Credit risk management is central to the stability and profitability of financial institutions. This study applies binary logistic regression to a real-world dataset of credit card clients to identify predictors of loan default. Using SPSS for statistical modeling, we evaluated the contribution of demographic and financial variables including credit limit, past bill amounts, and repayment history. The model achieved an overall classification accuracy of 81.2%, with strong predictive power for recent payment behavior.
The most important things were the ones that made payments late in the last three months (PAY_0, PAY_2, PAY_3). The model calibration isn't perfect, but the results do give useful information on how to find borrowers who are likely to default. Logistic regression is a useful tool for risk analysts because it is easy to understand and see through. This is especially true in regulated environments where it is important for models to be clear. These results support the use of data-driven credit scoring models in decision-making processes.
Table of Contents
- Abstract
- 1. Introduction
- 2. Literature Review
- 2.1 Overview of Existing Credit Scoring Methods
- 2.2 Justification for Logistic Regression in Binary Classification Tasks
- 2.3 Role of Tools Like SPSS in Credit Risk Analysis for Business Users
- 2.4 Gaps or Inconsistencies in Current Approaches
- 3. Materials and Methods
- 4. Results and Discussion
- 6. Conclusion
- References
Objective & Thematic Focus
This study applies binary logistic regression to a real-world dataset of credit card clients to identify predictors of loan default, aiming for a transparent approach to scoring. The core research question is whether a simple logistic regression, supported by clear data and meticulous analysis, can accurately identify clients likely to default.
- Credit risk management and financial stability
- Application of binary logistic regression for default prediction
- Transparency and interpretability of credit scoring models
- Role of statistical software (SPSS) in data analysis for business users
- Evaluation of demographic and financial variables as predictors of default
- Identification of gaps in current credit scoring approaches
Excerpt from the Book
Justification for Logistic Regression in Binary Classification Tasks
Logistic regression is a direct and effective way to predict whether something will happen or not, like when a client defaults on a loan. Logistic regression is made for binary classification, while linear models work best for continuous outcomes. It doesn't put the data in a framework that doesn't work. Instead, it respects the outcome variable's nature and gives results that are both valid and easy to understand.
In linear regression, the values that are predicted can be less than zero or more than one. This is a problem when trying to model probabilities, which should always be between zero and one. Logistic regression gets around this problem by changing the response using the log odds function. This change makes an S-shaped curve that shows how likely an event is to happen as the predictor variables change.
Take the example of a client's income in a credit scoring context. If income increases, we might expect the likelihood of default to decrease. Logistic regression does not assume a fixed drop or rise but calculates how each unit of increase in income changes the odds of default. If the model returns a coefficient of minus 0.5 for income, the odds ratio is exp(minus 0.5), which is approximately 0.61. This means that for each increase of one unit in income, the odds of default decrease by 39 percent, assuming other variables remain constant.
One of the greatest strengths of logistic regression is that this kind of interpretation is possible for each variable. Age, employment status, or loan amount can all be included, and the model will estimate how each contributes to the outcome. The coefficients are not abstract; they tell a story that managers and analysts can follow.
According to Hosmer, Lemeshow, and Sturdivant (2013), the model's diagnostic tools are another benefit. Analysts can determine the statistical significance of variables, the adequacy of the model in fitting the data, and the potential undue influence of specific observations. Deviance residuals, for instance, help find places where the model has trouble. This may lead to a reexamination of the data, which could improve accuracy or help you understand special cases better.
Chapter Summaries
1. Introduction: This chapter introduces the critical importance of predicting loan defaults for financial stability and positions logistic regression as a transparent method, outlining the study's objective to identify key client characteristics influencing default risk.
2. Literature Review: This section provides an overview of existing credit scoring methods, including logistic regression, LDA, decision trees, neural networks, and k-NN, emphasizing logistic regression's strength in interpretability.
2.1 Overview of Existing Credit Scoring Methods: This subsection discusses various statistical models used for consumer credit scoring, highlighting the trade-off between interpretability and predictive power, drawing significantly from Hand and Henley (1997).
2.2 Justification for Logistic Regression in Binary Classification Tasks: This part explains why logistic regression is a suitable and effective method for binary classification problems like loan default, detailing its interpretable coefficients, odds ratios, and robust diagnostic tools.
2.3 Role of Tools Like SPSS in Credit Risk Analysis for Business Users: This section illustrates how SPSS simplifies the process of applying logistic regression for credit risk evaluation, covering practical aspects like data preparation, running analyses, and interpreting model diagnostics for non-data scientists.
2.4 Gaps or Inconsistencies in Current Approaches: This subsection critiques modern machine learning models for credit scoring, highlighting their "black box" nature and lack of transparency, which poses challenges in regulated financial industries and for auditing purposes.
3. Materials and Methods: This chapter details the methodology, including the use of the UCI Credit Card dataset, data preparation steps, and the specification of the binary logistic regression model in SPSS.
4. Results and Discussion: This section presents the empirical findings, starting with a baseline model, identifying significant predictors of default, and evaluating the full model's performance, fit, and calibration.
6. Conclusion: This chapter reaffirms logistic regression as a valuable and interpretable tool for credit risk assessment, summarizing the key behavioral predictors of default identified and discussing practical implications for risk management and the model's limitations.
Keywords
Credit risk, Logistic regression, Default prediction, SPSS, Credit scoring, Banking analytics, Risk management, Binary classification, UCI dataset, Financial stability, Loan default, Predictive modeling, Interpretability, Data analysis, Machine learning
Frequently Asked Questions
What is this work fundamentally about?
This work fundamentally assesses credit default risk using binary logistic regression, focusing on a transparent approach to scoring with real-world credit card client data and SPSS.
What are the central thematic fields?
The central thematic fields include credit risk management, loan default prediction, binary logistic regression modeling, credit scoring methodologies, and the application of statistical software like SPSS in banking analytics.
What is the primary objective or research question?
The primary objective is to identify which client characteristics most influence the risk of loan default. The main research question is whether a simple logistic regression, supported by clear data and meticulous analysis, can precisely identify clients likely to default.
Which scientific method is used?
The primary scientific method used is binary logistic regression, applied for classification tasks to predict a binary outcome (default or no default).
What is covered in the main part?
The main part of the work covers a literature review of credit scoring methods, the justification for using logistic regression, the role of SPSS in data preparation and analysis, and a detailed presentation of the model's results and discussion, including predictor significance and model fit.
Which keywords characterize the work?
Key keywords characterizing the work are Credit risk, Logistic regression, Default prediction, SPSS, Credit scoring, Banking analytics, and Risk management.
Why is interpretability a key advantage of logistic regression in credit scoring?
Interpretability is a key advantage because it allows analysts and financial institutions to understand exactly which factors influence default probability through directly interpretable coefficients and odds ratios, which is crucial for explaining decisions to clients, auditors, and regulators.
What dataset was used for this study and what variables did it include?
The study used the UCI Credit Card dataset, which includes 30,000 observations of credit card clients in Taiwan, with variables such as age, gender, education, marital status, credit limit, past payment records, and monthly billing and payment history.
What were the strongest predictors of default identified by the model?
The strongest predictors of default identified were recent repayment status (PAY_0, PAY_2, PAY_3), credit limit (LIMIT_BAL), and gender (SEX (Male)), with repayment behavior being the most influential.
What are some identified limitations or areas for improvement of the model?
Some limitations include the model's calibration issue (as indicated by the Hosmer-Lemeshow test) and relatively low R² values, suggesting that the model could be improved by adding more features or trying different methods to explain more variance.
- Citation du texte
- Nabil Nakbi (Auteur), 2025, Assessing Credit Default Risk Using Logistic Regression. A Transparent Approach to Scoring with the UCI Dataset and SPSS, Munich, GRIN Verlag, https://www.grin.com/document/1618055
