Abstract
Today in the business world, significant loss can happen when the borrowers ignore paying their loans. Convenient credit-risk management represents a necessity for lending institutions. In most times, some persons prefer to late their monthly payments, otherwise, they may face difficulties in the loan payment process to the financial institution. Mainly, most fiscal organizations are considered managed and refined client classification systems, scanning a valid client from invalid ones. This paper produces the data mining idea, specifically the classification technique of data mining and builds a system of data mining process structure. The credit scoring problem will be applied using the Taiwan bank dataset. Besides that, three classification methods are adopted, Naïve Bayesian, Decision Tree (C5.0), and Artificial Neural Network. These classifiers are implemented in the WEKA machine learning application. The results show that the C5.0 algorithm is the best among them, it achieves 0.93 accuracy rates, 0.94 detection rates, 0.96 precision rates, and 0.95 F-Measure which is higher than Naïve Bayesian and Artificial Neural Network; also, the False Positive Rate in C5.0 algorithm achieves 0.1 which is less than Artificial Neural Network and Naïve Bayesian.
Highlights
Credit card fraud is a growing problem that affects cardholders around the world
The experiments are implemented using knowledge analysis WEKA data mining tool; it is an open gate Java-based program containing a different set of machine learning algorithms for data mining functions
WEKA only processes dataset in AttributeRelation File Format (ARFF) format
Summary
The credit models include the methods that are called today the techniques of data mining. Classifications are one of the most common goals in the online-based transactional activities that used in data mining techniques and it applied in the domain of credit models to predict the default probabilities of credit holders. Many techniques, such as the nearest neighbor method, decision trees, neural networks, and. Credit card fraud detection has been known as the process of identifying whether transactions are genuine or fraudulent [1]. Data mining is nominated enforcement of particular algorithms that extracting features from data as the following processes: 1) Extract features from the data, 2) Prepare and Preprocess the data, 3) Select the data, 4) Clean the data, 5) And the association of suitable preceding knowledge [3]
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