Credit Card Default Prediction: An Empirical Analysis on Predictive Performance Using Statistical and Machine Learning Methods
Problem
Predicting which credit card holders will default, and whether machine learning beats traditional statistical methods, using Taiwan UCI data.
Why it matters
Credit defaults drive major losses for lenders, so better prediction improves risk management.
What the AI did
Six models (LDA, logistic regression, SVM, XGBoost, random forest, DNN) were compared on 30,000 credit card users using accuracy, sensitivity, specificity, precision, G-mean, F1, ROC and AUC.
Results
ML methods outperformed traditional statistical methods on F1 score, G-mean and AUC; dataset had 6,636 defaults vs 23,364 non-defaults.
Impact
Evidence supports the comparative finding, though the abstract gives no specific metric values.
Record
| Venue | Journal of risk and financial management |
|---|---|
| Published | 2025 |
| Work type | article |
| Authors | Rakshith Bhandary, Bidyut Kumar Ghosh |
| Citations | 25 (OpenAlex, at import time) |
| References | 59 |
| Model or method | SVM, XGBoost, random forest, deep neural network |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/jrfm18010023 |
| Open access | hybrid |
| Keywords | credit default, machine learning, XGBoost, prediction, finance, benchmark comparison |
Importance rating
real but narrower advance
Default prediction aids lender risk management, but abstract only compares models on one dataset without stating losses or impact.
More in Business & Industry
| # | Paper | Year | Citations | Importance |
|---|---|---|---|---|
| 1 | Optimizing fraud detection in financial transactions with machine learning and imbalance mitigation | 2024 | 66 |
7
|
| 2 | BloombergGPT: A Large Language Model for Finance | 2023 | 289 |
6
|
| 3 | Search Personalization Using Machine Learning | 2020 | 167 |
6
|
| 4 | Combinatorial Optimization-Enriched Machine Learning to Solve the Dynamic Vehicle Routing Problem with Time Windows | 2024 | 69 |
6
|
| 5 | A review of the applications of multi-agent reinforcement learning in smart factories | 2022 | 76 |
6
|