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Relevant bibliographies by topics / Fraud detection

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Academic literature on the topic 'Fraud detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Fraud detection"

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Jasmine, A. Hudali, Kamalakshi, P. Mahalaxmi K, S. Magadum Namita, and Sudhir Belagali Prof. "Credit Card Fraud Detection by using ANN and Decision Tree." Advancement of Computer Technology and its Applications 2, no. 3 (2019): 1–4. https://doi.org/10.5281/zenodo.3562024.

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<em>Credit card fraud detection is one of the biggest ethical issues. Credit card fraud detection has different types of frauds. Based on those types of fraud it will apply the fraud detection techniques. There are several techniques for detecting the frauds. Those techniques take the input data and tell the user to that particular input has fraud or not. If the transaction is fraud it will takes some actions like sending the message to owner and credit card industries.</em>

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Banu, Muskan, and Prof Kavitha G. "Credit Card Fraud Detection Using Machine Learning Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 10 (2022): 1018–23. http://dx.doi.org/10.22214/ijraset.2022.47127.

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Abstract: Credit Card Fraud can be defined as a case where a person uses someone else’s credit card for personal reasons while the owner and the card-issuing authorities are unawareof the fact that the card is being used. Credit card frauds are easy and friendly targets. E-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds. In the era of digitalization, the need to identify credit card frauds is necessary. Fraud detection involves monitoring and analyzing the behaviour of various users to estimate, detect or avoid undesirable beh

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N.A., Padmalatha. "E-Commerce Fraud Detection Tools." Shanlax International Journal of Management 7, S1 (2020): 24–32. https://doi.org/10.5281/zenodo.3737716.

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India is a cash based society. However, as per the reports of NASSCOM, India  is expected to have Internet users base of about 730 million by the end of 2020.  It has been noted that by 2020, Indian e-commerce industry is expected to reach $34 billion, with 200 million individuals transacting online, growing 3X over 2015. This spread of e-Commerce has led to the rise of several niche players who largely specialize their products around a specific theme including fraud detection tools. Experts predict online credit card fraud to increase to $32 billion by 2020. In order to combat tech

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Bala, Bala Santhosh, Pasupula Praveen Yadav, and Mogathala Raghavendra Reddy. "An intelligent approach to detect and predict online fraud transaction using XGBoost algorithm." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1491. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp1491-1498.

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The most popular payment method in recent years is the credit card. Due to the E-commerce industry’s explosive growth, the usage of credit cards for online purchases have been greatly increased as a result frauds has increased. Banks have been facing challenges to detect the credit card system fraud in recent years. Credit card fraud happens when the card was stolen for any unauthorized purposes or if the fraudster utilizes the credit card information for his own use. In order to prevent credit card fraud, it is essential to build detection measures. While detecting credit card theft with mach

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Bala, Santhosh Bala Pasupula Praveen Yadav Mogathala Raghavendra Reddy. "An intelligent approach to detect and predict online fraud transaction using XGBoost algorithm." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 1491–98. https://doi.org/10.11591/ijeecs.v35.i3.pp1491-1498.

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The most popular payment method in recent years is the credit card. Due to the E-commerce industry’s explosive growth, the usage of credit cards for online purchases have been greatly increased as a result frauds has increased. Banks have been facing challenges to detect the credit card system fraud in recent years. Credit card fraud happens when the card was stolen for any unauthorized purposes or if the fraudster utilizes the credit card information for his own use. In order to prevent credit card fraud, it is essential to build detection measures. While detecting credit card theft wit

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Ishak, Nur Amirah, Keng-Hoong Ng, Gee-Kok Tong, Suraya Nurain Kalid, and Kok-Chin Khor. "Mitigating unbalanced and overlapped classes in credit card fraud data with enhanced stacking classifiers system." F1000Research 11 (January 21, 2022): 71. http://dx.doi.org/10.12688/f1000research.73359.1.

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Background: Credit cards remain the preferred payment method by many people nowadays. If not handled carefully, people may face severe consequences such as credit card frauds. Credit card frauds involve the illegal use of credit cards without the owner’s knowledge. Credit card fraud was estimated to exceed a $35.5 billion loss globally in 2020, and results in direct or indirect financial loss to the owners. Hence, a detection system capable of analysing and identifying fraudulent behaviour in credit card activities is highly desirable. Credit card data are not easy to handle due to their inher

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Venkata Suryanarayana, S., G. N. Balaji, and G. Venkateswara Rao. "Machine Learning Approaches for Credit Card Fraud Detection." International Journal of Engineering & Technology 7, no. 2 (2018): 917. http://dx.doi.org/10.14419/ijet.v7i2.9356.

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With the extensive use of credit cards, fraud appears as a major issue in the credit card business. It is hard to have some figures on the impact of fraud, since companies and banks do not like to disclose the amount of losses due to frauds. At the same time, public data are scarcely available for confidentiality issues, leaving unanswered many questions about what is the best strategy. Another problem in credit-card fraud loss estimation is that we can measure the loss of only those frauds that have been detected, and it is not possible to assess the size of unreported/undetected frauds. Frau

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ADARSH, ADARSH. "A Study of Fraud Detection in E-Commerce: An Application of Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50408.

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Abstract- The given research paper is aimed at investigating how machine learning can be applied to detect and prevent fraud in e-commerce transactions. As online commerce grows at a tremendous pace, the frauds have also evolved at an equally high rate, becoming a serious source of risk to the business as well as consumers in terms of financial as well as reputational risks. However, conventional rule-based fraud identification approaches sometimes fail because of their low flexibility and false-positive results. The research examines the machine learning methods, such as supervised, unsupervi

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Qayoom, Abdul, Yadong Wu, and Saeed Umair. "Exploring Data-Driven Approach for Financial Fraud Detection: A Comprehensive Literature Review." LC International Journal of STEM 5, no. 3 (2024): 64–72. https://doi.org/10.5281/zenodo.14338985.

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Financial fraud detection has emerged as a critical area of research with the growing complexity and scale of fraudulent activities in the financial sector. Traditional methods of fraud detection, which are based on rule-based systems and manual oversight, fail to capture the dynamic and sophisticated nature of modern fraud schemes. This comprehensive literature review examines data-driven approaches that take into account the advancement of machine learning, artificial intelligence, and big data analytics to improve fraud detection. Some of the key methodologies covered are supervised, unsupe

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Talekar, P. R. "Role of Forensic Accounting on Detection and Prevention of Fraud- An Overview." International Journal of Advance and Applied Research 5, no. 10 (2024): 35–39. https://doi.org/10.5281/zenodo.11298570.

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“Earth provides enough to satisfy every man’s needs but not the men’s greed.”- <strong>Mahatma Gandi</strong> <strong> </strong>India is not an exception to the global trend of fraud being a major problem for businesses and organizations in recent years.  The financial well-being, stakeholder trust, and reputation of an organization can all suffer significantly from financial fraud.  As a result, businesses are using forensic accounting more and more to identify, stop, and lessen the effects of fraud.  A specialist area of study called forensic accou

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Dissertations / Theses on the topic "Fraud detection"

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Perols, Johan L. "Detecting Financial Statement Fraud: Three Essays on Fraud Predictors, Multi-Classifier Combination and Fraud Detection Using Data Mining." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002486.

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Jurgovsky, Johannes. "Context-aware credit card fraud detection." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI109.

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La fraude par carte de crédit est devenue un problème majeur dans le secteur des paiements électroniques. Dans cette thèse, nous étudions la détection de fraude basée sur les données transactionnelles et abordons plusieurs de ces défis complexes en utilisant des méthodes d'apprentissage automatique visant à identifier les transactions frauduleuses qui ont été émises illégitimement au nom du titulaire légitime de la carte. En particulier, nous explorons plusieurs moyens d’exploiter les informations contextuelles au-delà des attributs de base d’une transaction, notamment au niveau de la transact

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Lu, Yifei. "Deep neural networks and fraud detection." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-331833.

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Domingues, Rémi. "Machine Learning for Unsupervised Fraud Detection." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181027.

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Fraud is a threat that most online service providers must address in the development of their systems to ensure an efficient security policy and the integrity of their revenue. Amadeus, a Global Distribution System providing a transaction platform for flight booking by travel agents, is targeted by fraud attempts that could lead to revenue losses and indemnifications. The objective of this thesis is to detect fraud attempts by applying machine learning algorithms to bookings represented by Passenger Name Record history. Due to the lack of labelled data, the current study presents a benchmark o

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Albrecht, Chad Orsen. "International fraud: A management perspective." Doctoral thesis, Universitat Ramon Llull, 2008. http://hdl.handle.net/10803/9196.

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L'objectiu de la meva tesi és tractar i entendre millor els múltiples aspectes de la corrupció i el frau internacionals des de la perspectiva del management. Amb aquesta finalitat, hi proporciono un compendi d'articles, tots els quals han estat publicats en journals amb revisors, o bé estan en procés de ser-ho. <br/>El primer article que presento en la meva tesi fou publicat al European Business Forum, una revista especialitzada patrocinada per la CEMS, que és llegida per més de 40.000 professionals dels negocis d'arreu d'Europa. Alguns dels diaris internacionals més importants, com ara el Tim

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Edmonds, Mark Allen. "THE INVISIBLE FRAUD: THE IMPACT OF INATTENTIONAL BLINDNESS ON AUDITOR FRAUD DETECTION." OpenSIUC, 2016. https://opensiuc.lib.siu.edu/dissertations/1153.

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Evidence gathered from major fraud investigations over the last decade has revealed that auditors in these cases failed to attend to fraud red flags within the substantive testing evidence. Research in psychology regarding inattentional blindness (IB) provides a theoretical framework for explaining why auditors may be prone to missing fraud red flags. This study examines the presence of IB during the performance of substantive testing and proposes two distinct interventions. Each intervention is predicted to improve auditor fraud detection. In a scenario involving fraudulent revenue transa

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Westerlund, Fredrik. "CREDIT CARD FRAUD DETECTION (Machine learning algorithms)." Thesis, Umeå universitet, Statistik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-136031.

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Credit card fraud is a field with perpetrators performing illegal actions that may affect other individuals or companies negatively. For instance, a criminalcan steal credit card information from an account holder and then conduct fraudulent transactions. The activities are a potential contributory factor to how illegal organizations such as terrorists and drug traffickers support themselves financially. Within the machine learning area, there are several methods that possess the ability to detect credit card fraud transactions; supervised learning and unsupervised learning algorithms. This es

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Yau, Kin-pong Harry. "The role of accountants in fraud detection." Click to view the E-thesis via HKUTO, 2000. http://sunzi.lib.hku.hk/hkuto/record/B42575552.

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Maruatona, Omaru. "Internet banking fraud detection using prudent analysis." Thesis, University of Ballarat, 2013. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/59631.

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The threat posed by cybercrime to individuals, banks and other online financial service providers is real and serious. Through phishing, unsuspecting victims’ Internet banking usernames and passwords are stolen and their accounts robbed. In addressing this issue, commercial banks and other financial institutions use a generically similar approach in their Internet banking fraud detection systems. This common approach involves the use of a rule-based system combined with an Artificial Neural Network (ANN). The approach used by commercial banks has limitations that affect their efficiency in cur

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Rose, Lydia M. "Modernizing Check Fraud Detection with Machine Learning." Thesis, Utica College, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=13421455.

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<p> Even as electronic payments and virtual currencies become more popular, checks are still the nearly ubiquitous form of payment for many situations in the United States such as payroll, purchasing a vehicle, paying rent, and hiring a contractor. Fraud has always plagued this form of payment, and this research aimed to capture the scope of this 15<sup>th</sup> century problem in the 21<sup>st</sup> century. Today, counterfeit checks originating from overseas are the scourge of online dating sites, classifieds forums, and mailboxes throughout the country. Additional frauds including alteratio

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Books on the topic "Fraud detection"

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Albrecht, W. Steve. Fraud: Detection and prevention. American Institute of Certified Public Accountants, 1996.

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Scott, Harshbarger L., and Massachusetts. Office of the Attorney General., eds. Fraud: Detection and prosecution : combatting the "fraud tax". Office of the Attorney General, Commonwealth of Massachusetts, 1993.

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Office, Massachusetts Attorney General's. Fraud detection and prosecution: Combatting the "fraud tax". Attorney General, Commonwealth of Massachusetts, 1993.

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Young, Michael R. Financial Fraud Prevention and Detection. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118691748.

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Shim, Jae K. Internal control and fraud detection. Global Professional, 2011.

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Coderre, David G. Computer Aided Fraud Prevention and Detection. John Wiley & Sons, Ltd., 2009.

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Rezaee, Zabihollah. Financial statement fraud: Prevention and detection. 2nd ed. Wiley, 2010.

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Coderre, David, ed. Computer-Aided Fraud Prevention and Detection. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119203971.

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Rezaee, Zabihollah. Financial statement fraud: Prevention and detection. 2nd ed. Wiley, 2010.

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Wells, Joseph T. Corporate fraud handbook: Prevention and detection. 3rd ed. Wiley, 2011.

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Book chapters on the topic "Fraud detection"

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Araz, Özgür M., and David L. Olson. "Fraud Detection." In Risk and Predictive Analytics in Business with R. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003562399-8.

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Gottschalk, Petter. "Crime Signal Detection." In Fraud Investigation. Routledge, 2018. http://dx.doi.org/10.4324/9781351139069-5.

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Özkul, Fatma Ulucan, and Ayşe Pamukçu. "Fraud Detection and Forensic Accounting." In Emerging Fraud. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-20826-3_2.

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Fenoff, Roy, and John Spink. "Food fraud and food fraud detection technologies." In The Routledge Handbook of Technology, Crime and Justice. Routledge, 2017. http://dx.doi.org/10.4324/9781315743981-18.

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Shi, Yong, Yingjie Tian, Gang Kou, Yi Peng, and Jianping Li. "Health Insurance Fraud Detection." In Advanced Information and Knowledge Processing. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-504-0_14.

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Janbandhu, Ruchika, Shameedha Begum, and N. Ramasubramanian. "Credit Card Fraud Detection." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9515-5_22.

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Priesterjahn, Steffen, Maik Anderka, Timo Klerx, and Uwe Mönks. "Generalized ATM Fraud Detection." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20910-4_13.

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Bhargava, Bharat, Yuhui Zhong, and Yunhua Lu. "Fraud Formalization and Detection." In Data Warehousing and Knowledge Discovery. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45228-7_33.

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Irofti, Paul, Andrei Pătraşcu, and Andra Băltoiu. "Fraud Detection in Networks." In Enabling AI Applications in Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52067-0_23.

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Fang, Liu, Ming Cai, Hao Fu, and Jinxiang Dong. "Ontology-Based Fraud Detection." In Computational Science – ICCS 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72588-6_168.

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Conference papers on the topic "Fraud detection"

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Kumar, Alok, Marella Venkata Poojitha, Turlapati Anuhya, Katuri Srinivas, and Maridu Bhargavi. "Credit Card Fraud Detection." In 2024 8th International Conference on Inventive Systems and Control (ICISC). IEEE, 2024. http://dx.doi.org/10.1109/icisc62624.2024.00020.

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Abdeen, Ahmad, Mahmoud Mohanna, Yusuf Mansur, and Elena Battini Sonmez. "Biometric Fraud Detection System." In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2024. http://dx.doi.org/10.1109/idap64064.2024.10710778.

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Smadi, Baker Al, Jhada Carmel, and Bennie D. Ferguson. "The Kiosk Fraud Detection System." In 2024 International Symposium on Networks, Computers and Communications (ISNCC). IEEE, 2024. http://dx.doi.org/10.1109/isncc62547.2024.10758974.

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Nair, Rekha R., V. Vjeya Kaveri, Shrinidhi Sivakumar, Srinithi Janani R, Tina Babu, and Rajesh Sharma R. "Fraud Detection in Monetary Transactions." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690586.

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Lim, Seonkyu, Jeongwhan Choi, Jaehoon Lee, and Noseong Park. "FrAug: Enhanced Fraud Detection in Interbank Transfers via Augmented Account Features." In 2025 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2025. https://doi.org/10.1109/bigcomp64353.2025.00048.

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Yehya, Batoul Abo, and Nazih Salhab. "Telecommunications Fraud Machine Learning-based Detection." In 2023 4th International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2023. http://dx.doi.org/10.1109/icdabi60145.2023.10629612.

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Marimuthu, M., K. Lekshmi, P. Saravanan, Dasari Nagaveni, P. Manikandan, and B. Natarajan. "Transaction Fraud Detection Using SMOTE Oversampling." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10796639.

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Islam Prova, Nuzhat Noor. "Healthcare Fraud Detection Using Machine Learning." In 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI). IEEE, 2024. http://dx.doi.org/10.1109/icoici62503.2024.10696476.

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Zhang, Xianwei, and Xingrui Wei. "Fraud Detection Method Design and Implementation." In 2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI). IEEE, 2024. http://dx.doi.org/10.1109/icecai62591.2024.10675208.

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M, Prabha, Sowmiya Sunder, Rajasreenithaa Kumarabaabu, Rachel Davis, and Saraswathi P. "AI-Powered Financial Fraud Detection System." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915218.

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Reports on the topic "Fraud detection"

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Hogden, J. Maximum likelihood continuity mapping for fraud detection. Office of Scientific and Technical Information (OSTI), 1997. http://dx.doi.org/10.2172/468619.

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Musciotto, Dr Federico, and Matteo Neri. FNA Papers: Annex 4: Enhancing Fraud Detection with Real -Time Money Trails. FNA, 2025. https://doi.org/10.69701/dkfj2748.

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Building AI models for fraud detection on the national or consortium level is challenging due to the need to combine payment and fraud data collected across different institutions. Fraud data may include labels for verified fraudulent transactions or labels for accounts that have been used for fraudulent transactions in the past. Payment data may include routing information, information on payment channels, etc.

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Shekhar, Shubhranshu, Jetson Leder-Luis, and Leman Akoglu. Unsupervised Machine Learning for Explainable Health Care Fraud Detection. National Bureau of Economic Research, 2023. http://dx.doi.org/10.3386/w30946.

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Kang, Haimeng. Fraud Detection in Mobile Money Transactions Using Machine Learning. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-759.

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Loecker, Florian, Amanah Ramadiah, and Kimmo Soramäki. Countering Consumer Fraud and Scams with National Fraud Portals. FNA, 2024. http://dx.doi.org/10.69701/oppl1525.

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In this paper, we argue that setting up a new Digital Public Infrastructure - a National Fraud Portal (NFP) - is the only way to address fraud and consumer scams efficiently. NFPs provide a technological solution as a shared facility for banks, law enforcement, the Financial Intelligence Unit (FIU), the central bank, the conduct supervisor, and other stakeholders. Further down the line, NFPs can connect to one another as cross-border criminal activity increases (a likely consequence of suppressing fraud domestically). The National Fraud Portal (NFP) enables: The real-time tracing and tracking

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Dutra, Lauren M., Matthew C. Farrelly, Brian Bradfield, Jamie Ridenhour, and Jamie Guillory. Modeling the Probability of Fraud in Social Media in a National Cannabis Survey. RTI Press, 2021. http://dx.doi.org/10.3768/rtipress.2021.mr.0046.2109.

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Cannabis legalization has spread rapidly in the United States. Although national surveys provide robust information on the prevalence of cannabis use, cannabis disorders, and related outcomes, information on knowledge, attitudes, and beliefs (KABs) about cannabis is lacking. To inform the relationship between cannabis legalization and cannabis-related KABs, RTI International launched the National Cannabis Climate Survey (NCCS) in 2016. The survey sampled US residents 18 years or older via mail (n = 2,102), mail-to-web (n = 1,046), and two social media data collections (n = 11,957). This report

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Sneha, Suruchi. Healthcare Provider Fraud Detection Analysis by applying supervised machine learning models. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-815.

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Soramäki, Kimmo, and Florian Loecker. Preserving Data Sovereignty in National Fraud Portals- a Distributed Data Architecture. FNA, 2024. https://doi.org/10.69701/dhmk3850.

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As a result of the widespread increase in consumer fraud and scams, several countries are looking to establish or strengthen cross-bank, cross-platform, and cross-industry utilities to counter fraud and scams on the national level and to augment traditional efforts at individual financial institutions1. However, questions quickly arise about how data on consumers and their payments can and should be shared across institutions. Some data sharing across Financial Institutions (FIs) is crucial for fighting consumer fraud and scams as it enables organizations to track fraudulent funds across the p

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Loecker, Florian, Amanah Ramadiah, Kimmo Soramäki, and Will Towning. Building Robust Anti-Fraud & Scam Capabilities at the National Level. FNA, 2023. http://dx.doi.org/10.69701/ektb6000.

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The introduction of instant payment systems around the world has accelerated in recent years. There are now over 80 instant payment systems globally, with more than 35 being launched in the last five years and eight currently being built. These systems bring unprecedented speed and efficiency to payments markets, with greater convenience for consumers. However, faster payments also means faster fraud. For example, in Hong Kong, the volume of fraud cases more than doubled in the four years following the introduction of the Faster Payment Service in 2018. Authorized Push Payments (APP) fraud los

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Pasupuleti, Murali Krishna. Automated Smart Contracts: AI-powered Blockchain Technologies for Secure and Intelligent Decentralized Governance. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv425.

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Abstract: Automated smart contracts represent a paradigm shift in decentralized governance by integrating artificial intelligence (AI) with blockchain technologies to enhance security, scalability, and adaptability. Traditional smart contracts, while enabling trustless and automated transactions, often lack the flexibility to adapt to dynamic regulatory frameworks, evolving economic conditions, and real-time security threats. AI-powered smart contracts leverage machine learning, reinforcement learning, and predictive analytics to optimize contract execution, detect fraudulent transactions, and

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