[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124724-en":3,"doc-seo-124724-105":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},124724,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A Numeric-Based Machine Learning Design for Detecting Organized Retail Fraud in Digital Marketplaces - Article","Organized retail crime (ORC) is a growing threat to retailers, digital marketplace platforms, and consumers as online commerce expands. A scalable supervised machine learning approach is presented to classify marketplace postings as fraudulent or legitimate using historical buyer and seller behaviors and transactions. The framework integrates tailored preprocessing, feature selection, and class-asymmetry resolution to enable effective discrimination. The best model reaches 0.97 recall on the holdout set and 0.94 on out-of-sample testing using 45 of 58 features.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nA numeric‑based machine learning design for detecting organized retail fraud in digital marketplaces  \nAbed Mutemi * & Fernando Bacao  \nOrganized retail crime (ORC) is a significant issue for retailers, marketplace platforms, and consumers. Its prevalence and influence have increased fast in lockstep with the expansion of online commerce, digital devices, and communication platforms. Today, it is a costly affair, wreaking havoc on enterprises’ overall revenues and continually jeopardizing community security. These negative consequences are set to rocket to unprecedented heights as more people and devices connect to the Internet. Detecting and responding to these terrible acts as early as possible is critical for protecting consumers and businesses while also keeping an eye on rising patterns and fraud. The issue of detecting fraud in general has been studied widely, especially in financial services, but studies focusing on organized retail crimes are extremely rare in literature. To contribute to the knowledge base in this area, we present a scalable machine learning strategy for detecting and isolating ORC listings on a prominent marketplace platform by merchants committing organized retail crimes or fraud. We employ a supervised learning approach to classify postings as fraudulent or real based on past data from buyer and seller behaviors and transactions on the platform. The proposed framework combines bespoke data preprocessing procedures, feature selection methods, and state‑of‑the‑art class asymmetry resolution techniques to search for aligned classification algorithms capable of discriminating between fraudulent and legitimate listings in this context. Our best detection model obtains a recall score of 0.97 on the holdout set and 0.94 on the out‑of‑sample testing data set. We achieve these results based on a select set of 45 features out of 58.  \nAbbreviations  \nML  \nORC  \nRTC  \nLR  \nKNN  \nSVM  \nCART  \nRF  \nGNB  \nGB  \nBRF  \nSG  \nFDM SMOTE  \nSMOTENC CV  \nEDA  \nTP  \nTN  \nFP  \nFN  \nMachine learning Organized retail crime Retail theft cases Logistic regression k-nearest neighbor  \nSupport vector machine Classification and regression tree Random forest  \nGaussian naive bayes Gradient boosting Balanced random forest Stacked generalization Fraud detection model  \nSynthetic minority oversampling technique  \nSynthetic minority oversampling technique for nominal and continuous  \nCross validation Exploratory data analysis True positive  \nTrue negative False positive  \nFalse negative  \nNOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312 Lisboa, Portugal.*[email: d20200455@novaims.unl.pt](email: d20200455@novaims.unl.pt)  \n[www. nature.com/scientificreports/](www. nature.com/scientificreports/)  \nRecently, there has been a growth in the use of internet commerce and communication platforms, heightened even more by the COVID-19 pandemic. More than ever before, a sizable portion of the population conducts normal activities online and at home, including work, school, shopping, doctor appointments, and entertainment1. Cybercrime and fraud have expanded substantially in line with the widespread use of digital devices and platforms2 continuing the pattern of losing the global economy billions of dollars3 and jeopardizing community security4.  \nCybercrime and fraud encompass a diverse range of heinous actions, including phishing, malware, fraudulent e-commerce, romance scams, tech support scams, extortion or blackmail, and denial of service1. Additionally, there are instances of credit card theft, money laundering, and plagiarism. Both practices have a detrimental effect on both enterprises and customers, posing significant economic, reputational, and psychological dangers to these entities.  \nCombating cybercrime and fraud is a time-consuming and costly task since bad actors are always evolving and capitaliz","cbCaifYRpahc45Mm","https://ap.wps.com/l/cbCaifYRpahc45Mm","pdf",2097063,1,16,"English","en",105,"# Introduction\n## Background and motivation\n## Cybercrime and fraud overview\n## Prevention vs detection methodologies\n## Competing approaches in fraud systems\n# Proposed machine learning framework\n## Data and supervised classification setup\n## Preprocessing and feature selection\n## Class asymmetry resolution techniques\n# Results and evaluation\n## Best model performance metrics\n## Selected features count","[{\"question\":\"Why is detecting organized retail fraud important in digital marketplaces?\",\"answer\":\"Organized retail crime increases as online commerce and connected devices grow, causing financial losses, harming enterprises’ revenues, and jeopardizing community security. Early detection and response help protect both consumers and businesses.\"},{\"question\":\"What learning approach does the proposed system use to detect ORC?\",\"answer\":\"It uses supervised learning to classify postings as fraudulent or real based on historical buyer and seller behaviors and platform transactions.\"},{\"question\":\"How well does the best detection model perform?\",\"answer\":\"The best model achieves 0.97 recall on the holdout set and 0.94 on the out-of-sample testing dataset. It relies on 45 selected features out of 58.\"}]","A Numeric-Based Machine Learning Design for Detecting Organized Retail Fraud in Digital Marketplaces - Article | PDF",1785894121,40,{"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},"a-numeric-based-machine-learning-design-for-detecting-organized-retail-fraud-in-digital-marketplaces-article","",{"@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/a-numeric-based-machine-learning-design-for-detecting-organized-retail-fraud-in-digital-marketplaces-article/124724/",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","2026-08-05",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 is detecting organized retail fraud important in digital marketplaces?","Question",{"text":75,"@type":76},"Organized retail crime increases as online commerce and connected devices grow, causing financial losses, harming enterprises’ revenues, and jeopardizing community security. Early detection and response help protect both consumers and businesses.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What learning approach does the proposed system use to detect ORC?",{"text":80,"@type":76},"It uses supervised learning to classify postings as fraudulent or real based on historical buyer and seller behaviors and platform transactions.",{"name":82,"@type":73,"acceptedAnswer":83},"How well does the best detection model perform?",{"text":84,"@type":76},"The best model achieves 0.97 recall on the holdout set and 0.94 on the out-of-sample testing dataset. 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