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Use Case
Rules-based fraud detection creates a cat-and-mouse game. You add a rule for a fraud pattern you've seen. Fraudsters adapt slightly and bypass the rule. You add another rule. The ruleset grows; maintenance complexity grows with it; novel patterns still get through.
Custom ML models learn the behavioural signatures of legitimate versus fraudulent activity in your specific transaction patterns. When fraud patterns shift, new account takeover techniques, new synthetic identity approaches, new API abuse vectors, the model detects the anomaly without a manual rule update, because it learned what "normal" looks like in your system.
We build transaction-level anomaly detection, account behaviour baselines, real-time inference pipelines, and retraining pipelines, deployed alongside your existing rules engine, not replacing it.
Tell us about your fraud detection challenge.
Six components that build a self-improving fraud detection layer on top of your transaction and behaviour data.
The model learns the baseline pattern of legitimate transactions in your system: amounts, frequencies, merchant categories, geographic distributions, device fingerprints. Transactions that deviate significantly from the learned baseline are scored as anomalies and flagged for review or automatic action.
Beyond individual transactions, the system builds a behavioural baseline per account: typical login times, device types, locations, transaction patterns. A session that deviates sharply from an account's baseline (login from a new country, unusual transaction amounts, rapid sequential actions) scores high and triggers immediate review.
For payment flows and account actions that need a decision within the transaction itself, the inference pipeline is built for sub-100ms latency. The model scores the event synchronously, the payment is approved, held, or declined based on the risk score before the user's session completes.
High-risk events are pushed to a manual review queue, your existing case management system or a purpose-built review dashboard. Analysts see the full event context, account history, and model explanation for why the event was flagged, not just a risk score number.
Most teams have existing rules-based fraud prevention. The ML model complements the rules engine rather than replacing it. Rules handle known fraud patterns explicitly; the ML model catches novel patterns the rules have not anticipated. The two systems score in parallel and the combined score determines the action.
Fraud patterns evolve. The retraining pipeline ingests confirmed fraud labels from your case management system and retrains the model on a scheduled basis, typically weekly or bi-weekly. This keeps the model current as fraud patterns shift without requiring a manual intervention to update it.
From data discovery to live inference with a retraining loop, the build sequence.
Data discovery and fraud label audit
We review your historical transaction data and fraud labels. Clean, confirmed fraud labels are the most important ingredient, a model trained on noisy labels produces noisy predictions. If labels are incomplete, we work with your fraud team to improve them before training.
Feature engineering for fraud signals
Fraud features are engineered from raw transaction and session data: velocity features (how many transactions in the last 60 minutes from this account?), graph features (is this payment destination connected to known fraudulent accounts?), deviation features (how much does this transaction differ from this account's normal pattern?).
Model training and threshold tuning
The model is trained and evaluated on historical labelled data. The decision threshold is tuned to your preferred operating point, typically a precision-recall tradeoff that balances catching fraud against blocking legitimate transactions. We simulate false positive rates on legitimate transactions before deployment.
Real-time inference setup
For synchronous fraud scoring (blocking at payment time), the inference pipeline is deployed as a low-latency API. We load-test the endpoint under peak transaction volumes to confirm it meets your latency SLA, typically sub-100ms at p99.
Case management and feedback loop
The review queue is integrated with your case management system. Analyst decisions (confirm fraud, mark as legitimate) are logged and fed back into the retraining pipeline as new labelled examples.
Monitoring and scheduled retraining
Model performance metrics (precision, recall, false positive rate) are monitored daily. Significant degradation triggers an alert. Scheduled retraining incorporates new labelled data on a bi-weekly basis, keeping the model current with evolving fraud patterns.
Platforms where fraud is a material risk and rules-based prevention is leaving gaps.
As transaction volume grows, manual review does not scale. A model that flags the top 2% of high-risk transactions for human review (while automatically blocking the highest-risk events) keeps your fraud team focused on the cases that require judgment.
Account takeover fraud (where fraudsters use stolen credentials to make purchases) follows behavioural patterns that rules engines miss. A model trained on session behaviour detects the anomalous usage pattern even when the login credentials are valid.
APIs that charge per call or have rate limits are targets for abuse, scraping, credential stuffing, and programmatic fraud. Anomaly detection on API usage patterns identifies abusive behaviour that does not match any registered user's normal usage profile.
We’d rather decline than take a project that won’t deliver value.
Products with no historical fraud labels
A supervised fraud model needs confirmed examples of fraud to learn from. If you have no historical fraud labels (either because fraud was not tracked or because your product is very new), we cannot train a supervised model. An unsupervised anomaly detection approach is possible as a starting point, but its precision will be lower and it will require more analyst review time to validate. We scope this honestly during discovery.
Teams who want zero false positives
Any fraud detection system involves a tradeoff between catching fraud (recall) and blocking legitimate transactions (false positive rate). Chasing zero false positives means accepting much lower fraud recall, meaning more fraud gets through. We tune the model to your stated operating point, but we will not promise a false positive rate of zero. We show you the tradeoff curve during evaluation and let your team choose the operating point.
Tell us your transaction volume, your current fraud detection approach (rules engine, manual review, third-party service), and the fraud types you are most concerned about. We'll reply within one business day.