TY - JOUR ID - TI - Determination Efficient Classification Algorithm for Credit Card Owners: Comparative Study AU - Raghad A. Azeez PY - 2021 VL - 39 IS - 1part (B) Science SP - 21 EP - 29 JO - Engineering and Technology Journal مجلة الهندسة والتكنولوجيا SN - 16816900 24120758 AB - Today in the business world, significant loss can happen when theborrowers ignore paying their loans. Convenient credit-risk managementrepresents a necessity for lending institutions. In most times, some personsprefer to late their monthly payments, otherwise, they may face difficultiesin the loan payment process to the financial institution. Mainly, most fiscalorganizations are considered managed and refined client classificationsystems, scanning a valid client from invalid ones. This paper produces thedata mining idea, specifically the classification technique of data miningand builds a system of data mining process structure. The credit scoringproblem will be applied using the Taiwan bank dataset. Besides that, threeclassification methods are adopted, Naïve Bayesian, Decision Tree (C5.0),and Artificial Neural Network. These classifiers are implemented in theWEKA machine learning application. The results show that the C5.0algorithm is the best among them, it achieves 0.93 accuracy rates, 0.94detection rates, 0.96 precision rates, and 0.95 F-Measure which is higherthan Naïve Bayesian and Artificial Neural Network; also, the FalsePositive Rate in C5.0 algorithm achieves 0.1 which is less than ArtificialNeural Network and Naïve Bayesian

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