PIG E. BANK RISK MODELING

This project explores how predictive analytics can safeguard lending operations by identifying fraudulent behavior, reducing risk, and enhancing decision-making in the banking sector.

🔍 Project Overview

Pig E. Bank’s goal was to strengthen its risk management system by analyzing historical transaction and customer data to predict high-risk behaviors. Using Python and machine learning, this project aimed to develop an effective fraud detection model that could classify potential risks before damage occurred.

✅ Objective

To build a predictive model that flags risky loan applications and fraudulent accounts using transaction patterns and customer behavior.

🚩 Problem Statement

Pig E. Bank faced growing fraud-related losses due to insufficient early-warning systems. They needed a data-driven approach to proactively detect anomalies in transaction and profile data before approving loans or account activity.

🧠 Hypothesis

Customers flagged with specific transactional patterns (e.g., sudden high withdrawals, irregular deposits, or multiple accounts) are more likely to be involved in fraud or default, and machine learning can learn to detect such patterns effectively.

📊 Key Questions Explored

  • Can we build a model that accurately classifies high-risk customers?

  • What features (age, transaction type, location) are most predictive of fraud?

  • How do true positives and false positives affect decision-making?

  • Which algorithm (logistic regression, random forest, etc.) performs best?

Customer Exit by Age Group

The highest number of customer exits occurred in the 46–65 age range, representing a significant risk segment. Retention strategies should focus on providing targeted financial products and personalized support for older demographics.

Decision Tree – Exit Risk Prediction

The decision tree reveals that account activity is the most significant predictor of customer exit. Inactive users were far more likely to churn. Among active customers, those above age 50 with only one product and low engagement scores were also at high risk. These insights can help Pig E. Bank implement early intervention strategies, such as targeted loyalty programs or multi-product engagement campaigns, to retain customers before they exit.

Key Findings

  • Fraudulent behavior is often signaled by specific transaction patterns and demographic combinations.

  • Machine learning models significantly outperform manual rule-based detection.

  • Reducing false positives is crucial to avoid unnecessary customer restrictions.

💡 Recommendations

  • Deploy the random forest model in the pre-approval stage to flag high-risk applicants.

  • Monitor flagged transactions in real time to prevent fraudulent withdrawals.

  • Educate customers about safe banking practices through targeted communication.

  • Periodically retrain the model to adapt to evolving fraud tactics.

Ready to Make Data-Driven Decisions?

Whether it's detecting risk, improving customer retention, or building smarter systems —
this project shows the power of combining analytics with action.