Credit platform for
smart decisions
Predicts the probability of default based on a customer's financial features. All variables are anonymized for privacy following the AMEX Kaggle dataset guidelines.
Risk Calculator
Enter normalized customer metrics (features anonymized for privacy)
Feature Analysis
Temporal Aggregation
Our model relies on a 12-month historical window of customer behavior. Rather than using raw transaction logs, we aggregated data over 3, 6, and 12-month intervals. This allows the model to detect both recent changes in financial health (e.g., a sudden spike in spending) and long-term stability (e.g., consistent repayment over a year).
Deterministic vs. Stochastic
The input features (payment history, balance ratios) are deterministic—they are fixed historical facts. However, the target variable (future default) is stochastic. The model estimates the probability of this uncertain future event based on the known behavioral patterns extracted from the historical data.
Feature Categories
The 700+ engineered features are derived from five key categories of anonymized financial variables:
- D_*DelinquencyIndicators of past past-due payments or negative status.
- S_*SpendTransaction volumes and spending behavior patterns.
- P_*PaymentRepayment history and maturity scores.
- B_*BalanceCurrent balance amounts and credit utilization ratios.
- R_*RiskInternal risk estimates and aggregated risk signals.
Project Overview
Credit default prediction is a task that has characteristics of high dimensionality, class imbalance, scale, privacy constraints, and financial impact. In the lending industry today, transaction data is being generated at a massive scale, comes in anonymized forms, and is critical for risk management. The ability to process this vast amount of historical records and generate accurate default probabilities is a fundamental capability for modern lending institutions.
My project goal, which is to build a robust risk assessment system, relies heavily on the ability to engineer features from and model these large datasets. Credit risk modeling involves training algorithms with millions of customer snapshots to be able to forecast delinquencies and make lending decisions without manual review of every case. The higher the quality of features engineered into the XGBoost models, the more reliable the predictions are because their accuracy is dependent on the signal extracted from 12-month historical windows to be able to distinguish safe borrowers from risky ones. Also, the volume of digital transactions in the economy has increased and the complexity of financial behavior on these payment networks has increased accordingly. To be able to protect lenders and borrowers alike relies on the ability to interpret the signals they generate in their transaction history and that history is also labelled big data.
In conclusion, automated credit scoring is a technology that will continue to evolve in the financial sector. Building this mastery now is definitely required to deploying successful machine learning solutions and better securing the financial future.
Model Interpretation (SHAP)
The chart below shows the top features driving the model's decisions.
Red bars indicate features where higher values increase risk.
Green bars indicate features where higher values decrease risk (safer).