How to Build a Fraud Detection Model Without Code

You can build a fraud detection model with no code by uploading historical transactions labeled fraud or legitimate, describing the goal in chat, and letting TensorTurn train an imbalance-aware classifier (XGBoost or scikit-learn) on a cloud GPU, then deploy a real-time /predict API. Because fraud is rare — often well under 1% of transactions — the health check, leakage detection and precision/recall tuning matter far more than raw accuracy; the practical target is catching as much fraud as your review team can handle.

Why fraud detection is a special kind of classification

Fraud is a highly imbalanced binary classification problem. A model that simply predicts 'everything is legitimate' can be 99.5% accurate and catch zero fraud, which is why accuracy is the wrong metric here. What you actually manage is a trade-off: catch more fraud (higher recall) at the cost of flagging more legitimate transactions for review (lower precision). TensorTurn helps you tune that trade-off to your team's review capacity rather than chasing a meaningless accuracy number.

What data you need

• One row per transaction or event, with a verified fraud label (1/0) you already confirmed.
• Behavioral and contextual features: amount, time of day, merchant category, geo distance from usual location, device, account age.
• Velocity features that expose fraud rings: number of transactions in the last hour or day, repeated failed attempts.
• Enough confirmed fraud cases — at least hundreds — even though the overall rate is tiny.
• No post-hoc leakage columns such as chargeback flag or manual-review outcome, which only exist after fraud is known.

How to build it on TensorTurn (no code)

• Import your transaction file (CSV, Excel or from a URL).
• Run the tabular health check — leakage detection is especially important here, along with outliers and correlations.
• In chat: 'Detect fraud in the is_fraud column; it is about 0.5% positive, maximise recall while keeping precision above 0.3.'
• TensorTurn trains with class weighting, self-heals broken cells, and reports imbalance-aware metrics.
• Review PR-AUC, ROC-AUC, precision and recall at different thresholds, and the confusion matrix.
• Deploy a real-time /predict endpoint that scores each incoming transaction.

Metrics that actually matter

Never judge a fraud model on plain accuracy. Use PR-AUC (precision-recall area, which is designed for rare positives), and look at recall at a fixed precision — for example, 'what fraction of fraud do we catch if we can only afford to review 2% of transactions?'. The confusion matrix at your chosen threshold tells you exactly how many fraud cases you catch versus miss and how many false alarms your team must handle.

How well can it work?

With good behavioral and velocity features, catching 60–85% of fraud at a manageable false-positive rate is a realistic range. With only thin, static features, detection is much weaker. Two honest caveats: leakage can make results look perfect but fail in production, so trust the health check; and fraud patterns drift as fraudsters adapt, so you should retrain on fresh confirmed cases regularly to keep the model effective.

Deploy as an API

Publish an authenticated /predict endpoint on Modal with a SHA-256-hashed Bearer key and per-call logging, scaling to zero when idle. Score transactions in real time via the playground's cURL, JavaScript, Python or Rust snippets, and use the returned probability to auto-approve, hold for review, or block.