[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122094-en":3,"doc-seo-122094-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122094,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Credit card fraud detection with advanced graph based machine learning techniques","Credit card fraud detection faces rapidly evolving, more sophisticated attack methods, increasing financial losses over time. The research introduces a framework that combines bipartite graph visualization with advanced machine learning to evaluate how effectively a random forest classifier identifies fraudulent credit card transactions. Model predictions are mapped onto transaction bipartite graphs to improve interpretability and expose hidden relational patterns. The approach supports actionable insights for detection success and targeted improvements through network-aware analysis and graph-based anomaly detection, enabling analysts and stakeholders to fine-tune model parameters and strengthen fraud prevention.","Indonesian Journal of Electrical Engineering and Computer Science  \nVol. 35, No. 3, September 2024, pp. 1963∼ 1975  \nISSN: 2502-4752, DOI: 10.11591/ijeecs.v35.i3.pp1963-1975 ❒ 1963  \nCredit card fraud detection with advanced graph based machine learning techniques  \nKrishna Kumari Renganathan1 , Janaki Karuppiah2 , Mahimairaj Pathinathan3 ,  \nSudharani Raghuraman4  \n1Career Development Centre, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chennai, India  \n2Department of Mathematics, Saveetha Engineering College, Saveetha Nagar, Thandalam, Chennai, India  \n3Department of Mathematics, Loyola College, Chennai, India  \n4Department of Mathematics, Panimalar Engineering College, Chennai, India  \nArticle Info ABSTRACT  \nArticle history:  \nReceived Feb 12, 2024 Revised Apr 8, 2024 Accepted May 12, 2024  \nKeywords:  \nBipartite graphs Machine learning Random forest classifier  \nCorresponding Author:  \nIn the realm of credit card fraud detection, the landscape is continually evolving, demanding innovative approaches to stay ahead of increasingly sophisticated fraudulent activities. Our research pioneers a groundbreaking methodology that amalgamates the power of bipartite graph visualization with advanced machine learning techniques. This fusion yields a comprehensive framework capable of effectively evaluating the efficacy of a random forest classifier in uncovering fraudulent credit card transactions. Our study showcases the compelling application of this methodology, offering a paradigm shift in how we analyze and understand credit card fraud detection systems. By seamlessly integrating machine learning algorithms with network analysis, we provide a holistic view of the data, unveiling intricate patterns hidden within. Atthe heart of our approach lies the innovative use of bipartite graphs, which serve asa dynamic visual bridge between model predictions and real-world outcomes. This visual representation not only enhances interpretability but also facilitates a deeper understanding of the classifier’s performance. By visually mapping the relationships between transactions and their respective classifications, our methodology offers actionable insights into both successful detection and potential areas for improvement. Empowering analysts and stakeholders, our approach facilitates informed decisionmaking by enabling them to fine-tune model parameters and enhance the overall effectiveness of fraud detection systems. Through this synergy between cutting-edge machine learning and network analysis techniques, we provide a powerful tool to combat the critical challenge of credit card fraud prevention. Step into the future of fraud detection with our innovative methodology, where every transaction is scrutinized with precision, and where security is not just a possibility, but a promise fulfilled.  \nThis is an open access article under the CC BY-SA license.  \nKrishna Kumari Renganathan  \nCareer Development Centre, College of Engineering and Technology SRM Institute of Science and Technology  \nSRM Nagar, Kattankulathur-603203, Chennai, Tamilnadu, India  \nEmail: [krishrengan@gmail.com](krishrengan@gmail.com)  \n1. INTRODUCTION  \nCredit card fraud remains a significant challenge in today’s financial landscape, with its impact amounting to billions of dollars each year [1] . As fraudulent techniques evolve and become increasingly sophisticated,  \nthe financial losses attributed to such activities have steadily risen over the past decade, as indicated by the FDS Annual Fraud Report of 2023 . Addressing this challenge requires innovative approaches, and one promising avenue involves the fusion of machine learning techniques with bipartite graph visualization [2] . Machine learning algorithms have garnered attention for their efficacy in identifying fraudulent transactions [3], [4] . However, several challenges hinder their performance, including skewed datasets [5],[6] and concept drift [7","cbCaipWVvOMBm7ni","https://ap.wps.com/l/cbCaipWVvOMBm7ni","pdf",705958,1,13,"English","en",105,"# Introduction\n## Credit card fraud challenge and evolving techniques\n## Machine learning and dataset issues\n## Bipartite graph visualization for transactional relationships\n# Methodology and Framework\n## Mapping model predictions onto bipartite graphs\n## Graph metrics and unsupervised anomaly detection\n## Identifying high-risk communities and fraud patterns\n# Outcomes and Implications\n## Improved interpretability and actionable insights\n## Model tuning to strengthen fraud prevention","[{\"question\":\"What main technique does the paper use for credit card fraud detection?\",\"answer\":\"It combines bipartite graph visualization with advanced machine learning, using a random forest classifier to evaluate fraudulent credit card transactions.\"},{\"question\":\"How do bipartite graphs contribute to interpreting fraud results?\",\"answer\":\"The method maps model predictions onto transaction bipartite graphs, visually linking transactions and their classifications to reveal complex relational patterns.\"},{\"question\":\"What additional detection capability is incorporated beyond supervised classification?\",\"answer\":\"The framework applies unsupervised anomaly detection on graph metrics to identify surges in suspicious activities and emerging threats.\"}]","Credit card fraud detection with advanced graph based machine learning techniques | 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