Can OctOpus model PD, LGD, and EAD?

Yes. OctOpus handles probability of default (PD) as a calibrated binary classifier, loss given default (LGD) as a bounded regression (beta or tweedie objective), and exposure at default (EAD) as a regression with monotonic constraints. The agent picks the right loss function and calibration per task and validates on out-of-time holdout.

Does OctOpus support monotonic constraints required for explainable credit models?

Yes. LightGBM, XGBoost, and CatBoost all support monotonic feature constraints, and OctOpus uses them when the regulatory profile of the use case demands directional consistency (e.g., probability of default must be non-decreasing in delinquency count). The agent encodes the constraint set into the training script and verifies the deployed model preserves monotonicity.

How does OctOpus support Model Risk Management (MRM, SR 11-7, IFRS 9, CECL)?

Every research run captures the research plan, every experiment's train.py, every error and revision, the validated winner's holdout metrics, and the deployed artifact hash. The audit log is exportable for MRM review. The train.py is fully readable — no black-box AutoML pipeline blocks — and you can run the same model deterministically inside your own infrastructure. For IFRS 9 / CECL, OctOpus emits calibrated PD and expected-loss outputs with confidence bounds.

Can OctOpus produce calibrated probability scores for credit scoring?

Yes. The agent calibrates output probabilities with isotonic or sigmoid (Platt) calibration on a separate calibration slice, and emits a calibration plot alongside the holdout metrics. Calibrated scores are essential for decisioning, pricing, and expected-loss aggregation.

Use case · Credit risk

Autonomous AI credit risk modeling — PD, LGD, EAD, calibrated and auditable.

Drop your loan tape or application data. OctOpus picks the right model family, applies monotonic constraints where the regulator demands them, calibrates probabilities, validates on out-of-time holdout, and emits a fully auditable train.py for your Model Risk Management team.

TL;DR. Credit risk is the most regulated tabular ML problem. OctOpus is built for it: monotonic-constrained GBMs, isotonic calibration, out-of-time holdout, SHAP attributions, and an audit log MRM can sign off on. Every experiment's training code is inspectable — no opaque AutoML pipeline blocks to defend in committee.

Credit risk problems OctOpus handles well

Probability of default (PD) — origination, behavioural, IRB.
Loss given default (LGD) — beta and tweedie objectives for bounded recovery rates.
Exposure at default (EAD) — credit conversion factor models with monotonic constraints.
Application scoring — origination decisioning with calibrated outputs and explainability.
IFRS 9 / CECL expected loss — PD x LGD x EAD with macroeconomic-conditioned scenarios.
Early-warning indicators — behavioural triggers, delinquency forecasting.

Models the agent rotates through

Tier	Family	When the agent picks it
1 · Baseline	LightGBM / CatBoost with monotonic constraints + isotonic calibration	Almost always — strong, fast, MRM-defensible.
2 · Tuned GBM	XGBoost with Optuna, monotonic-constrained, time-based CV	When tier 1 has headroom on out-of-time AUC / KS.
3 · Interpretable benchmark	Logistic regression with WOE binning	Regulatory comparison baseline — always reported alongside.
4 · Deep tabular	FT-Transformer, TabPFN (n<10k labels)	Small portfolios with rich categorical interactions.
5 · Stacking	Calibrated linear stacker over GBM + WOE-LR base learners	When residuals are uncorrelated and MRM accepts ensemble.

How a credit-risk run looks

Profile. Detects the loan tape structure, the time column, default rate, censoring, and monotonicity requirements from the role hint (e.g., "PD model for IRB").
Plan. Writes a research spec: AUC, KS, Brier score, expected calibration error as primary metrics. Out-of-time holdout. Monotonic constraint set per feature.
Run. Generates a fresh train.py per experiment, executes in sandbox, emits calibration plots and partial dependence per feature.
Diagnose. When something fails (monotonicity violation, calibration collapse, time leakage), the agent writes a targeted fix and retries.
Validate. Out-of-time holdout the LLM never sees — guards against temporal leakage and overfit.
Deploy. Scoring endpoint plus a deploy bundle for self-hosted inference inside your VPC. Every artifact is hashed and audit-logged.

What enterprise risk and MRM teams get back

Calibrated PD / LGD / EAD score per record with confidence bounds.
AUC, KS, Brier, expected calibration error on out-of-time holdout.
Partial dependence plots and SHAP attributions for monotonicity audit.
The exact train.py the agent wrote — fully inspectable for governance.
Audit log of every experiment, every diagnosis, every revision.
Deployable scoring endpoint, or a deploy bundle for your own inference stack.

Compliance and audit

OctOpus Enterprise is designed for SR 11-7-, IFRS 9-, CECL-, and Basel-aligned deployments. Every research run is fully audited and exportable. The Desktop app keeps the entire process on-prem; VPC deployment keeps it inside your cloud perimeter. See Enterprise for residency, SSO/SCIM, MRM-friendly audit log, and procurement details.

Score your loan tape free → See benchmarks Enterprise deployment