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Assessing Credit Default Risk Using Logistic Regression. A Transparent Approach to Scoring with the UCI Dataset and SPSS

By Nabil Nakbi

Assessing Credit Default Risk Using Logistic Regression. A Transparent Approach to Scoring with the UCI Dataset and SPSS Law

Assessing Credit Default Risk Using Logistic Regression. A Transparent Approach to Scoring with the UCI Dataset and SPSS

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Description
Scientific Study from the year 2025 in the subject Economics - Finance, grade: 10.00, Mohammed V University at Agdal, course: Econométrie, language: English, abstract: 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.
Product details
Binding:
Paperback
Edition:
1
Number of Pages:
28
Release Date:
2025-09-12
Publication Date:
2025-09-12
Publisher:
GRIN Verlag
Languages:
Original: English
ISBN10:
338915759X
ISBN13:
9783389157596
GPSR Manufacturer Reference:
[email protected]
Weight:
56 g
Height:
148 cm
Width:
210 cm
Thickness:
3 cm
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