No clean labels
Learning without a perfect fraud dataset. The model had to work with limited tagging and real banking data quality issues.
Impact / Financial Crime
Finding fraudneedles intransactionalhaystacks.
A banking use case for SciFraud: start from messy transactional data, build a risk engine, explain the alerts and give fraud teams a defensible shortlist of cases.
- Client typeRetail bank
- ProblemPotential fraud discovery
- Data6 months of transactions
- Precision85%
Retail bankingTransactional fraudBehavioral AIExplainable alerts
The challenge
Detect 14 potential frauds among thousands of transactions.
The task was not only to detect risk. It was to identify a short, auditable set of cases that fraud teams could understand, prioritize and act on.
Operational noise
Low alert volume mattered. Fraud teams cannot work with endless false positives. The system needed precision and explainability.
Infrastructure
Deploy inside client constraints. Deployment was designed for the client's infrastructure and governance context, with production-ready integration.
Behavior
The transaction is not enough. SciFraud uses behavioral and contextual features to understand what is normal for a customer and what is suspicious.
Approach
Data cleaning, risk engine, explanation engine.
SciFraud is not just a model. It is a pipeline from raw banking data to explainable operational intelligence.
Normalize messy banking data
Clean, engineer and structure transaction, channel and customer behavior data for model readiness.
Build behavioral risk signals
Model anomalies around individual behavior, transaction context and patterns that traditional rules can miss.
Explain the alert
Deliver alert narratives that explain why the case is risky and what evidence should be reviewed by the fraud team.
Results
A smaller, sharper queue for fraud review.
Measurable outcomes from the SciFraud deployment on the client's transactional data.
85%
Precision
0.04%
Alert rate
Weeks
To UAT deployment
14
Potential cases surfaced
“The value is not only flagging a transaction. The value is telling the analyst why this customer, why this behavior and why now.”
— SciFraud engagement lead, MRM Analytics