Bank plays a big role on economic system as they significantly contribute through facilitation of business. Hence, the collapse of several banks can cause a huge damage on financial systems not only in a country but also globally. Nonetheless, 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 willbe performing two supervised based machine learnings such as Support Vector Machines (SVM) and modification of SVM by adding fuzzy membership function called Fuzzy Support Vector Machines (FSVM). The experiment will also be using kernel RBF and kernel polynomial for both methods. We chose machine learning for bankruptcy prediction because it can give faster result rather than traditional statistical method. We will be measuring prediction accuracy using dataset that consist of 65 Turkish bank from the annual publication “Banks in Turkey†issued by the Banks Association of Turkey (BAT). Each of 65 banks that we collected from 1997—2004 has information of total of 20 financial ratios with six feature groups based on CAMELS rating system. 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 classified into healthy and non-healthy bank. We found that using 5 features gives the highest accuracy prediction, which scores 98.28%, among the others. For most cases, SVM gives better performance compared to FSVM.