CNN-Bidirectional LSTM based Approach for Financial Fraud Detection and Prevention System
subject: Bidirectional Long Short-Term Memory (BiLSTM); Kernel Principal Component Analysis (KPCA); Normalization
description: Detecting fraudulent activity has become a pressing issue in the ever-expanding realm of financial services, which is vital to ensuring a positive ecosystem for everyone involved. Traditional approaches to fraud detection typically rely on rule-based algorithms or manually pick a subset of attributes to perform prediction. Yet, users have complex interactions and always display a wealth of information when using financial services. These data provide a sizable Multiview network that is underutilized by standard approaches. The proposed method solves this problem by first cleaning and normalizing the data, then using Kernel principal component analysis to extract features, and finally using these features to train a model with CNN-BiLS TM, a neural network architecture that combines the best parts of the Bidirectional Long Short-Term Memory (BiLS TM) network and the Convolution Neural Network (CNN). BiLSTM makes better use of how text fits into time by looking at both the historical context and the context of what came after. 2023 IEEE.
creator: Madhusudhana Reddy N.; Sharada K.A.; Pilli D.; Paranthaman R.N.; Reddy K.S.; Chauhan A.
source: International Conference on Sustainable Computing and Smart Systems, ICSCSS 2023 - Proceedings, pp. 541-546.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2023-01-01
rights: Restricted Access
relation: ISBN: 979-835033360-2
format: Online
language: English
type: Conference paper
coverage: Madhusudhana Reddy N., Rajeev Gandhi Memorial College of Engineering and Technology, Department of Cse, Andhra Pradesh, Nandyal, India; Sharada K.A., Hkbk College of Engineering, Department of Cse, Karnataka, Bangalore, India; Pilli D., Koneru Lakshmaiah Educational Foundation, Mba Department, Andhra Pradesh, India; Paranthaman R.N., School of Computing, College of Engineering and Technology, Srm Institute of Science and Technology, Department of Networking and Communications, Tamilnadu, India; Reddy K.S., Prakasam Engineering College, Department of Computer Science and Engineering, Andhra Pradesh, Kandukur, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India