Fraud Detection
Fraud is cross-channel. Most detection systems are not.
Financial institutions manage fraud across card transactions, account activity, digital channels and third-party data simultaneously. Detection that operates channel by channel misses the patterns that span all of them and cannot produce the lineage regulators now require.
Real-time
detection across all transaction channels and accounts simultaneously
Full lineage
behind every fraud decision, explainable to regulators on demand
Weeks
to first fraud models running on complete, governed transaction data
WHAT IT DELIVERS
Fraud is caught before the loss is confirmed.
Fraud signals are detected across all transaction channels simultaneously, not one channel at a time. Stratio reads the full customer relationship, reducing both missed fraud and false positives that block legitimate customers.
Card transactions, account activity, behavioural signals and external watchlists all feed the model. No source is excluded because it was difficult to connect. Detection is only as good as the data behind it.
Full data lineage is built into every detection decision. Regulatory enquiries are answered immediately with traceable evidence. Internal teams can challenge and refine thresholds without external support.
Models are monitored and updated as fraud behaviour evolves. New signals from any connected source are incorporated without disrupting existing detection. The system improves without starting over.
What the organisation gains
01
Real-time detection across all channels means fraud is caught at the point of transaction, not discovered in reconciliation. The cost of the loss and the cost of remediation both fall.
02
When the model is trained on complete, cross-channel data, it distinguishes genuine fraud from unusual-but-legitimate behaviour more accurately. Customer experience improves, and investigation costs fall.
03
Every detection decision has complete lineage. Regulatory inspections, internal audits, and disputes are handled with traceable data rather than manual reconstruction of what the model saw and why.
Complete data, calibrated models, continuous governance.