Academic journal article Informatica Economica

An Analysis of the Most Used Machine Learning Algorithms for Online Fraud Detection

Academic journal article Informatica Economica

An Analysis of the Most Used Machine Learning Algorithms for Online Fraud Detection

Article excerpt

(ProQuest: ... denotes formulae omitted.)

1 Introduction

During the last decades, the dependency on ecommerce and online payments has increasingly grown. As the area of information technology is developing every day to be better over the time, illegal attempts in online transactions have been increased worldwide and because of that most organizations and people are suffering substantial financial losses [1]. In the literature [2], fraud is defined as "the abuse of a profit organization system without necessarily leading to direct legal consequences".

Online bank fraud is continuously evolving and is difficult to analyze and detect because of the fraudulent behavior which is dynamic, spread across different customer profiles and dispersed in very large and dynamic datasets. Complex decision-making systems based on algorithms and analytical technologies have been developed. These can learn from previous experiences and create patterns that can detect proactively potentially fraudulent transactions.

Going through a number of important research studies within the last few years, this paper aims to provide a review of up-to-date techniques for fraud detection based on the most outstanding criteria:

* The algorithm should achieve high accuracy while processing large volumes of transaction data => high accuracy

* The algorithm should help to obtain high fraud coverage combined with low false positive rate => high coverage

* The algorithm should be useful for both the organizations and individual users in terms of cost and time efficiency => cost

The structure of the paper is divided as follows. The first part offers background over the machine-learning algorithms used in fraud detection highlighting the chosen criteria. The second part presents the methodology of the research and the classification of the various techniques used in fraud detection based on the defined criteria. Finally, the paper presents the research results and conclusions.

2 Background

Online banking fraud has become a serious issue in financial crime management for all bank institutions. It is becoming ever more challenging and leads to massive losses, due to the emergence and evolution of complex and innovative online banking fraud, such as phishing scams, malware infection and ghost websites. The detection of online banking fraud needs to be instant because it is very difficult to recover the loss if fraud is undiscovered during the detection period. Most customers usually rarely check their online banking history regularly and are therefore not able to discover and report fraud transactions immediately after an occurrence of fraud. This makes the possibility of loss recovery very low. In this context, online banking detection systems are expected to have high accuracy, high detection rate, and low false positive rate for generating a small, manageable number of alerts in complex online banking business. These characteristics greatly challenge existing fraud detection techniques for protecting credit card transactions, e-commerce, insurance, retail, telecommunication, computer intrusion, etc. These existing methods demonstrate poor performance in efficiency and/or accuracy when directly applied to online banking fraud detection [3]. For instance, credit card fraud detection often focuses on discovering particular behavior patterns of a specific customer or group, but fraud-related online banking transactions are very dynamic and appear very similar to genuine customer behavior. Some intrusion detection methods perform well in a dynamic computing environment, but they require a large amount of training data with complete attack logs as evidence. However, there is no obvious evidence to show whether an online banking transaction is fraudulent.

As stated in the work of Wei et al. (2013) [1], the essence of online fraud reflects the abuse of interaction between resources in three worlds:

* the fraudster's intelligence abuses in the social world,

* the abuse of web technology and Internet banking resources in the cyber world

* the abuse of trading tools and resources in the physical world. …

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