ikimina-reliability-t11

=== BEGIN ===

---

language:

• en
• fr
• rw license: mit tags:
• fintech
• tabular-classification
• credit-scoring
• logistic-regression
• calibrated-classifier
• aims-ktt-hackathon
• ikimina
• rwanda library_name: scikit-learn pipeline_tag: tabular-classification

---

Ikimina Digital Trust & Reliability Index — T1.1

A calibrated Logistic Regression classifier that scores members of Rwandan Ikimina (rotating-savings groups) on a 0–100 reliability scale from 12 months of paper-record-derived contribution, penalty, and borrowing history. Submission for the AIMS KTT Fellowship Hackathon 2026, Tier 1 challenge T1.1.

Model summary

• Architecture: StandardScaler → LogisticRegression(L2, class_weight='balanced') wrapped in CalibratedClassifierCV(method='sigmoid', cv=5) (Platt scaling).
• Input: 12 engineered features (see below).
• Output: calibrated probability of default within 6 months; post-processed to an integer in [0, 100] via reliability = round((1 - P(default)) × 100).
• Tier bands (brief-mandated): 0–40 = high risk, 41–70 = watch, 71–100 = low risk.

Why Logistic Regression over XGBoost

The T1.1 brief's default label is logistic on four linear features (missed_count_total, late_penalty_unpaid, borrowed/repaid_ratio, tenure). A 5-fold CV diagnostic on the training set showed:

Logistic regression is the Bayes-optimal classifier for a logistically-generated label. Tree-based non-linearities don't exist in the ground-truth process — XGBoost memorised the 400 training rows without improving generalisation. LogReg is one of the three blessed model choices in the brief.

Features

Intended use

• Give a bank or microfinance institution (MFI) a calibrated trust signal derived from an Ikimina secretary's paper records.
• Delivered via USSD on a feature phone (*654*member_id#), with per-decision feature attribution for regulatory explainability.
• See the GitHub repo for the full reference USSD flow in Kinyarwanda + French, consent capture, and SS7 privacy analysis.

Not intended for: automated loan approval without human review; any use that transmits personally-identifiable information over the USSD or SS7 layer; any deployment without a native-Kinyarwanda speaker reviewing the SMS response templates.

Evaluation

Holdout: last 100 member_ids (12 actual positives). Training: first 400 member_ids.

Explainability

For any prediction, the scorer surfaces the top-3 features by coef × scaled_feature — the exact log-odds contribution of each feature. This is not a SHAP approximation; for a linear model, coefficients give the ground truth. A bank can print these contributions next to a decline decision to satisfy regulatory "explanation of adverse action" requirements.

Training data

Synthetic, generated from the T1.1 brief's seeded recipe (NumPy, seed 42):

• 500 members × 12 months of contribution / missed / penalty / borrowing history
• 40 groups × 6 attributes (size, avg contribution, founded year, district, urban flag)
• 500 binary labels (defaulted_within_6m) with ~14% positive rate
• AR(1) ρ=0.4 on latent monthly miss-rate deviation; balanced 12–13 members per group; 1 secretary + 1 treasurer enforced per group

Generator script + data regeneration instructions live in the GitHub repo.

Limitations

1. Tiny dataset. 400 training rows / 58 positives. Holdout AUC has ±0.13 std error. The reported 5-fold CV AUC is the more reliable ceiling.
2. Synthetic, not production. Real Ikimina records may have label noise, missing weeks, and reporting bias that this synthetic dataset doesn't capture.
3. Kinyarwanda not validated. The USSD response templates in ussd_flow.md are auto-translated. Must be reviewed by a native Kinyarwanda Umurenge SACCO speaker before deployment.
4. Single-point predictions. Model returns one score per (member, group). A real deployment would need time-series drift monitoring.
5. No PII handling. The scorer takes a bare integer member_id — any PII lookup/consent/privacy concerns are the caller's responsibility. The reference USSD flow documents the SS7 pseudonymisation model.

Quick start

import joblib
import pandas as pd

model = joblib.load("logreg_calibrated.joblib")

# Load feature order
import json
feature_order = json.load(open("feature_order.json"))

# `features_dict` is a dict keyed by feature name (see feature table above).
X = pd.DataFrame([[features_dict[f] for f in feature_order]], columns=feature_order)
p_default = model.predict_proba(X)[0, 1]
score = round((1 - p_default) * 100)
# tier: 0-40 high risk, 41-70 watch, 71-100 low risk

Full feature-construction pipeline is in the GitHub repo (features.py). Use build_single_feature_vector(member_record, group_record) for the canonical path.

Citation / source

• GitHub repo: (to be added after first push)
• Live demo: http://207.180.242.232:7860/ (Gradio UI, Docker-isolated)
• 4-minute walkthrough video: (to be added after recording)

Author

Ahmed Eldaw — AIMS KTT Fellowship Hackathon 2026 candidate

License

MIT

=== END ===