Financial Fraud Detection Using Hybrid Convolutional and Recurrent Neural Networks: An Analysis of Unstructured Data in Banking-Reference-Cited by-同舟云学术

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Financial Fraud Detection Using Hybrid Convolutional and Recurrent Neural Networks: An Analysis of Unstructured Data in Banking

Published:2024-04-12 Issue: Volume:27 Page:1027-1031
ISSN:
Container-title:2024 10th International Conference on Communication and Signal Processing (ICCSP)
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Author:

Banu Shaik Rehana¹,Gongada Taviti Naidu²,Santosh Kathari³,Chowdhary Harish⁴,Sabareesh R⁵,Muthuperumal S⁶

Affiliation:

1. Lincoln University College,Department of Business Management,Selangor,Malaysia

2. GITAM University,Department of Operations,Vishakapatnam,India

3. CMR Institute of Technology,Department of MBA,Bengaluru,India

4. National Forensic Sciences University,School of Doctoral Studies & Research,Gandhinagar,Gujarat

5. Fortuna Business Solutions (R& D),Skill Development & Employability Programs,Coimbatore

6. K.Ramakrishnan college of Engineering,Department of mathematics,Trichy,India,621112

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10543256/10543221/10543545.pdf?arnumber=10543545

Reference15 articles.

1. Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework

2. Financial fraud detection: the use of visualization techniques in credit card fraud and money laundering domains

3. Role of Artificial Intelligence in Financial Fraud Detection;Mohanty;Academy of Marketing Studies Journal,2023

4. Credit Card Fraud Detection for Contemporary Financial Management Using XGBoost-Driven Machine Learning and Data Augmentation Techniques

5. Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation

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