AI Data Scientist for Finance Teams
Finance teams ship the same model classes every quarter — credit risk, fraud, AML alerts, portfolio attribution, default forecasts. OctOpus is the AI data scientist that builds every one of them end-to-end: profiles the data, picks the right model family (CatBoost / TabPFN / LightGBM with monotonicity constraints), validates with proper time-aware CV, and ships a production prediction API. No three-month consulting engagement, no notebook hand-off.
What finance models OctOpus handles out of the box
Credit risk scoring with monotonicity constraints (FICO-style, regulator-friendly). Fraud detection with class-imbalance handling and PR-AUC reporting. AML alert triage with anomaly scoring on transaction graphs. Probability-of-default with calibration (Brier score, isotonic regression). Customer lifetime value for retail banking. Treasury cash forecasting with hierarchical reconciliation. Each one ships with a model card the model-risk team can sign off on.
Leakage detection — the bug that costs finance teams the most
The most common credit-model failure is target leakage — a feature that wouldn't exist at prediction time leaks in during training, the model scores 0.95 AUC on backtest, and crashes to 0.6 in production. OctOpus's leakage probe runs BEFORE every experiment: it checks for too-perfect correlations, future-dated features, and identifier columns posing as predictors. Catches what reviews miss.
Compliance-friendly defaults
Monotonicity constraints, holdout governance (the holdout dataset is stored outside the agent workspace so the LLM cannot see it), reproducible train.py for every experiment, audit log of every decision. Runs inside your VPC on private deployments. Bring-your-own Bedrock / Azure OpenAI keys so prompts never leave your cloud.
Key capabilities
- Credit risk · fraud · AML · default · portfolio · treasury — same workflow.
- Time-aware CV, no random splits on time-series data.
- Built-in leakage detector + calibration metrics.
- Monotonicity constraints for regulator-friendly tree models.
- Runs in your VPC; data never leaves your cloud.
Frequently asked questions
Can OctOpus build a credit risk model in finance?
Yes. OctOpus builds credit risk models end-to-end: profiles the loan-book data, applies monotonicity constraints for regulator-friendliness, runs time-aware cross-validation, calibrates probability scores, and ships a model card. The most common variants — application scorecards, behavioural scorecards, IFRS-9 PD models — all fit the same pipeline.
How does OctOpus avoid target leakage in financial data?
A leakage probe runs BEFORE every experiment. It flags features that correlate suspiciously well with the target, columns that look like outcomes rather than predictors, and time-dated fields that wouldn't exist at prediction time. The same probe catches identifier leakage (account IDs, customer IDs) that consulting projects routinely miss.
Is OctOpus suitable for regulated environments (SOC 2, GDPR, model-risk)?
Yes. OctOpus Enterprise runs inside your VPC, bring-your-own Bedrock / Azure OpenAI keys so prompts never leave your cloud, audit log of every research decision, holdout dataset stored outside the agent workspace, reproducible train.py per experiment, model card for sign-off.
What models does OctOpus pick for fraud detection?
Imbalanced binary fraud: CatBoost / LightGBM with class weights, PR-AUC as the optimisation target, calibration with isotonic regression. For graph-shaped fraud (collusion, money mules), anomaly detection on transaction features plus an isolation-forest baseline. The agent picks the family based on the data signature.