Comparison of SVM and FSVM for predicting bank failures using chi-square feature selection

Z. Rustam, F. Nadhifa, M. Acar

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)


Bankruptcy doesn't happen suddenly, but there are early indications that can be seen by investigating the financial statement of a bank. In this research, we aim to find the best bankruptcy prediction model to give an early warning for regulators so that it can help them to prevent or lessen the negative effects on economic systems. We will be performing SVM and modification of SVM by adding fuzzy membership function called FSVM to analyze bank's health. We chose machine learning for bankruptcy prediction because it can give faster result rather than traditional statistical method. The prediction accuracy will be measured by using the dataset that consists of 65 Turkish banks of which each of them has an information of 20 financial ratios. Furthermore, to improve the accuracy prediction, we also perform chi-square feature selection (CSFS) to filter any irrelevant features of total 20 features in our dataset. CSFS can sort all 20 features based on chi-square score from the most relevant feature to the least one. After that, we will choose 5, 10, and 15 best features, so that we have four datasets to be classified into healthy and non-healthy banks. We found that using 5 features and SVM classifier gives the highest accuracy prediction, which scores 98.28%. For most cases, SVM gives better performance compared to FSVM.

Original languageEnglish
Article number012115
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 4 Dec 2018
Event2nd Mathematics, Informatics, Science and Education International Conference, MISEIC 2018 - Surabaya, Indonesia
Duration: 21 Jul 2018 → …


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