Exploratory Data Analysis for Credit Scoring with Python

2026-03-12 · Source: Towards Data Science · Field: Technology & Digital — Data Science & Analytics, Artificial Intelligence & Machine Learning, Banking & Financial Services · Depth: Novice, long

Summary

This article details a foundational step in credit scoring projects: understanding the data through descriptive analysis. It utilizes an open-source Credit Scoring Dataset from Kaggle, comprising 32,581 observations and 12 variables related to individual bank loans ranging from $500 to $35,000. The dataset includes contract characteristics (e.g., loan amount, interest rate, credit grade) and borrower characteristics (e.g., age, income, housing status), with "default" as the target variable. The analysis methodology involves statistically describing each variable, analyzing observation counts and default rates for categorical variables, and discretizing continuous variables into quartiles for similar analysis. Key findings include a 78% non-default rate, an imbalanced dataset, and the absence of temporal data, which limits dynamic risk analysis. The article also provides Python functions for automating this descriptive analysis and exporting results to Excel.

Key takeaway

For data scientists or analysts building credit risk models, prioritize comprehensive descriptive data analysis before jumping into modeling. Your initial exploration of variables like age, income, and previous default history will reveal critical risk indicators and data imbalances, such as the 78% non-default rate in the example dataset. Automate this process using provided Python functions to efficiently generate statistical summaries and identify key characteristics, saving time and ensuring robust model foundations.

Key insights

Thorough descriptive data analysis is crucial for understanding credit risk and identifying predictive variables before modeling.

Principles

• Past repayment behavior predicts future default.
• Higher income correlates with lower default risk.
• Loan purpose can indicate varying financial stability.

Method

Discretize continuous variables into quartiles, then analyze observation counts and default rates for each interval, similar to categorical variables. Automate this process using Python functions.

In practice

• Use `build_default_summary` for categorical variable analysis.
• Apply `discretize_variable_by_quartiles` for continuous variables.
• Export reports to Excel with `export_summary_to_excel`.

Topics

• Credit Risk Modeling
• Exploratory Data Analysis
• Data Preprocessing
• Feature Engineering
• Imbalanced Data

Best for: AI Student, Data Scientist, Data Analyst

Related on AIssential

• Building Robust Credit Scoring Models (Part 3) — Proper data splitting and preprocessing are crucial for building generalizable and stable credit scoring models.
• Getting Started with Exploratory Data Analysis (EDA) in Python: A Beginner’s Practical Guide — Exploratory Data Analysis (EDA) is crucial for understanding data's inherent story and quality before any advanced modeling.
• From Raw Data to Risk Classes — Effective variable categorization is crucial for building robust, interpretable, and stable credit scoring models.
• Statistical analysis recapitulates the development of statistical methods — Statistical analysis often recapitulates the historical development of methods, progressing from simple techniques to complex models incrementally.
• Data Presentation Over Architecture: Resampling Strategies for Credit Risk Prediction with Tabular Foundation Models — Context construction significantly impacts Tabular Foundation Model performance in imbalanced credit risk prediction.
• Starting Off on the Wrong Foot: Pitfalls in Data Preparation — Statistically informed data preparation significantly improves model robustness and efficiency for imbalanced, real-world datasets.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Towards Data Science.