[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119394-en":3,"doc-seo-119394-105":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},119394,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Machine Learning Algorithms and Visualization Techniques for Financial Anomaly Detection - Bachelor’s Thesis","This bachelor’s thesis analyzes financial anomalies, focusing on suspicious bank transactions, using supervised and unsupervised machine learning models. The study evaluates how different models identify unusual transaction patterns and determine the most effective approaches for fraud detection. Supervised techniques include KNN, Decision Tree, Random Forest, SVM, and Gradient Boosting, compared with accuracy, recall, and F1-score, supported by feature analysis. Unsupervised models such as K-Means, DBSCAN, Isolation Forest, and LOF are assessed via detected outliers and cluster distributions, complemented by visual pattern comparisons and permutation-based feature importance. Results indicate Random Forest and Gradient Boosting as leading methods, with SVM effective on smaller datasets, and a recommended combined supervised-unsupervised system for higher accuracy.","Machine learning algorithms and visualization techniques for financial anomaly detection  \nBachelor’s Thesis  \nDegree Programme in Computer Applications  \nSpring 2025  \nNastaran Dehnavi  \nDP Degree Programme in Computer Applications  \nAuthor Nastaran Dehnavi Year 2025  \nSubject Machine learning algorithms and visualization techniques for financial anomaly detection Supervisors Tommi Lahti  \nThe aim of this thesis is to analyze anomalies, more specifically suspicious bank transactions, through supervised and unsupervised machine learning algorithms. The core research query is how various machine learning models detect and analyze unusual patterns occurring in transaction data and picking out the best algorithms for fraud detection. This study involves supervised learning techniques such as KNN, Decision Tree, Random Forest, SVM, and Gradient Boosting. On the other hand, unsupervised learning algorithms include K-Means, DBSCAN, Isolation Forest, and Local outlier factor (LOF) .  \nThis is a practical study based on the report on bank transactions. Various preprocessing data techniques are applied first. Then, different supervised and unsupervised models are trained and monitored through the process of fraud detection. Supervised models are monitored and compared using the metrics of accuracy, recall, and F1-score; important insights were then extracted through feature analysis. For unsupervised models, there was analysis based on the number of detected outliers and cluster distributions; visual analysis and pattern comparisons are also taken into consideration. Feature importance is analysed with Permutation Importance to detect which are the most influential factors for fraud detection.  \nThe results indicate that Machine Learning algorithms, especially Random Forest and Gradient Boosting, are the most useful methods for the detection of fraudulent transactions. SVM can also successfully detect fraudulent transactions, provided the data set size is not very large. Unsupervised techniques such as Isolation Forest, LOF, and K-Means are also utilized for the detection of abnormal transactions. Hence, the recommendation from this analysis is that fraud detection systems in banks should use a combination of both supervised and unsupervised models to increase the accuracy of detection. For feature importance, Decision Tree, Random Forest, and Gradient Boosting are recommended.  \nKeywords Anomaly detection, supervised machine learning, unsupervised machine learning, fraud Pages 45 pages and appendices 1 page  \nTable of Contents  \n1 Introduction ..................................................................................................................................... 1  \n2 Anomaly detection........................................................................................................................... 3  \n3 Financial fraud in bank transactions ................................................................................................ 4  \n4 Machine learning algorithms in fraud detection ............................................................................... 5  \n4.1 Supervised learning Technique .............................................................................................. 6  \n4.1.1 K-Nearest Neighbors (KNN) ....................................................................................... 7  \n4.1.2 Decision Tree ............................................................................................................. 8  \n4.1.3 Random Forest ........................................................................................................... 9  \n4.1.4 Support Vector Machine (SVM) ................................................................................ 10  \n4.1.5 Gradient Boosting ..................................................................................................... 11  \n4.2 Unsupervised learning Technique .................................................","cbCaivhfZvRktk5h","https://ap.wps.com/l/cbCaivhfZvRktk5h","pdf",1386880,1,52,"English","en",105,"# Introduction\n# Anomaly detection\n# Financial fraud in bank transactions\n# Machine learning algorithms in fraud detection\n## Supervised learning Technique\n## Unsupervised learning Technique\n# Methods and techniques\n## Datasets\n## Implementation\n## Tools and Libraries\n# Introduction of practical\n## Supervised Learning Algorithms Analysis","[{\"question\":\"Which machine learning models are used for suspicious bank transaction detection?\",\"answer\":\"The thesis uses supervised models including KNN, Decision Tree, Random Forest, SVM, and Gradient Boosting. It also applies unsupervised methods such as K-Means, DBSCAN, Isolation Forest, and Local Outlier Factor (LOF).\"},{\"question\":\"How are supervised and unsupervised models evaluated in the study?\",\"answer\":\"Supervised models are monitored and compared using accuracy, recall, and F1-score, with insights extracted through feature analysis. Unsupervised models are analyzed by the number of detected outliers and cluster distributions, with additional visual analysis and pattern comparisons.\"},{\"question\":\"What do the results recommend for building a bank fraud detection system?\",\"answer\":\"Random Forest and Gradient Boosting are identified as the most useful methods, while SVM can also detect fraud when the dataset size is not very large. The recommendation is to combine supervised and unsupervised models to improve detection accuracy.\"}]","Machine Learning Algorithms and Visualization Techniques for Financial Anomaly Detection - Bachelor’s Thesis | PDF",1785724075,131,{"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-algorithms-and-visualization-techniques-for-financial-anomaly-detection-bachelors-thesis","",{"@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-algorithms-and-visualization-techniques-for-financial-anomaly-detection-bachelors-thesis/119394/",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},"Which machine learning models are used for suspicious bank transaction detection?","Question",{"text":75,"@type":76},"The thesis uses supervised models including KNN, Decision Tree, Random Forest, SVM, and Gradient Boosting. It also applies unsupervised methods such as K-Means, DBSCAN, Isolation Forest, and Local Outlier Factor (LOF).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are supervised and unsupervised models evaluated in the study?",{"text":80,"@type":76},"Supervised models are monitored and compared using accuracy, recall, and F1-score, with insights extracted through feature analysis. Unsupervised models are analyzed by the number of detected outliers and cluster distributions, with additional visual analysis and pattern comparisons.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results recommend for building a bank fraud detection system?",{"text":84,"@type":76},"Random Forest and Gradient Boosting are identified as the most useful methods, while SVM can also detect fraud when the dataset size is not very large. 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