PocketQuant — Merchant Liquidity Shortage Prediction
End-to-end ML system predicting merchant 48-hour liquidity shortages using XGBoost with high precision/recall, FastAPI prediction engine, and Streamlit dashboard.
Problem and Solution Breakdown
🎯 The Engineering Problem
Merchant liquidity defaults happen suddenly in high-volume transaction environments, while severe class imbalance (1.29% positive rate) blinds standard ML classifiers.
💡 Architecture Solution
Developed a cost-sensitive XGBoost prediction model with custom liquidity buffer ratios, SHAP feature attributions, and an operational risk threshold (0.40).
System Architecture & Data Pipeline
End-to-End Quantitative Risk Prediction & SHAP Model Attribution System
50k Merchant-Day Records
Feature engineering (liquidity_buffer_ratio)
XGBoost Classifier
Handling 76.5:1 class imbalance (0.844 F1)
SHAP Feature Attribution
Feature contribution & fairness validation
FastAPI + Streamlit
Operational threshold (0.40) risk dashboard
Key Features & Measured Impact
- ✦Predicts 48-hour merchant liquidity shortage (`liquidity_shortage_next_48h`) on highly imbalanced data (1.29% rate).
- ✦Tuned XGBoost classifier achieving 0.9956 accuracy, 0.844 F1 score, and 0.9991 ROC-AUC.
- ✦Includes SHAP model explainability, operational risk threshold analysis (0.40), FastAPI service, and Streamlit dashboard.
📊 Measured Outcome:
Achieved 0.9956 accuracy, 0.844 F1 score, and 0.9991 ROC-AUC on 50k merchant-day records with a production FastAPI serving microservice.
🛠️Technologies & Frameworks
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