AI FRAUD DETECTION
Fraud detection and risk analysis
We build fraud detection around your transaction flow, balancing risk, customer friction and the response times your business requires.
- Real-time scoring inside a hard latency budget
- Graph ML that sees the ring a single transaction cannot
- Tuned on expected monetary loss, not an abstract score
A decision under a hard latency budget, where either mistake is expensive.
A card-not-present payment, a login from a new device, a sudden change of payee. Approve fraud and you take the loss; decline a good customer and you lose the sale and the trust.
The goal is not a perfect classifier. It is the decision threshold that costs your business the least once you account for both fraud losses and false-decline losses.
No single technique is enough. Together they cover what any one misses.
A systems problem as much as a modelling one.
The model is only as good as the features it can compute in the moment.
Gradient boosting scores the individual transaction, graph ML catches the coordinated ring, and anomaly detection covers the novel attack.
Measure fraud risk and customer friction together.
A model calling everything legitimate scores over 99% and catches nothing. We tune on the measures that reflect the real trade-off.
Precision and recall
Of what we flag, how much is truly fraud, and of all fraud, how much we catch. The two trade off, and where you sit is a business decision.
AUC-PR
Area under the precision-recall curve, the right summary under extreme class imbalance. Where ROC-AUC flatters a weak model, this does not.
Expected monetary loss
Each decision weighted by its cost, a missed high-value fraud versus a blocked genuine purchase. We optimise the threshold to minimise total expected loss, the number that actually matters.
From a costed baseline to a scorer inside your transaction flow.
Baseline and cost model
Current fraud loss and false-decline rates quantified, with the expected-loss target agreed up front.
Real-time feature and model build
Feature store, gradient-boosted scorer, and graph and anomaly layers matched to your fraud profile.
Decision orchestration
Approve, decline, and step-up logic plus the analyst escalation path, in your transaction flow.
Analyst workbench integration
Scores, drivers, and network context in your case-management tools.
Monitoring and retraining
Performance and drift tracked in production on our [MLOps](/mlops) foundation.
The model does not act alone on the hard cases.
Clear-cut transactions are approved or declined straight through. Ambiguous ones trigger a step-up, a challenge rather than a blunt block. Uncertain, high-value cases route to a fraud analyst with the risk drivers and linked-account context laid out.
Analyst verdicts and confirmed-fraud labels feed straight back as training signal, so the system adapts as fraud tactics shift, which they will. Automation absorbs the volume; analysts keep control where the stakes and uncertainty are highest.
Agree fraud-loss, false-decline and response-time limits before release. Evaluate on representative transactions and review the results with the fraud team before expanding automated decisions.
Part of our applied AI solutions portfolio.
Applied AI Solutions
The broader portfolio of generative, predictive and vision systems this model belongs to.
Fraud Detection Accelerator
A defined eight-week path from risk definition to a measured pilot and production roadmap.
Scaled GenAI & AI Platforms
The platform foundation the real-time scorer runs on.
Banking & Financial Services
The sector view of where real-time fraud decisioning lands.
What risk and fraud teams ask us first.
Because fraud is rare, accuracy is dominated by legitimate transactions and hides the errors that cost money. We optimise precision, recall, AUC-PR, and, above all, expected monetary loss, which balances missed fraud against blocked good customers.
Yes. Graph ML surfaces connected accounts, shared devices, and payee networks a per-transaction model cannot see, so coordinated rings are detectable even when each transaction looks fine alone.
Anomaly detection flags novel patterns before they are labelled, and analyst-confirmed cases retrain the model quickly, so it adapts rather than protecting against last year's fraud.
Let us quantify your current fraud and false-decline costs and show what a real-time, cost-aware model would change.
The threshold that costs your business the least, with analysts in control where the stakes are highest.