM · Supervised
Learns from confirmed fraud — sharpens what's already known. A classifier trained on historical labeled cases assigns a fraud probability to every new event. Training and prediction run as separate phases so the model improves continuously.
- High precision on known fraud typologies.
- Retrainable as new labeled cases arrive.
- Pairs with UFDM for layered defense and lower false positives.
One engine · three applications
The same scientific core, tuned for the threats that matter most.
AML & Sanctions Monitoring
Behavioral baselines and probabilistic scoring to surface money-laundering patterns and sanctions exposure with fewer false positives.
Transactional Fraud Prevention
Real-time anomaly detection on every payment — UFDM + SFDM working together to stop novel and known fraud as it happens.
Internal Fraud Prevention
Profiles employee and operator behavior to detect collusion, policy abuse and insider anomalies before they escalate.
Benefits
A unified scientific framework that turns fraud detection into a strategic advantage.
Faster, sharper, and dramatically more efficient than static, rule-based, siloed systems.
Unified Scientific AI framework
Proactive, explainable, and scalable fraud detection — far beyond the static, rule-based, siloed systems used across the industry.
Performance improvement
Higher detection accuracy that moves institutions from reactive case management to strategic risk leadership.
Adaptive real-time AI
Detects emerging and never-before-seen fraud patterns the moment they appear in your transaction stream.
Operational cost reduction
Up to 70% less effort on review, validation, and prioritization processes across the fraud operations stack.
Frees resources for high-value work
Analysts focus on investigation, proactive prevention, and customer experience instead of repetitive triage.
Detection from day one
Delivers value immediately — even with scarce, sparse, or incomplete historical fraud records.
MRM Research
Featured insights and success cases
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