[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120939-en":3,"doc-seo-120939-105":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},120939,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Financial Fraud Detection - A Comparative Study of Quantum Machine Learning Models","This research presents a comparative study of four Quantum Machine Learning (QML) models applied to financial fraud detection. Performance comparison shows the Quantum Support Vector Classifier achieves the strongest results, with F1 scores of 0.98 for both fraud and non-fraud classes. Variational Quantum Classifier, Estimator Quantum Neural Network (QNN), and Sampler QNN deliver promising outcomes. Limitations remain, including the need for more efficient quantum algorithms and larger, more complex datasets.","arXiv :2308 .05237v1 [ quant-ph] 9 Aug 2023  \nFinancial Fraud Detection: A Comparative Study of Quantum Machine Learning Models  \nNouhaila Innan , 1, 2, ∗ Muhammad Al-Zafar Khan ,2, 3,† and Mohamed Bennai 1,‡  \n1 Quantum Physics and Magnetism Team, LPMC,  \nFaculty of Sciences Ben M’sick, Hassan II University of Casablanca, Morocco  \n2 Quantum Formalism Fellow, Zaiku Group Ltd, Liverpool, United Kingdom  \n3 Robotics, Autonomous Intelligence, and Learning Laboratory (RAIL),  \nSchool of Computer Science and Applied Mathematics,  \nUniversity of the Witwatersrand, 1 Jan Smuts Ave,  \nBraamfontein, Johannesburg 2000, Gauteng, South Africa  \nAbstract  \nIn this research, a comparative study of four Quantum Machine Learning (QML) models was conducted for fraud detection in finance. We proved that the Quantum Support Vector Classifier model achieved the highest performance, with F1 scores of 0.98 for fraud and non-fraud classes. Other models like the Variational Quantum Classifier, Estimator Quantum Neural Network (QNN), and Sampler QNN demonstrate promising results, propelling the potential of QML classification for financial applications. While they exhibit certain limitations, the insights attained pave the way for future enhancements and optimisation strategies. However, challenges exist, including the need for more efficient quantum algorithms and larger and more complex datasets. The article provides solutions to overcome current limitations and contributes new insights to the field of Quantum Machine Learning in fraud detection, with important implications for its future development. Keywords: Quantum Machine Learning, Quantum Neural Networks, Quantum Feature Maps, Fraud  \nDetection.  \n∗ [nouhailainnan@gmail.com](nouhailainnan@gmail.com)[ ](nouhailainnan@gmail.com)† [muhammadalzafark@gmail.com](muhammadalzafark@gmail.com)[ ](muhammadalzafark@gmail.com)‡ [mohamed.bennai@univh2c.ma](mohamed.bennai@univh2c.ma)  \nI. INTRODUCTION  \nFraud is the act of deceiving and misleading a person, or group of people, with the intention of obtaining some kind of gain (oftentimes financial) . It involves the provisioning of misrepresented information or data to the victim, which seems “too good to be true”, or the request of the victim’s private data. Frequently, the targets of these attacks are elderly folk or those individuals whom are not technologically inclined. Fraudsters play on the emotions of their victims by usually creating a need for urgency around performing a certain task, like the victim disclosing his/her confidential information like identity/social security numbers, pin codes, One-Time Pins (OTPs), or other information that can render the victim susceptible. Over the years, fraud schemes have become even more sophisticated, and with the advent of Generative Artificial Intelligence (GenAI) becoming more ubiquitous, more suave and ultra-modern schemes such as the employment of various phishing scams and Natural Language Processing (NLP) to use voices of the victim’s family members or friends are used in order to gain their trust, and credence.  \nBroadly speaking, fraud can be categorised into the following flavours:  \nI.1.1. Purloinment of Identity: Also known as “identity theft”, This occurs when the perpetrator steals the personal information from the victim and “assumes their identity”in the sense of using their details with nefarious intent: Using the victim’s personal identification number, applying for any licenses, using the victim’s debit/credit card details for purchasing goods or paying for services.  \nI.1.2. Insurance Claims Fraud: This occurs when the perpetrator intentionally files fallacious insurance claims or overinflates the value of losses that occurred.  \nI.1.3. Financial Fraud: This falls under the broader category of white collar crimes and constitutes:  \nI.1.3.1. Accounting Fraud: Also known as “crooking the books”. This involves the deliberate manipulation and misrepresentation of figures in financial statement","cbCaijZDeEl7aKGu","https://ap.wps.com/l/cbCaijZDeEl7aKGu","pdf",1152482,1,30,"English","en",105,"# Introduction\n## Fraud categories and schemes\n## Identity theft\n## Insurance claims fraud\n## Accounting fraud\n## Ponzi and pyramidal schemes\n## Embezzlement and insider trading\n## Wire fraud\n## Credit fraud","[{\"question\":\"Which quantum machine learning model performs best for fraud detection in the study?\",\"answer\":\"The Quantum Support Vector Classifier achieves the highest performance, reaching F1 scores of 0.98 for fraud and non-fraud classes.\"},{\"question\":\"What other QML models are compared besides the Quantum Support Vector Classifier?\",\"answer\":\"The study compares the Variational Quantum Classifier, Estimator Quantum Neural Network (QNN), and Sampler QNN, all showing promising results.\"},{\"question\":\"What key challenges are highlighted for future improvements?\",\"answer\":\"The article notes challenges such as the need for more efficient quantum algorithms and the use of larger and more complex datasets.\"}]","Financial Fraud Detection - A Comparative Study of Quantum Machine Learning Models | PDF",1785732888,76,{"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},"financial-fraud-detection-a-comparative-study-of-quantum-machine-learning-models","",{"@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/financial-fraud-detection-a-comparative-study-of-quantum-machine-learning-models/120939/",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 quantum machine learning model performs best for fraud detection in the study?","Question",{"text":75,"@type":76},"The Quantum Support Vector Classifier achieves the highest performance, reaching F1 scores of 0.98 for fraud and non-fraud classes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What other QML models are compared besides the Quantum Support Vector Classifier?",{"text":80,"@type":76},"The study compares the Variational Quantum Classifier, Estimator Quantum Neural Network (QNN), and Sampler QNN, all showing promising results.",{"name":82,"@type":73,"acceptedAnswer":83},"What key challenges are highlighted for future improvements?",{"text":84,"@type":76},"The article notes challenges such as the need for more efficient quantum algorithms and the use of larger and more complex datasets.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":21,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]