Work

The usage of statistical analysis to predict credit card acceptance and income level

Year: 2025

Type: article

Abstract: This paper presents a statistical analysis of variables within a credit card approval dataset to predict approval decisions and income level of applicants. Descriptive analysis was performed on continuous variables and inferential statistical techniques, including confidence intervals, t-tests, chi-square tests, and ANOVA, were employed to predict credit card approval. Using a binary logistic regression model, a misclassification rate of 0.48% was achieved. As for predicting income level, Various models were developed and evaluated to identify the most effective approach. Findings indicate that Model 5, which incorporated data centering and adjusted reference levels for categorical variables, demonstrated superior performance by effectively mitigating multicollinearity, though it still had a low R2 of 20.8%, MAPE of 32.3% and RAE or 0.86.

Source: Zenodo (CERN European Organization for Nuclear Research)

Authors: Ali, Khalid Adnan, AbuIktish, Sara Hussien, Hasan, Isra Mohammad

Institution: American University of Sharjah

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W7084095633

FWCI:

Cites:

Cited by:

Related to: