This paper aims to show how business intelligence can be applied in the credit card approval process. More specifically, the paper investigates how information like an applicant’s age, credit score, debt, income, and prior default can be used in credit card approval prediction.The dataset used for analysis is a publicly available dataset from the UCI machine learning repository. Logistic regression is used to make a prediction model with a reasonable number of attributes for a comprehensible business model. The Chi-square test of independence is used to test the dependence of credit card approval results with attributes. Research uncovers that prior default is supposed to be the most important attribute in the approval process. Finally, the authors propose several visualizations that could help make smarter decisions with effective credit risk assessment.

In this study we investigate the level of adoption of internet banking in Bosnia and Herzegovina across gender, age group and education levels. Data is collected true the google forms questioner. We use descriptive statistics and inferential statistics to test out hypothesis in SPSS 22. We find out that some of the main reasons of not adopting internet in providing some of the banking services are security issue and that clients do not find reason for use of internet banking. Study suggests that significantly lower transaction cost and faster transaction process are important reason for increase in internet banking adoption by clients in Bosnia and Herzegovina. Also, we find out that null hypothesis regarding distribution of internet banking adoption across gender, age group and education level cannot be rejected.

The main objective of this research is to measure the efficiency of commercial banks operating in the Federation of Bosnia and Herzegovina in the period 2016-2017. An analysis is conducted of over 12 banks that had positive overall profit lost at the end of 2016 and 2017 years published by the Banking Agency of Federation of Bosnia and Herzegovina. Data Envelopment Analysis (DEA) method with two input and three output parameters is used for efficiency measurement. Each bank’s efficiency is presented for the 2016 and 2017 years. For an observed period, large banks showed more efficiency than small banks. Based on the results shown in this research and features used in this model there is a significant difference in the relative efficiency of the top two banks and the rest of the 10 banks.

Growing problem of card payment fraudulent abuse is a main focus of banks and payment Service Providers (PSPs). This study is using naive Bayes, C4.5 decision tree and bagging ensemble machine learning algorithms to predict outcome of regular and fraud transactions. Performance of algorithms is evaluated through: precision, recall, PRC area rates. Performance of machine learning algorithms PRC rates between 0,999 and 1,000 expressing that these algorithms are quite good in distinguishing binary class 0 in our data set. Amongst all algorithms best performing PRC class 1 rate has Bagging with C4.5 decision tree as base learner with rate of 0,825. For prediction of fraud transactions with success of 92,74% correctly predicted with C4.5 decision tree algorithm.

Credit card default payment prediction studies are very important for any nancial institution dealing with credit cards. The purpose of this work is to evaluate the performance of machine learning methods on credit card default payment prediction using logistic regression, C4.5 decision tree, support vector machines (SVM), naive Bayes, k-nearest neighbors algorithms (k-NN) and ensemble learning methods voting, bagging and boosting. The performance of the algorithms is evaluated through following performance metrics: accuracy, sensitivity and specicity. The best result among all algorithms for overall accuracy rate was achieved by logistic regression model with a rate of 0.820. The best performing model for default credit card customer detection, with success of 71,3% was naive Bayes model. This approach could improve and ease the process of credit card default, and therefore help the banking system in decision making.