Empirical Analysis of Machine Learning Algorithms on Detection of Fraudulent Electronic Fund Transfer Transactions

Abstract

Online financial systems like electronic funds transfers (EFT) are heavily dependent on the public Internet. A diverse set of Internet traffic is generated from the use of different online financial platforms. The analysis of raw Internet traffic fails to identify any anomalous behavior in financial transactions. A similar result is obtained when we apply application layer anomaly detection in financial transactions. In this article, we propose a machine learning-based multi-layer framework which will detect and classify anomalous financial transactions. The proposed framework can help a financial service provider to avoid incidents like intrusions and online frauds. It also provides a secure mechanism to detect network anomalies in financial transactions to augment the credibility of such online financial platforms or gateways.

KEYWORDS:

• Cyber security
• Electronic funds transfer
• Financial security
• Fraud detection
• Intrusion detection
• Machine learning

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Notes

1 http://spark.apache.org

2 http://cassandra.apache.org

3 http://kafka.apache.org

4 http://www.acl.com

5 https://powerbi.microsoft.com/en-us/

6 https://www.tableau.com/

Additional information

Funding

This work was supported by Higher Education Commission (HEC), Pakistan: [Grant Number NCCS-NED].

Notes on contributors

A. Asad Arfeen

A Asad Arfeen is an assistant professor at the Department of Computer and Information Systems Engineering of NED University of Engineering & Technology, Karachi Pakistan. He completed his PhD from the University of Canterbury, New Zealand, in 2015. He has been holder of REANNZ Planet Lab New Zealand scholarship and Battersby Trimble award for advancement of computing in New Zealand. As a principal investigator Asad has won various competitive research grants from the Higher Education Commission of Pakistan. He has also won a 118 million PKR competitive research grant for establishing the National Research Centre for Cyber Security in Pakistan. He is also currently heading the IT Department and Network Operations Centre of NED University of Engineering & Technology, Karachi, Pakistan. Dr Asad has extensive research collaborations with the WAND research group of the University of Waikato, New Zealand, and the DFKI German research centre for Artificial Intelligence.

B. Muhammad Asim Khan

B Muhammad Asim Khan is currently a research associate at National Center for Cyber Security at NED University of Engineering & Technology, Karachi Pakistan. He completed his Bachelors of Engineering in computer and information system in 2018 from NED University of Engineering & Technology, Karachi Pakistan. He has published a Dataset on Harvard Dataverse. He has carried out and published several researches in the field of datascience, cyber security and ML. He loves making use of his information and knowledge in information technology, datascience, data analysis, and visualization to assist the world in turning out to be a better place. Email: [email protected]