Real Time Credit Card Fraud Detection Using Fuzzy and Deep Neural Network

The financial fraud prediction is important research is needy one for current research.Because every minute's lakhs of fraud activities are happening in the worldwide.The different approaches are used to predict and analysis the credit card fraud activities.In credit card fraud prediction behaviour analysis is an important one.In this work, presented new prediction model based on the real time behaviour analysis and with the help of history transaction dataset.The behaviour analysis is performed based on the proactive data such as user typing speed of username and password, normal, abnormal activities and other properties.The proposed model prediction is performed based on the fuzzy logic and deep neural network system.The fuzzy logic used to check the behaviour of human and find the member activities.The deep neural network is used to identifying anomalous behaviour of credit card activities.For implementation, a real-time dataset from a commercial bank used static and dynamic features are used.The accuracy, sensitivity, and specificity metrics are used to measure the performance of proposed work.

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Publication Details

Journal
International Journal of Computer Applications
Published
2025-02-03
DOI
https://doi.org/10.5120/ijca2025924393
Citations
1
Primary Topic
Imbalanced Data Classification Techniques
Type
article
Field-Weighted Citation Impact
2.48
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article

Real Time Credit Card Fraud Detection Using Fuzzy and Deep Neural Network

N. Prabha, S. Manimekalai
1 citations
International Journal of Computer Applications
Imbalanced Data Classification Techniques
2.48
article

Real Time Credit Card Fraud Detection Using Fuzzy and Deep Neural Network

N. Prabha, S. Manimekalai
article en
1 citations

Abstract

The financial fraud prediction is important research is needy one for current research.Because every minute's lakhs of fraud activities are happening in the worldwide.The different approaches are used to predict and analysis the credit card fraud activities.In credit card fraud prediction behaviour analysis is an important one.In this work, presented new prediction model based on the real time behaviour analysis and with the help of history transaction dataset.The behaviour analysis is performed based on the proactive data such as user typing speed of username and password, normal, abnormal activities and other properties.The proposed model prediction is performed based on the fuzzy logic and deep neural network system.The fuzzy logic used to check the behaviour of human and find the member activities.The deep neural network is used to identifying anomalous behaviour of credit card activities.For implementation, a real-time dataset from a commercial bank used static and dynamic features are used.The accuracy, sensitivity, and specificity metrics are used to measure the performance of proposed work.

International Journal of Computer ApplicationsVol. 186(64)
Thiruvalluvar University (IN), Periyar Maniammai Institute of Science & Technology (IN)
Peace, Justice and strong institutions
Openalex Percentile: Top 12%
Imbalanced Data Classification Techniques
2.48
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