[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117993-en":3,"doc-seo-117993-105":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},117993,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Online Fraud Detection Using Machine Learning Approach","Online fraud detection addresses a growing threat to individuals, businesses, and financial institutions by enabling real-time identification and prevention of online extortion. The proposed machine-learning solution is implemented in Python and leverages chronicled transaction data, combining client behavior, exchange details, and financial information to build robust predictive models. Data preprocessing cleans and transforms inputs for model training, after which algorithms such as logistic regression, decision trees, random forests, and gradient boosting are used. The system is evaluated in a real online transaction environment, with performance continuously monitored and improved to adapt to evolving fraud patterns, supporting AML efforts and reducing financial loss and reputational damage.","Online Fraud Detection Using Machine Learning Approach  \nViswanatha V 1, Ramachandra A.C2, Deeksha V3 and Ranjitha R4  \n1Assistant Professor, Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology,  \nBangalore, INDIA  \n2Professor, Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore,  \nINDIA  \n3Student, Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore,  \nINDIA  \n4Student, Department of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bangalore,  \nINDIA  \n[1](1Corresponding Author: viswas779@gmail.com)[Corresponding Author: viswas779@gmail.com](1Corresponding Author: viswas779@gmail.com)  \nReceived: 04-07-2023 Revised: 20-07-2023 Accepted: 04-08-2023  \nABSTRACT  \nOnline extortion discovery has ended up a tremendous issue in today’s advanced age and poses a danger to individuals, businesses, and budgetary teachers all over the world. The increment in extortion illustrates the require for compelling extortion discovery, especially within the setting of anti-money laundering (AML) endeavors. This extent is planned to create a machine learning based arrangement utilizing Python to distinguish and avoid online extortion in genuine time.  \nThe proposed framework employment chronicled exchange information, combining different components such as client behavior, exchanges, and budgetary information. First, the information control prepare is utilized to clean the information and change over it into organize reasonable for the preparing show. At that point, different machine learning calculations such as calculated relapse, choice trees, irregular timberlands or angle boosting are used to build predictive algorithms that can spot fraud. The extended concludes with the usage of the created show in a genuine world online exchange environment, permitting for genuine time extortion location and avoidance. The system’s adequacy is persistently checked and assessed, and essential overhauls and advancements are made to adjust to advancing extortion designs and procedures. By and large, this extends points to supply a strong and proficient arrangement utilizing Python and machine learning strategies to combat online extortion. By precisely recognizing false exchanges in genuine time, this framework can altogether contribute to fortifying AML endeavors and ensuring people and organizations from money related misfortunes and reputational harm related with online extortion.  \nKeywords-- Unique Information Mining, Online Fraud Detection, Machine Learning, Decision Tree Algorithm  \nⅠ. INTRODUCTION  \nOnline extortion has become a predominant issue in today's advanced age. With the expanding dependence on innovation and the web for different exchanges, hoodlums have found modern and advanced ways to misdirect and dupe clueless people and businesses. The result is required for successful extortion discovery frameworks has ended up more vital than ever some time recently. One such structure has risen as effective apparatus in combating online extortion is online extortion detection[1]-[2]  \nOnline extortion discovery alludes to the utilize of progressed calculations and machine learning procedures to distinguish and anticipate false exercises happening over the web. It includes analyzing huge volumes of information in real-time to identify designs, inconsistencies, and suspicious behavior that will demonstrate false action. By leveraging fake insights and information analytics, online extortion location frameworks can rapidly distinguish potential fraudstersand take fitting activities to relieve the dangers related with online extortion [3]-[4] .  \nThe presentation of online extortion locations has revolutionized the way organizations approach extortion avoidance. Conventional strategies of extortion discovery, such as manual audits and rule-based frameworks, were re","cbCaioA2GtO1txLi","https://ap.wps.com/l/cbCaioA2GtO1txLi","pdf",913432,1,13,"English","en",105,"# Introduction\n## Concept of Online Fraud Detection\n## Importance and Limitations of Traditional Methods\n# Proposed Framework\n## Data Collection and Preprocessing\n## Machine Learning Algorithms\n# Real-Time Deployment and Evaluation","[{\"question\":\"What problem does the proposed work address?\",\"answer\":\"The work addresses online fraud/extortion, aiming to detect and prevent fraudulent activity in real time to reduce risks to individuals and organizations.\"},{\"question\":\"Which data sources are used in the proposed framework?\",\"answer\":\"The framework uses transaction-related datasets, including client behavior, exchange information, and financial information.\"},{\"question\":\"How does the system detect fraud in real time?\",\"answer\":\"After preprocessing the data into a model-ready form, machine learning algorithms such as logistic regression, decision trees, random forests, and gradient boosting generate predictive models that flag suspicious transactions during online processing.\"}]","Online Fraud Detection Using Machine Learning Approach | 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