Use case · Fraud detection
Autonomous AI fraud detection — from labeled data to a real-time scoring API.
Drop your transaction history, claims data, or account events. OctOpus profiles the imbalance, picks the right combination of supervised and anomaly models, calibrates probabilities, validates on a real out-of-sample holdout, and ships a low-latency scoring endpoint — without a data scientist in the loop.
TL;DR. Fraud is the hardest tabular ML problem: severe class imbalance, adversarial drift, regulatory scrutiny, and unforgiving latency budgets. OctOpus handles all four. The agent combines supervised classifiers with anomaly detectors, evaluates on PR-AUC and recall-at-fixed-FPR, calibrates probabilities, and bundles the full training code and the trained model so your risk team can audit every line.
Fraud problems OctOpus handles well
- Payments fraud — card-present, card-not-present, P2P, BNPL chargeback risk.
- Account takeover (ATO) — login pattern anomalies, session fingerprint drift, velocity rules + ML.
- Application fraud — synthetic identity, document-signal scoring, bust-out risk on new accounts.
- Claims fraud — insurance claim severity scoring, provider-collusion network signals.
- AML transaction monitoring — structuring, layering, money-mule rings, typology drift.
- E-commerce abuse — promo abuse, refund fraud, return fraud, account farming.
Models the agent rotates through
| Approach | Family | When the agent picks it |
|---|---|---|
| Baseline | Gradient boosting with class weighting + isotonic calibration | Almost always — handles imbalance natively, fast to score. |
| Tuned gradient boosting | A different boosting family with automated hyperparameter search (PR-AUC objective) | When the baseline has headroom on the positive class. |
| Anomaly | Unsupervised anomaly detectors and reconstruction error | Cold-start / unsupervised drift / rare-typology detection. |
| Deep / modern | Modern tabular deep learning (for n<10k labels) | Rich categorical interactions, small labeled sets. |
| Stacking | An ensemble over supervised + anomaly base learners | When supervised and anomaly residuals are uncorrelated. |
How a fraud-detection run looks
- Profile. Detects the positive rate, the label noise risk, leakage candidates (timestamp leakage is fatal), and the realistic out-of-time validation split.
- Plan. Writes a research spec: PR-AUC + recall-at-FPR as primary metrics, isotonic calibration, time-based holdout for adversarial drift.
- Run. Writes new training code for every experiment, executes it in a sandbox, captures metrics + calibration plots.
- Diagnose. When something fails (rare-class collapse, NaN gradient, OOM on cardinality), the agent writes a targeted fix and retries.
- Validate. Scored on a time-ordered holdout slice the LLM never sees — guards against time leakage and overfit.
- Deploy. Low-latency scoring API plus a deploy bundle for self-hosted inference inside your VPC.
What enterprise risk teams get back
- Calibrated probability score per transaction, account, or claim.
- Recall and precision at every operating point — pick your block / review threshold from the curve.
- Feature importances and SHAP attributions per prediction for case-investigator workflows.
- Out-of-time holdout report you can hand to model-risk-management (MRM).
- The exact training code the agent wrote — fully inspectable for governance and compliance.
- Deployable real-time scoring endpoint, or a deploy bundle for your own inference stack.
Compliance and audit
OctOpus Enterprise is designed for SOC 2-, PCI DSS-, and AML-aligned deployments. Every research run is fully audited — the research plan, every version of the training code, every error, every revision, the validated winner's holdout metrics, and the deployed artifact hash. For PCI workloads, the Desktop app or private VPC deployment keeps cardholder data on your perimeter. See Enterprise for residency, SSO/SCIM, and audit details.