Project Details

Banking Credit Risk & Customer Behavior Analysis

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Project Overview

This project is an end-to-end analytical solution focused on assessing customer credit risk and detecting fraudulent transaction behavior within a financial context. It integrates two complementary datasets: the UCI Credit Card Default dataset for customer-level risk analysis and the European Credit Card Transactions dataset for fraud detection. A structured SQL-based data architecture was designed to transform raw data into clean, analysis-ready datasets through a staging and core layer approach, ensuring data integrity, reproducibility, and auditability.

The analysis focuses on understanding customer repayment behavior, credit utilization patterns, and transaction-level anomalies to identify high-risk segments and potential loss exposures. Advanced data validation, deduplication, and feature standardization techniques were applied to ensure high data quality. The project enables financial institutions to make data-driven decisions by translating raw financial data into meaningful insights, supporting improved credit approval strategies, risk segmentation, and fraud monitoring.

Key Features

End-to-end data pipeline: raw data → staging → core analytical layer
Dual-dataset integration (credit risk + fraud detection)
Advanced data validation and quality checks (NULLs, duplicates, ranges)
Deterministic deduplication using SQL window functions
Business-friendly data modeling with standardized variables
Feature engineering for risk indicators and behavioral metrics
Performance optimization using indexing strategies
Export-ready datasets for BI and advanced analytics tools

Tools and Technologies Used

This project was implemented using MySQL for data storage, transformation, and analysis. SQL was used extensively for data cleaning, validation, feature engineering, and performance optimization, including advanced techniques such as window functions and indexing. Data ingestion was performed using MySQL Workbench. The final analytical datasets were prepared for visualization and reporting in tools such as Power BI. The overall workflow follows a structured data engineering and analytics pipeline aligned with industry best practices.

Results and Outcomes

The project successfully produced clean, structured, and analysis-ready datasets for both customer-level and transaction-level analysis. It identified and resolved key data quality issues, including duplicate transactions and inconsistencies in customer records. The analysis enabled segmentation of customers based on credit risk and highlighted behavioral patterns linked to potential default. Additionally, fraud-related transaction patterns were isolated within a highly imbalanced dataset, supporting more effective risk monitoring. Overall, the solution provides a strong foundation for predictive modeling, risk scoring, and business intelligence reporting.

Role and Responsibilities

I designed and implemented the complete data architecture and analytical workflow for this project. My responsibilities included building the SQL database, creating staging and core data models, and performing comprehensive data cleaning and validation. I developed transformation logic, implemented deduplication strategies, and optimized performance through indexing. Additionally, I structured the datasets for downstream analysis, defined key analytical objectives, and translated business requirements into technical solutions. The entire pipeline was developed with a focus on scalability, accuracy, and alignment with real-world financial analytics use cases.

Documents

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Documents

Banking_Credit_Risk_&_Customer_Behaviour_End-to-End_Analysis Report.docx Document