Explainable ML for Indian lending — your data, your weights, your audit trail.
A 5-model fraud ensemble and a calibrated probability-of-default model, with full feature attribution per decision.
CortexData's ML decisioning is built around a single principle: every decision has to be defensible to your credit committee, RBI, and the Banking Ombudsman. We've engineered the stack — feature engineering, ensemble architecture, calibration, attribution, drift monitoring — to deliver predictive lift without losing explainability.
- 5-model fraud ensemble (XGBoost / LightGBM / RF / GB / Isolation Forest)
- Calibrated probability-of-default model with isotonic regression
- 32 hand-engineered features tuned for CIBIL / CRIF / Experian / Equifax
- Per-decision feature attribution stored in immutable audit chain
- Trained on your data, on your infrastructure — never leaves your perimeter
- PSI / CSI drift monitoring with automated retraining gates
- 015-model fraud ensemble (XGBoost / LightGBM / RF / GB / Isolation Forest)
- 02Calibrated probability-of-default model with isotonic regression
- 0332 hand-engineered features tuned for CIBIL / CRIF / Experian / Equifax
- 04Per-decision feature attribution stored in immutable audit chain
Predictive lift you can defend.
Most ML lending platforms promise 'AI-powered'. We promise 'audit-ready'. The architecture, features, and evaluation harness are designed around what an RBI inspector or your credit committee will actually ask.
5-model fraud ensemble
Weighted voting: XGBoost (30%), LightGBM (30%), Random Forest (20%), Gradient Boosting (15%), Isolation Forest (5%). Each contributes complementary signal. Ensemble outperforms any single model on Indian fraud patterns.
Calibrated PD model
Probability-of-default model with isotonic regression calibration — so a 4.2% PD output means actually-4.2% empirical default in your portfolio. Critical for risk-based pricing and IRACP provisioning math.
Per-decision attribution
Every approve/reject/refer decision returns the top-N features that drove it, with their weights. Defensible to your credit committee, RBI inspection, and the Banking Ombudsman.
32 banking-tuned features
Feature engineering tuned for Indian credit bureaus (CIBIL, CRIF, Experian, Equifax): bureau score, DPD bands, credit-utilisation, enquiries, vintage, write-offs, settled, employment tenure, FOIR, LTI, plus device, velocity, address, and email-phone-risk signals.
Hybrid: scorecard + ML
WOE-based scorecard (banker-readable, regulator-friendly) layered with ML PD model (predictive lift). Either alone is workable. Together they give you regulatory defensibility AND model performance.
Anomaly detection
Isolation Forest for outlier detection on the 3-sigma rule across application velocity, device risk, address consistency. Flags applications that don't match any prior pattern — even ones the labelled ensemble hasn't seen.
Retraining pipeline
End-to-end retraining: data versioning, feature store, train/validate/test split, threshold optimisation via F1, model registry, A/B-deployable versions. Your bank's data, your training cadence, your control.
Drift detection
Population stability index (PSI) on inputs, characteristic stability index (CSI) on features, and rank-order stability on scores. Drift alerts gate model promotion automatically.
From application to decision, end to end.
Frequently asked questions
See decision attribution on your portfolio.
Bring us a labelled sample (approved + rejected, last 12 months). We'll train the ensemble on your data and show you per-decision feature attribution alongside your current process — side by side.