Machine Learning-Based Detection of Credit Card Fraud: A Comparative Study

Zainab Khamees Alkhateeb,Abeer Tariq Maolood

doi:10.3844/ajeassp.2019.535.542

Zainab Khamees Alkhateeb, Abeer Tariq Maolood

Open Access

https://doi.org/10.3844/ajeassp.2019.535.542

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Abstract

One of the fastest-growing problems with a high impact on the financial sector is financial fraud. Recently, data mining has been identified as one of the effective ways of detecting fraudulent credit card transactions. As a data mining problem, the detection of fraudulent credit card transaction is a challenging task due to the following reasons: (i) The frequent changes in the patterns of normal and fraudulent activities and (ii) the high level of skewness related with credit card fraud datasets. The aim of this article is to review the existing techniques for fraudulent transactions detection in credit cards, with more focus on the techniques that are Machine Learning (ML) based and nature inspired-based. The recent trend in the detection of credit card fraud was also presented in this article. Furthermore, the limitations and usefulness of the existing techniques for fraudulent transaction detection in credit cards were also outlined. The necessary fundamental information for further studies in this area was also provided. This review will also guide individuals and financial institutions seeking for effective techniques for credit card fraud detection, especially those that are based on ML and nature-inspired algorithms.

Highlights

The progression of the existing technology and worldwide communication has resulted in an increased rate of fraudulent activities (Halvaiee and Akbari, 2014)
-It can handle non-linear classification issues such as fraud detection. -It requires low computational power and minimal training, thereby suitable for real-time application. -Easy to use and understand. - It requires low computational power and minimal training, thereby suitable for real-time application
-Suitable for other binary classification problems. -Ideal for real-time application due to its high computational efficiency. - implementable using classification accuracy as the fitness functions. -Suitable for other binary classification problems. -Easy to implement. -The visual nature of the results can be understood by auditors. -Suitable for tasks that are associated with data imbalance, for instance, fraud detection. -Can adapt to new types of fraud as it combined the advantages of several conventional methods

Summary

Introduction

The progression of the existing technology and worldwide communication has resulted in an increased rate of fraudulent activities (Halvaiee and Akbari, 2014). Fraud can be curbed by either preventing or detecting its occurrence. The aim is to prevent the occurrence of fraudulent activities on the data. Fraud detection involves the identification of fraudulent activity and triggering the required response as soon as the activity perpetrated. This implies that detection is the second line of defense (triggered when prevention has failed). It is, important to ensure that detection is always enabled since it may not be possible to predict when a given protection technique will fail (Michael and Pedro, 2009; Adrian, 2015). The pattern and characteristics of normal and suspicious financial transactions can be determined using data processing techniques supported by expert knowledge of normal and abnormal behaviors (Shukur, 2019)

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