[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125108-en":3,"doc-seo-125108-105":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},125108,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes","Continuous digitalisation increases the volume of purchase data and forces organisations to detect anomalies that may indicate suspicious activities. This work proposes a methodology to prioritise investigation of detected cases in two large real enterprise purchase datasets. Exploratory Data Analysis is performed before applying unsupervised machine learning for anomaly detection, using a univariate z-Score and DBSCAN approach, and a multivariate k-Means and Isolation Forest approach, with the Silhouette index to score transaction candidates. An ensemble prioritisation and explainability methods (LIME, Shapley, SHAP) support specialists’ understanding.","APPLIED MACHINE LEARNING TO ANOMALY DETECTION IN ENTERPRISE PURCHASE PROCESSES  \narXiv :2405 . 14754v1 [ cs .CE] 23 May 2024  \nA. Herreros-Martínez  \nVeolia Group  \nAlicante, Spain [antonio.herreros@veolia.com](antonio.herreros@veolia.com)  \nR. Magdalena-Benedicto, J. Vila-Francés, A.J. Serrano-López  \nUniversidad de Valencia, Research Group IDAL Valencia, Spain  \n{rafael.magdalena,joan.vila,[antonio.j.serrano}@uv.es](antonio.j.serrano}@uv.es)  \nS. Pérez-Díaz  \nUniversity of Alcalá de Henares, Research Group ASYNACS,  \nMadrid, Spain  \n[sonia.perez@uah.es](sonia.perez@uah.es)  \nABSTRACT  \nIn a context of a continuous digitalisation of processes, organisations must deal with the challenge of detecting anomalies that can reveal suspicious activities upon an increasing volume of data. To pursue this goal, audit engagements are carried out regularly, and internal auditors and purchase specialists are constantly looking for new methods to automate these processes. This work proposesa methodology to prioritise the investigation of the cases detected in two large purchase datasets from real data. The goal is to contribute to the effectiveness of the companies’ control efforts and to increase the performance of carrying out such tasks. A comprehensive Exploratory Data Analysis is carried out before using unsupervised Machine Learning techniques addressed to detect anomalies. Aunivariate approach has been applied through the z-Score index and the DBSCAN algorithm, while a multivariate analysis is implemented with the k-Means and Isolation Forest algorithms, and the Silhouette index, resulting in each method having a transaction candidates’ proposal to be reviewed.  \nAn ensemble prioritisation of the candidates is provided jointly with a proposal of explicability methods (LIME, Shapley, SHAP) to help the company specialists in their understanding.  \n1 Introduction  \nThe Internal Audit department of a company (normally multinationals groups and/or big-sized entities) is aimed to ensure the correctness and effectiveness of the entities’ processes, its compliance to the approved internal policies and to reduce risks in any form that could be presented [1] . In order to achieve this goal, the companies’ internal teams conduct audits through on a regular basis defined audit engagements. During their missions, the auditors identify, evaluate and document adequate information to achieve the objectives of the engagement [2], carrying out interviews with the auditees and performing a rigorous tracking of evidences supporting the audit findings.  \nCurrently, auditing still mainly relies on sampling the information (registers, transactions, etc.) to assess the processes’compliance during the audit engagements [3] . Consequently, the so-called sampling-risk makes that relevant information in the registers/transactions could remain out of the sampling selection to be reviewed. Additionally, with the growing amount of data, this traditional approach becomes obsolete, and the sampling risk is aggravated [4] .  \nAmong the business processes, a special interest resides in searching for anomalies or misbehaviours on purchases. Internal audit and purchase managers need to prospect, evaluate, and select the methodologies and IT tools capable of monitoring expenses and discovering relevant information that can highlight an out-of-policy act or, even, fraud [5, 6] . The goal is to automate processes within the company that help to prioritize the investigation activities according to the level of suspicion of any fact.  \nIn this business context, data analytics has been revealed as a key element in supporting organisations in the challenge of processing and controlling huge quantities of information. Among the various algorithms available, those related to machine learning are known to offer good results in finding relevant insights over structured and unstructured data. Machine learning and data analytics are changing the auditing approaches [7] and are becom","cbCaicYfTIh0XVfL","https://ap.wps.com/l/cbCaicYfTIh0XVfL","pdf",612731,1,12,"English","en",105,"# Abstract\n# Introduction\n## Audit processes and sampling risk\n## Machine learning for anomaly detection\n# Goals of the work\n# Purchase Business Process","[{\"question\":\"Why is anomaly detection important in enterprise purchase processes?\",\"answer\":\"It helps internal audit and purchase managers discover out-of-policy acts or even fraud as data volumes grow and traditional sampling becomes less effective.\"},{\"question\":\"How does the methodology detect anomalies without labelled data?\",\"answer\":\"It uses unsupervised machine learning, applying univariate z-Score and DBSCAN, and multivariate k-Means and Isolation Forest, supported by Silhouette scoring for candidate selection.\"},{\"question\":\"What does the work provide to help specialists investigate the results?\",\"answer\":\"It delivers an ensemble prioritisation of transaction candidates and proposes explainability methods such as LIME, Shapley, and SHAP to support case understanding.\"}]","Applied Machine Learning to Anomaly Detection in Enterprise Purchase Processes | 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is anomaly detection important in enterprise purchase processes?","Question",{"text":75,"@type":76},"It helps internal audit and purchase managers discover out-of-policy acts or even fraud as data volumes grow and traditional sampling becomes less effective.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the methodology detect anomalies without labelled data?",{"text":80,"@type":76},"It uses unsupervised machine learning, applying univariate z-Score and DBSCAN, and multivariate k-Means and Isolation Forest, supported by Silhouette scoring for candidate selection.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the work provide to help specialists investigate the results?",{"text":84,"@type":76},"It delivers an ensemble prioritisation of transaction candidates and proposes explainability methods such as LIME, Shapley, and SHAP to support case 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