An Ameliorated method for Fraud Detection using Complex Generative Model: Variational Autoencoder
Kaithekuzhical Leena Kurien1, Ajeet Chikkamannur2
1Ms. Kaithekuzhical Leena Kurien, Department of Computer Science and Engineering, R.L Jalappa Institute of Technology, VTU, Bengaluru (Karnataka), India.
2Dr. Ajeet Chikkamannur Prof & HOD, Department of Computer Science and Engineering, R.L Jalappa Institute of Technology, VTU, Bengaluru (Karnataka), India.
Manuscript received on 04 December 2019 | Revised Manuscript received on 12 December 2019 | Manuscript Published on 31 December 2019 | PP: 262-268 | Volume-9 Issue-2S December 2019 | Retrieval Number: B10051292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1005.1292S19
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Perpetrating fraud for financial gain is a known phenomenon, in this fast-growing adoption of smart phones and increased internet penetration, embracing digital technology. Evolution of financial transactions over the years, from paper currency to electronic media, leading the way in the form of credit cards or interbank electronic transactions. Consumers trending towards e-commerce hasn’t deterred criminals, but considered this as the opportunity to make money through defrauding methods. Criminals are rapidly improving their fraud abilities. The current Supervised and Unsupervised Machine Learning Algorithm approaches to the discovery of fraud are their inability to learn and explore all possible information representation. The proposed system, VAE based fraud detection, which uses a variational autoencoder for predicting and detecting of fraud detection. The VAE based fraud detection model consists of three major layers, an encoder, a decoder and a fraud detector element. The VAE-based fraud detection model is capable of learning latent variable probabilistic models by optimizing the average value of the information observed. The fraud detector uses the latent representations obtained from the variational autoencoder to classify whether transactions are fraud or not. The model is applied on real time credit card fraud dataset. The experimental results show that, implemented model perform better than supervised Logistic Regression, unsupervised Autoencoders or Random Forest ensemble model.
Keywords: Fraud Detection, Credit Card, Machine Learning, Generative Models, Variational Autoencoder.
Scope of the Article: Encryption Methods and Tools