[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120478-en":3,"doc-seo-120478-105":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},120478,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Machine learning and Blockchain approaches for enhancing fraud prevention in financial transactions","Financial fraud undermines the integrity of digital financial systems as rule-based detection struggles to address rapidly evolving attack tactics. A hybrid solution is explored by integrating Machine Learning with Blockchain to strengthen fraud detection accuracy, transparency, and response efficiency. Using a publicly available dataset of over 2,500 transactions, supervised models including Random Forest and Support Vector Machine classify records as fraudulent or legitimate. Performance is assessed with accuracy, precision, recall, and F1-score, showing Random Forest achieving 99.9% across metrics, while blockchain supports secure immutable storage and real-time auditability for trustworthy, compliance-ready operations.","Open Access  \nEngineering Science & Technology Journal ISSN 2708-8944 (Print), ISSN 2708-8952 (Online) Fair East Publishers  \n[www.fepbl.com](www.fepbl.com)  \nMachine learning and Blockchain approaches for enhancing fraud prevention in financial transactions  \nRianat Abbas 1, Ajuwon Samuel2, Kumbirai Bernard Muhwati3, Sarah Mavire4, Enock Katenda5, & Adetomiwa Adesokan6  \n1Information Systems, Baylor University, Texas, USA  \n2Electrical and Computer Systems Engineering, Morgan State University, USA 3Computer Science, Yeshiva University, New York, USA  \n4Computer Science, Yeshiva University, New York, USA 5Computer Science, Yeshiva University, New York, USA 6Economics, University of Nevada, Reno, USA  \nCorresponding Author: Rianat Abbas  \nCorresponding Author Email: [rihanatoluwatosin@gmail.com](rihanatoluwatosin@gmail.com)  \nArticle Info  \nReceived: 20-04-25  \nAccepted: 27-06-25  \nPublished: 17-07-25  \nVolume: 6  \nIssue: 6  \nPage No: 296-312  \nLicensing Details:  \nAuthor retains the right of this article. The article is distributed under the terms of the Creative Commons Attribution Non  \nCommercial 4.0 Licence  \nAbstract  \nFinancial fraud continues to threaten the integrity of digital financial systems, with traditional rule-based detection methods increasingly ineffective against evolving tactics. Recent advances suggest that integrating Machine Learning (ML) and Blockchain Technology may provide a robust solution. This study explores how this hybrid approach can improve fraud detection in financial transactions by enhancing accuracy, transparency, and response efficiency. The study adopted a quantitative research design using a publicly available dataset of over 2,500 financial transactions. Features included transaction amounts, account behavior, login attempts, and timestamps. Supervised machine learning models—Random Forest and Support Vector Machine (SVM)—were applied to classify transactions as fraudulent or legitimate. The models were trained, optimized, and evaluated using metrics such as accuracy, precision, recall, and F1-score. Descriptive analysis revealed fraud accounted for only 6.8% of transactions, confirming significant class imbalance. The Random Forest model outperformed the SVM, achieving 99.9% accuracy, precision, recall, and F1-score. TransactionAmount, TransactionDuration, and CustomerOccupation were found to be the most influential predictors. The integration of blockchain was identified as vital for secure, immutable data storage, enabling real-time auditability and enhancing the trustworthiness of the machine learning process. The combination of machine learning’s predictive power with blockchain’s immutable  \nledger creates a highly effective fraud detection framework. Random Forest was identified as the superior model in terms of both performance and reliability for this application. Financial institutions should adopt integrated ML-blockchain systems to strengthen fraud prevention, ensure transaction transparency, and support regulatory compliance. This study contributes to the advancement of intelligent and secure financial systems by offering empirical evidence of the value of this hybrid approach.  \nKeywords: Fraud Detection, Machine Learning, Blockchain, Random Forest, Financial Transactions, Cybersecurity, Anomaly Detection.  \nDOI: 10.51594/estj.v6i6 .1971  \n DOI URL: [https://doi.org/10.51594/estj.v6i6.1971](https://doi.org/10.51594/estj.v6i6.1971)   \nINTRODUCTION  \nFinancial fraud is still a constant and changing menace in the online world, with yearly global losses totaling in the billions of dollars (Stapleton 2022) . Also, Njoku et al. (2024) avowed that traditional fraud detection methods such as rule-based systems and manual reviews find it difficult to keep up with sophisticated fraud techniques as monetary transactions move more to online platforms. In reaction, prominent tools for improving fraud detection include advanced technologies like Machine Learning and Blockchain, (Pranto ","cbCaigO5yd4ayOrP","https://ap.wps.com/l/cbCaigO5yd4ayOrP","pdf",925678,1,17,"English","en",105,"# Introduction\n## Machine Learning for fraud detection\n## Blockchain for transparency and security\n## Synergy of blockchain and artificial intelligence\n# Methodology and Data (based on study design)\n## Dataset and features\n## Supervised models and evaluation metrics\n## Results and influential predictors\n# Conclusion and Recommendations","[{\"question\":\"Why do traditional rule-based fraud detection methods become less effective over time?\",\"answer\":\"They struggle to keep up with increasingly sophisticated fraud tactics as transactions shift online, leading to reduced adaptability and higher false results.\"},{\"question\":\"Which machine learning models were used, and how were they evaluated?\",\"answer\":\"Random Forest and Support Vector Machine (SVM) classified transactions as fraudulent or legitimate. Models were trained and optimized, then evaluated using accuracy, precision, recall, and F1-score.\"},{\"question\":\"How does blockchain contribute to the fraud prevention framework?\",\"answer\":\"Blockchain provides an immutable, decentralized ledger that improves transparency and traceability, enabling real-time auditability and reducing the likelihood of data tampering affecting detection outcomes.\"}]","Machine learning and Blockchain approaches for enhancing fraud prevention in financial transactions | PDF",1785730287,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-and-blockchain-approaches-for-enhancing-fraud-prevention-in-financial-transactions","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-and-blockchain-approaches-for-enhancing-fraud-prevention-in-financial-transactions/120478/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do traditional rule-based fraud detection methods become less effective over time?","Question",{"text":75,"@type":76},"They struggle to keep up with increasingly sophisticated fraud tactics as transactions shift online, leading to reduced adaptability and higher false results.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models were used, and how were they evaluated?",{"text":80,"@type":76},"Random Forest and Support Vector Machine (SVM) classified transactions as fraudulent or legitimate. Models were trained and optimized, then evaluated using accuracy, precision, recall, and F1-score.",{"name":82,"@type":73,"acceptedAnswer":83},"How does blockchain contribute to the fraud prevention framework?",{"text":84,"@type":76},"Blockchain provides an immutable, decentralized ledger that improves transparency and traceability, enabling real-time auditability and reducing the likelihood of data tampering affecting detection outcomes.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]