feat: the UdyamLinc ML layer — scoring, explanation and quantile forecast
Brings the Python service under version control for the first time. It has
been running and serving the demo since 23 August; until now it existed only
on one developer's disk.
Contents:
training/build_dataset.py point-in-time join over the feature store, with
leakage assertions. dscr_p10 and dscr_p50 are
excluded deliberately — both are derived from
the forecast, which is derived from the
instalment, so the model would read its own
answer.
training/train_stress.py LightGBM vs XGBoost vs two single-feature
baselines, expanding-window validation over
as-of dates, publishes to the model_metric table
training/train_forecast.py three quantile heads (P10/P50/P90), horizon as a
feature rather than one model per horizon
serving/explain.py SHAP TreeExplainer folded into the five risk
families the console already speaks. The family
map mirrors RuleBasedScoringStrategy in the API
repo; where they disagree the rule engine wins.
serving/app.py /score, /score/batch, /forecast, /health, /model
The trained models, their metrics and the training dataset are committed
alongside the code. Without them the published AUC of 0.760 is a claim nobody
else can check; at ~2.2 MB together they are worth the space.
Measured, on 5,523 rows / 263 enterprises / 242 positives / 100% synthetic
labels: LightGBM AUC-ROC 0.7599, and the emi_to_inflow baseline 0.7685 on the
same folds. The baseline wins. That is recorded rather than tuned away — on
generated labels, beating it would mostly prove the model had learned the
generator.
The quantile forecast beats the deterministic strategy on all three quantiles
(pinball loss, 12-33% better) but flags every borrower as breaching, so the
deployment runs udyam.forecast.engine=DETERMINISTIC and this model is held
back pending calibration against real repayment outcomes.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
This commit is contained in:
LincPay
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{
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"rows": 30711,
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"enterprises": 263,
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"as_of_months": 22,
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"horizons": 6,
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"validation": "expanding-window time-series split over as-of months",
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"feature_order": [
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"feed_cost_change",
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"output_price_change",
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"margin_squeeze",
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"price_z_score",
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"rainfall_anomaly",
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"heat_anomaly",
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"cash_flow_change",
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"emi_delay",
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"inventory_days",
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"feed_staleness_days",
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"days_since_last_entry",
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"emi_to_inflow",
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"feed_cost_change__missing",
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"output_price_change__missing",
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"margin_squeeze__missing",
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"price_z_score__missing",
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"rainfall_anomaly__missing",
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"heat_anomaly__missing",
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"cash_flow_change__missing",
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"emi_delay__missing",
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"inventory_days__missing",
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"feed_staleness_days__missing",
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"days_since_last_entry__missing",
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"emi_to_inflow__missing",
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"trail_mean",
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"trail_sd",
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"trail_min",
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"trail_inflow",
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"trail_months",
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"emi",
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"horizon",
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"sector_id",
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"district_id"
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],
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"quantiles": {
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"p10": {
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"alpha": 0.1,
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"pinball_model": 3150.78,
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"pinball_deterministic": 4182.0
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},
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"p50": {
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"alpha": 0.5,
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"pinball_model": 6194.72,
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"pinball_deterministic": 7061.24
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},
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"p90": {
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"alpha": 0.9,
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"pinball_model": 2770.14,
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"pinball_deterministic": 4112.53
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}
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},
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"verdict": "3/3 quantiles beat the deterministic strategy on held-out months",
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"trainedAt": "2026-08-24T11:07:50.265843"
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}
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{
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"dataset": {
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"rows": 5523,
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"positives": 242,
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"positive_rate": 0.0438,
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"as_of_dates": 21,
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"synthetic_label_share": 1.0
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},
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"validation": "expanding-window time-series split over as-of dates",
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"feature_order": [
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"feed_cost_change",
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"output_price_change",
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"margin_squeeze",
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"price_z_score",
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"rainfall_anomaly",
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"heat_anomaly",
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"cash_flow_change",
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"emi_delay",
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"inventory_days",
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"upi_inflow_change",
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"feed_staleness_days",
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"days_since_last_entry",
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"emi_to_inflow",
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"feed_cost_change__missing",
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"output_price_change__missing",
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"margin_squeeze__missing",
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"price_z_score__missing",
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"rainfall_anomaly__missing",
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"heat_anomaly__missing",
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"cash_flow_change__missing",
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"emi_delay__missing",
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"inventory_days__missing",
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"upi_inflow_change__missing",
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"feed_staleness_days__missing",
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"days_since_last_entry__missing",
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"emi_to_inflow__missing",
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"dpd_at_t",
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"outstanding_at_t",
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"emi_at_t",
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"sector_id",
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"district_id"
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],
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"algorithms": {
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"lightgbm": {
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"folds": [
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{
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"n": 789,
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"positives": 38,
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"auc_roc": 0.6814422874763473,
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"pr_auc": 0.12090605567917119,
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"brier": 0.0690301962610968,
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"red_flagged": 23,
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"precision_red": 0.17391304347826086,
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"recall_red": 0.10526315789473684,
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"recall_flagged": 0.3684210526315789,
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"train_to": "2025-03-31",
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"test_from": "2025-04-30"
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},
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{
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"n": 789,
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"positives": 31,
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"auc_roc": 0.7962805345135756,
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"pr_auc": 0.17203736204143655,
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"brier": 0.08691410549130126,
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"red_flagged": 54,
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"precision_red": 0.14814814814814814,
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"recall_red": 0.25806451612903225,
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"recall_flagged": 0.6129032258064516,
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"train_to": "2025-06-30",
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"test_from": "2025-07-31"
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},
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{
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"n": 789,
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"positives": 39,
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"auc_roc": 0.8423931623931624,
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"pr_auc": 0.22591607572996394,
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"brier": 0.11095460084002903,
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"red_flagged": 90,
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"precision_red": 0.2222222222222222,
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"recall_red": 0.5128205128205128,
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"recall_flagged": 0.8205128205128205,
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"train_to": "2025-09-30",
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"test_from": "2025-10-31"
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},
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{
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"n": 1052,
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"positives": 51,
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"auc_roc": 0.7193982488100136,
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"pr_auc": 0.20698077930278122,
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"brier": 0.08188506640426521,
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"red_flagged": 59,
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"precision_red": 0.22033898305084745,
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"recall_red": 0.2549019607843137,
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"recall_flagged": 0.43137254901960786,
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"train_to": "2025-12-31",
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"test_from": "2026-01-31"
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}
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],
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"pooled": {
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"auc_roc": 0.7599,
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"pr_auc": 0.1815,
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"brier": 0.0872,
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"precision_red": 0.1912,
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"recall_red": 0.2828,
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"recall_flagged": 0.5583
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}
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},
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"xgboost": {
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"folds": [
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{
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"n": 789,
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"positives": 38,
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"auc_roc": 0.6397084588969094,
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"pr_auc": 0.10510222243195182,
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"brier": 0.055753882974386215,
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"red_flagged": 13,
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"precision_red": 0.15384615384615385,
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"recall_red": 0.05263157894736842,
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"recall_flagged": 0.15789473684210525,
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"train_to": "2025-03-31",
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"test_from": "2025-04-30"
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},
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{
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"n": 789,
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"positives": 31,
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"auc_roc": 0.7543195165546004,
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"pr_auc": 0.14310881976856776,
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"brier": 0.05676882714033127,
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"red_flagged": 24,
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"precision_red": 0.20833333333333334,
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"recall_red": 0.16129032258064516,
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"recall_flagged": 0.3870967741935484,
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"train_to": "2025-06-30",
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"test_from": "2025-07-31"
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},
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{
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"n": 789,
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"positives": 39,
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"auc_roc": 0.8195897435897437,
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"pr_auc": 0.17177662573810526,
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"brier": 0.0742882490158081,
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"red_flagged": 41,
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"recall_red": 0.20512820512820512,
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"recall_flagged": 0.5641025641025641,
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"train_to": "2025-09-30",
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"test_from": "2025-10-31"
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},
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{
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"n": 1052,
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"positives": 51,
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"auc_roc": 0.7052947052947053,
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"pr_auc": 0.1285152676436765,
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"brier": 0.06149039790034294,
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"red_flagged": 32,
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"precision_red": 0.125,
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"recall_red": 0.0784313725490196,
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"recall_flagged": 0.27450980392156865,
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"train_to": "2025-12-31",
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"test_from": "2026-01-31"
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}
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],
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"pooled": {
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"auc_roc": 0.7297,
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"pr_auc": 0.1371,
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"brier": 0.0621,
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"precision_red": 0.1706,
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"recall_red": 0.1244,
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"recall_flagged": 0.3459
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}
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}
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},
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"caveats": [
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"Feature rows are reconstructed by fact date, not knowledge date \u2014 the archive was ingested in one pass, so a past row uses a value a live run of that date would not yet have had.",
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"Labels are generated DPD history (SEEDED_HISTORY). Metrics computed on this dataset measure the generator as much as the borrowers, and must be reported as computed on synthetic history.",
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"dscr_p10 and dscr_p50 are excluded: both read the current forecast at every as-of date."
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],
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"baselines": {
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"repayment_history_only": {
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"auc_roc": 0.6181,
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"pr_auc": 0.0869,
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"precision_red": 0.11
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},
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"obligation_ratio_only": {
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"auc_roc": 0.7685,
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"pr_auc": 0.1828,
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"precision_red": 0.1001
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}
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},
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"winner": "lightgbm",
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"base_rate": 0.0438
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}
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