A Novel Method for Detection of Fraudulent Bank Transactions using Multi-Layer Neural Networks with Adaptive Learning Rate

Document Type : Research Paper


Department of Electrical and Computer Engineering, Mahdishahr Branch, Islamic Azad University, Mahdishahr, Iran.


Fraud refers to earn wealth including property, goods and services through immoral and non-legal channels. Fraud has always been in action and experiences an increasing trend worldwide. Fraud in financial transactions not only leads to losing huge financial resources, but also leads to reduction in trust of customers on using modern banking systems and hence, reduction in efficiency of the systems and optimal management of financial transactions. In recent years, by emerging new tech- nologies of banking industry, new means of fraud are discovered. Although a new information system carry advantages and benefits, new opportunities are made for fraudsters. The applications of fraud detection methods encompasses detection of frauds in an organization, analysis of frauds and also user/customer behavior analytics in order to predict future behavior and reduce the fraud risks. In recent decades, employing new technologies in management of banking transactions has risen. Banks and financial institutions inevitably migrated from traditional banking to modern online banking to provide effective services. Although, the use of online banking systems improves the management of financial processes and speeds up services to customers of institutions, but some issues would also be carried. Financial frauds is one of the issues which organizations seek to prevent and reduce effects. In this paper, a novel machine learning based model is presented to detect fraud in electronic banking transactions using profile data of bank customers. In the proposed method, transactional data from banks are leveraged and a multi-layer perceptron neural network with adaptive learning rate is trained to prove the validity of a transaction and hence, improve the fraud detection in elec- tronic banking. The proposed method shows promising results compared with logistic regression and support vector machines.


Volume 11, Issue 2
December 2020
Pages 437-445
  • Receive Date: 24 August 2019
  • Revise Date: 09 August 2020
  • Accept Date: 21 September 2020