Fraud Detection
Stop fraud in real time, without blocking good customers.
A multi-layer fraud detection system that combines behavioural models, graph analytics and real-time scoring. Every decision comes with an explanation your analysts and auditors can use.
01 · Overview
Caught between two costs
Fraud teams pay for the losses they miss and for the good customers they block. Static rules can't keep up. Fraudsters probe thresholds, rotate devices and coordinate across accounts. Each new rule adds friction for legitimate users, and analysts drown in alerts.
Built with
- Behavioural models
- Graph analytics
- Real-time scoring
Deployment
On-premise, private cloud or in-region.
02 · How it works
Four layers, one decision
- 1
Understand normal
A behavioural profile for every customer, device and merchant: spending rhythm, location, channel, velocity. Anomalies are measured against that customer's own baseline.
- 2
See the network
Graph analytics link accounts, devices, IPs and beneficiaries, so mule rings, synthetic identities and coordinated attacks show up as structures.
- 3
Score in milliseconds
Rules, gradient-boosted models and deep sequence models combine into one risk score inline with authorisation, with a design target under 100 ms.
- 4
Explain, act, learn
High-risk events go to a case queue with the top reasons attached. Analyst decisions feed back into retraining as fraud patterns shift.
- Event streamCards, transfers, logins
- Feature storeBehavioural features
- Ensemble scoringRules · GBM · sequence · graph
- Decision API<100 ms target
- Case managementFeedback ↺ retraining
03 · Capabilities
What it detects
- Card-not-present and e-commerce payment fraud
- Account takeover and suspicious login behaviour
- Synthetic identity and first-party fraud
- Mule accounts and coordinated fraud rings
- Mobile wallet and instant-payment fraud
04 · Outcomes
What we measure
- Lower fraud losses
- Fraud basis points, before vs. after, on a held-out period
- Fewer false declines
- False-positive rate at a fixed detection rate
- Faster investigations
- Alerts per analyst per day; time to decision
- Regulatory confidence
- Every decision has stored, human-readable reasons
Example scenarios
Illustrative- Card issuerReal-time scoring of every authorisation, with step-up authentication on borderline cases instead of hard declines.
- E-commerce merchantPayment-fraud screening that reduces chargebacks without lowering checkout conversion.
- Digital bankAccount-takeover detection from login, device and session behaviour.
- LenderGraph analysis that exposes networks of synthetic identities at onboarding.
05 · Governance
Explainability & governance
Controls built in
- Per-decision reason codes (SHAP) in the analyst's case view
- Model cards, validation reports and a versioned model registry
- Drift monitoring with alerting and retraining triggers
- Full decision log for audit and dispute handling
Technology
- Python
- PyTorch
- TensorFlow
- XGBoost
- Kafka
- Flink
- Spark
- Redis
- Neo4j
- Kubernetes
06 · FAQ
Common questions
Does it replace our existing rules engine?
No, it doesn't have to. It can run alongside your rules, either scoring in shadow mode first or adding a model score your rules can use.
How much historical data do we need?
Typically 6–12 months of transactions with fraud labels. Where labels are scarce, we start with unsupervised anomaly and graph methods.
How long until production?
A proof of concept on your data usually takes 3–6 weeks. Shadow-mode pilot to production depends on your integration path.
Can the data stay in our environment?
Yes. We can deploy and train entirely inside your infrastructure.
Let's look at your fraud data together.
Start with a discovery session. We'll tell you where AI can move the numbers, and where it can't.
Start a project
