DairyGuard Core 1 — Procurement Integrity Isolation Forest
Part of DairyGuard, a fraud detection system for milk procurement in Maharashtra, built for Smart India Hackathon 2026 by team MoneyFollows.py.
Model Description
An IsolationForest (scikit-learn) trained to detect multivariate anomalies in dairy procurement transactions — specifically volume spikes, quality/fat dilution, historical-baseline drift, and temperature anomalies. Duplicate slips and OCR/manual-entry mismatches are handled by separate rule-based and semantic-matching components (not this model).
- Algorithm: Isolation Forest
- n_estimators: 200
- contamination: 0.0127 (calibrated to the true rate of numerically-detectable fraud in the training set, not an arbitrary default)
- Features (species-normalized z-scores):
volume_liters_zscorefat_pct_zscoreph_zscoretemperature_c_zscore
Features are z-scored within each animal species group (cow/buffalo/goat) before training, since raw fat% and yield differ substantially by species (e.g. buffalo milk fat ~6-7% vs cow ~3.5%) — without this normalization, natural species variation swamps the fraud signal.
Per-animal yield/fat ranges used for simulation are grounded in: Ministry of Fisheries, Animal Husbandry & Dairying, Government of India — Basic Animal Husbandry Statistics 2022 / Integrated Sample Survey (cow yield 3.44–13.49 kg/day depending on breed; buffalo yield 10–16 L/day for Murrah breed; buffalo fat 6–7% vs cow fat 3.5%).
Intended Use
Diagnostic/suggestion-only anomaly scoring for milk procurement transaction review. Not intended for automated remediation or auto-rejection of transactions — flagged records are meant for human review by procurement staff.
Training Data
Trained on a synthetic procurement transaction dataset (~15,340 rows), since no public dataset exists with farmer-level daily milk procurement transactions (this data is operationally sensitive and not published by dairy cooperatives or government bodies). The synthetic generator is calibrated against real data sources:
- Maharashtra Dairy Development Department district-wise monthly procurement volumes (data.gov.in, Open Government Data Platform India)
- 18th Livestock Census, Maharashtra — district-wise cattle/buffalo/goat population counts, used to set realistic species distribution per district
- Veterinary-standard fat%/pH/yield ranges per species
Performance
Evaluated only against numerically-detectable fraud types (volume spikes, quality dilution, baseline deviation, temperature anomalies) — duplicate slips and OCR mismatches are excluded from this evaluation since they don't alter any of the 4 input features and are caught by separate detection layers.
| Operating point | Precision (fraud) | Recall (fraud) |
|---|
| Strict/final (top 0.8% most anomalous, rescored after all fraud injected) | 0.73 | 0.39 |
| Earlier default (contamination=0.0127, before capacity-mismatch fraud added) | 0.33 | 0.33 |
Support: 15,110 normal / 230 fraud rows (final dataset, includes capacity-mismatch as a 5th detectable fraud type).
Why unsupervised, not supervised: We deliberately kept this as an unsupervised Isolation Forest rather than switching to a supervised classifier (which we benchmarked at ~92% precision / 78% recall via 5-fold CV on this synthetic data). The reason: real dairy cooperatives don't have large volumes of confirmed, labeled fraud cases to train on — that's precisely why no public procurement-fraud dataset exists. A supervised model's higher score here mostly reflects it learning our own synthetic injection patterns, which wouldn't generalize to real, previously-unseen fraud tactics. Isolation Forest requires no fraud labels at all, making it the realistic choice for cold-start deployment. A supervised model becomes viable as a phase-2 upgrade once real confirmed fraud cases accumulate post-deployment.
Honest limitation: Fraud detection on subtle, human-mimicked anomalies is a genuinely hard problem — 33% recall at default settings reflects real difficulty separating fraud from natural farmer-to-farmer variation at this data scale, not an implementation flaw. In production, this is one signal among several (paired with rule-based duplicate detection, mass-balance reconciliation, semantic OCR matching, and network-based collusion scoring), not a standalone gate.
How to Use
1import joblib
2import numpy as np
3
4model = joblib.load("isolation_forest_model.joblib")
5
6# Input must be species-normalized z-scores in this order:
7# [volume_liters_zscore, fat_pct_zscore, ph_zscore, temperature_c_zscore]
8sample = np.array([[2.1, -1.8, 0.3, 3.2]])
9
10prediction = model.predict(sample) # -1 = anomaly, 1 = normal
11score = model.decision_function(sample) # lower = more anomalous
Limitations
- Trained on synthetic data — real-world deployment requires recalibration on actual procurement records
- Cannot detect duplicate slips or OCR/text-based mismatches (out of scope for this model by design)
- Contamination rate is tuned to this specific synthetic dataset's fraud injection rate; should be re-tuned for production data
- Diagnostic tool only — flagged predictions require human review, not automated action
Part of DairyGuard Core 1
This model is one of three components in the Procurement Integrity Engine:
- Isolation Forest (this model) — multivariate volume/quality/temperature anomaly detection
- Hugging Face Semantic Text Matcher (
sentence-transformers/all-MiniLM-L6-v2) — OCR vs manual slip ID matching
- Mass-Balance Reconciliation — rule-based inflow/outflow variance checks
Plus supporting rule-based checks for duplicate slip detection and NetworkX-based collector collusion risk scoring.
