Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
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Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
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Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique
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Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
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QoS-aware Virtual Machine (VM) for Optimal Resource Utilization and Energy Conservation
Volume-3 | Issue-3
Probabilistic Neural Network based Managing Algorithm for Building Automation System
Volume-3 | Issue-4
Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach
Volume-3 | Issue-1
Multi-scale CNN Approach for Accurate Detection of Underwater Static Fish Image
Volume-3 | Issue-3
Real Time Anomaly Detection Techniques Using PySpark Frame Work
Volume-2 | Issue-1
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
Volume-3 | Issue-4
Frontiers of AI beyond 2030: Novel Perspectives
Volume-4 | Issue-4
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
Volume-3 | Issue-4
ARTIFICIAL INTELLIGENCE APPLICATION IN SMART WAREHOUSING ENVIRONMENT FOR AUTOMATED LOGISTICS
Volume-1 | Issue-2
Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert
Volume-3 | Issue-2
Volume - 3 | Issue - 2 | june 2021
Published
22 June, 2021
With the exponential increase in the usage of the internet, numerous organisations, including the financial industry, have operationalized online services. The massive financial losses occur as a result of the global growth in financial fraud. Henceforth, devising advanced financial fraud detection systems can actively detect the risks such as illegal transactions and irregular attacks. Over the recent years, these issues are tackled to a larger extent by means of data mining and machine learning techniques. However, in terms of unknown attack pattern identification, big data analytics and speed computation, several improvements must be performed in these techniques. The Deep Convolution Neural Network (DCNN) scheme based financial fraud detection scheme using deep learning algorithm is proposed in this paper. When large volume of data is involved, the detection accuracy can be enhanced by using this technique. The existing machine learning models, auto-encoder model and other deep learning models are compared with the proposed model to evaluate the performance by using a real-time credit card fraud dataset. Over a time duration of 45 seconds, a detection accuracy of 99% has been obtained by using the proposed model as observed in the experimental results.
KeywordsArtificial neural network deep learning financial fraud convolution neural network data mining
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