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All Journal Jurnal Akuntansi, Manajemen dan Bisnis Digital
Deki Fujiansyah
Universitas Serelo Lahat
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Analisis Default Kartu Kredit Dengan Deep Learning Untuk Mendukung Keputusan Manajemen Keuangan Digital Dini Pratiwi; Deki Fujiansyah
Jurnal Akuntansi, Manajemen dan Bisnis Digital Vol 5 No 2 (2026): April
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jambd.v5i2.10484

Abstract

The development of the digital economy requires financial institutions to optimize risk management through data-driven analysis. This study aims to analyze the factors influencing credit card default and to develop a predictive model using a Deep Learning algorithm based on an Artificial Neural Network (ANN) to support digital financial management decision-making. The data were obtained from the public “Default of Credit Card Clients” dataset (UCI/Kaggle), consisting of 30,000 observations and 23 financial variables. The results show that the model achieved an accuracy of 81.6% and an AUC value of 0.771, with high specificity but relatively low recall. These findings indicate that deep learning is effective in capturing non-linear patterns in customer payment behavior and can serve as a decision support tool for digital financial institutions in identifying credit risk and designing more adaptive default mitigation strategies.
Co-Authors Dini Pratiwi