AI Fraud Detection
ML models scoring transactions in real time, tuned against the actual cost of each error — a declined good customer doesn't cost the same as a missed fraud case.
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What makes fraud detection trustworthy, not just accurate?
A fraud model's accuracy figure hides the decision that actually matters: a missed fraud case and a wrongly blocked good customer don't cost the same, and a threshold tuned to a single accuracy number ignores that asymmetry. AI fraud detection done properly is tuned against the real cost of each error type for your business, not a leaderboard metric.
It also has to be fast enough to run inside the payment flow rather than after it — a model that adds even a second of noticeable delay costs more in cart abandonment than it saves in blocked fraud — and every flagged transaction needs to carry the signals that drove its score, because "the model said so" isn't an answer your support team can give a disputing customer.
What the build covers
Real-time scoring
Transactions scored inside the payment flow in under 100ms, so the check doesn't add noticeable checkout latency.
Cost-weighted tuning
Thresholds tuned against the actual cost of a missed fraud case versus a wrongly blocked good customer, not a single accuracy figure.
Explainable scoring
Every flagged transaction carries the signals that drove its score in a reviewer console, giving support a real answer instead of a black box.
Reviewer console
A queue for manually reviewing flagged transactions with full context, so genuine fraud and false positives are triaged efficiently.
Model monitoring
Ongoing tracking of model drift and false-positive rate, so the model doesn't quietly degrade as fraud patterns shift.
Dispute support
Transaction history and scoring rationale available in a form that supports a chargeback or customer dispute investigation.
How the build runs
Quantify the real error costs
We work with you to price a missed fraud case against a wrongly blocked good customer, because that asymmetry should set the threshold, not a generic target.
Build & train against your data
Models are trained on your actual transaction patterns, not a generic fraud dataset that doesn't reflect your customer base.
Integrate for sub-100ms scoring
Scoring is engineered to run inside the payment flow without adding noticeable checkout latency.
Ship the reviewer console & monitor
The reviewer console and drift monitoring go live alongside the model, not as a later addition, so review and tuning start from day one.
AI Fraud Detection FAQ
The tools we build with
Latency and explainability are treated as requirements, not tuning — a decision you can't justify is a decision you can't ship.
Risk & ML
PyTorch
scikit-learn
PandasCore services
Python
Go
PostgreSQLStreaming & cache
Kafka
Redis
TimescaleDBOps
AWS
Terraform
DatadogRelated work
Related reading
Blocking good customers to catch fraud you can't explain?
Tell us your transaction volume and where false positives are costing you customers. We'll scope a model tuned to your real error costs.