[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121965-en":3,"doc-seo-121965-105":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},121965,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Detecting Collusion in Public Procurement - A Comparative Study of Machine Learning Models","Detecting collusion in public procurement is essential to safeguard fair, transparent government acquisitions. Bid collusion among competing firms can produce unlawful cooperation that drives unfair price increases and disrupts the supply chain. This thesis applies machine learning to identify collusion, training and tuning multiple models—random forests, extra tree classifiers, support vector classifiers, neural networks, and gradient boosting—using bid-derived screening variables as additional features. Comparative evaluation uses accuracy, balanced accuracy, precision, recall, F1-score, and ROC-AUC to reduce false positives while maximizing true positives.","Detecting Collusion in Public Procurement: A Comparative Study of Machine Learning Models  \nRuchika Barot  \nA Thesis  \nin  \nThe Department  \nof  \nSupply Chain & Business Technology Management  \nPresented in Partial Fulfillment of the Requirements  \nfor the Degree of Master of Supply Chain Management at  \nConcordia University  \nMontréal, Quebec, Canada  \nNovember 2023  \n© Ruchika Barot, 2023  \nCONCORDIA UNIVERSITY  \nSchool of Graduate Studies  \nThis is to certify that the thesis prepared,  \nBy: Ruchika Barot  \nEntitled: Detecting Collusion in Public Procurement: A Comparative Study of Machine Learning Models  \nand submitted in partial fulfillment of the requirements for the degree of  \nMaster of Supply Chain Management  \ncomplies with the regulations of the University and meets the accepted standards with respect to originality and quality.  \nSigned by the final Examining Committee:  \nDr. Danielle Morin  \n\n| Dr. Danielle Morin |\n| --- |\n| Dr. Mohsen Farhadloo |\n| Dr. Chaher Alzaman |\n\nApproved by: Dr. Satyaveer S. Chauhan, Graduate Program Director  \nDate:  December 20th, 2023   \nDean: Dr. Anne-Marie Croteau  \nChair  \nExaminer  \nExaminer  \nSupervisor  \nABSTRACT  \nDetecting Collusion in Public Procurement: A Comparative Study of Machine Learning Models  \nRuchika Barot  \nDetecting collusion in public procurement is critical to ensure fair and transparent practices in government acquisitions. Bid collusion in auctions poses a major challenge in public procurement by causing unfair price hikes through unlawful cooperation among competing firms, consistently affecting the overall supply chain. This study uses machine learning methods to investigate collusion in public procurement processes. It delves deeply into exploring multiple machine learning models such as random forests, extra tree classifiers, support vector classifiers, Neural Networks, Gradient Boosting, and various combinations of models for collusion detection. First, the models were trained using available data, followed by the inclusion of screening variables derived from bid information as additional features. The additional features were fed to the models, which went through fine-tuning of parameters. Additionally, comparative analyses were carried out to evaluate the merits and drawbacks of each model. Metrics including Accuracy, balanced accuracy, precision, recall, F1-score, and ROC-AUC score were evaluated, providing a comprehensive evaluation framework. Various settings were used to compare which set of inputs gives the highest accuracy in collusion detection. The ROC-AUC analysis brought forward crucial insights, particularly regarding models' abilities to minimize false positives while maximizing true positives. Models like Random Forest and Gradient Boosting demonstrated superior performance, showcasing lower false positive rates—a crucial aspect when identifying collusion in public procurement. Additionally, the study underscores the significance of feature engineering in collusion detection. Specifically, attributes like screens-CV, SPD, DIFFP, RD, SKEW, KURTO, and KS significantly aid algorithms in processing data effectively to identify collusion patterns. The outcomes of this study carry significant implications for both the specific domain under investigation and the broader field of collusion detection. Ultimately, this research provides a valuable guide for policymakers, procurement officers, and data scientists, offering valuable insights into the effective machine learning techniques tailored for detecting collusion in public procurement.  \nKeywords: Collusion detection, public procurement, Machine learning models, Feature engineering  \nACKNOWLEDGEMENT  \nI'm incredibly thankful to Dr. Chaher Alzaman, my thesis supervisor, for his invaluable guidance and unwavering support throughout this journey. Having such a dedicated and responsive mentor has been a true privilege. His expertise was pivotal in the development of this work. This journey, both acad","cbCaipvojtTISBP6","https://ap.wps.com/l/cbCaipvojtTISBP6","pdf",2514413,1,70,"English","en",105,"# Chapter 1 INTRODUCTION\n## 1.1 Research Problem Description\n## 1.2 Structure of Thesis\n# Chapter 2 LITERATURE REVIEW\n## 2.1 Collusion in Public Procurement Practices and impact\n## 2.2 Corruption/ anomaly detection methods in different sectors\n## 2.3 Collusion detection methods in public procurement\n## 2.4 Summary table\n# Chapter 3 DATASET","[{\"question\":\"Why is detecting collusion in public procurement important?\",\"answer\":\"Bid collusion undermines fair and transparent government acquisitions by encouraging unlawful cooperation among competing firms, leading to unfair price hikes and supply-chain impacts.\"},{\"question\":\"Which machine learning models are compared in this study?\",\"answer\":\"The study evaluates models including random forests, extra tree classifiers, support vector classifiers, neural networks, gradient boosting, and model combinations, tuned using bid-derived screening features.\"},{\"question\":\"How are model performance and collusion detection effectiveness assessed?\",\"answer\":\"Performance is measured using accuracy, balanced accuracy, precision, recall, F1-score, and ROC-AUC, with emphasis on minimizing false positives while maximizing true positives.\"}]","Detecting Collusion in Public Procurement - 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