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All Journal Asia Pacific Fraud Journal
Nadya Intan Mustika
PT. Adira Dinamika Multi Finance, Tbk.
Economics, Econometrics & Finance Social Sciences

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Machine Learning Algorithms in Fraud Detection: Case Study on Retail Consumer Financing Company Nadya Intan Mustika; Bagus Nenda; Dona Ramadhan
Asia Pacific Fraud Journal Vol 6, No 2: Volume 6, No. 2nd Edition (July-December 2021)
Publisher : Association of Certified Fraud Examiners Indonesia Chapter

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21532/apfjournal.v6i2.216

Abstract

This study aims to implement a machine learning algorithm in detecting fraud based on historical data set in a retail consumer financing company. The outcome of machine learning is used as samples for the fraud detection team. Data analysis is performed through data processing, feature selection, hold-on methods, and accuracy testing. There are five machine learning methods applied in this study: Logistic Regression, K-Nearest Neighbor (KNN), Decision Tree, Random Forest, and Support Vector Machine (SVM). Historical data are divided into two groups: training data and test data. The results show that the Random Forest algorithm has the highest accuracy with a training score of 0.994999 and a test score of 0.745437. This means that the Random Forest algorithm is the most accurate method for detecting fraud. Further research is suggested to add more predictor variables to increase the accuracy value and apply this method to different financial institutions and different industries.
Co-Authors Bagus Nenda Dona Ramadhan