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Journal articles on the topic 'Fraud detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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Premsai, Ranga. "INTEGRATING AI IN IDENTITY AND ACCESS MANAGEMENT FOR IMPROVED CYBERSECURITY POSTURE." International Scientific Journal of Engineering and Management 03, no. 12 (2024): 1–7. https://doi.org/10.55041/isjem01327.

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Abstract—The rise of cybercrime has made fraud detection a critical focus for financial services, where even minor improvements in detection rates can translate into significant savings. Traditional rule- based detection systems are increasingly unable to keep up with the complexity and evolving nature of fraudulent schemes. This work explores the role of artificial intelligence (AI), particularly deep learning, in enhancing fraud detection capabilities. Here we propose a novel AI-driven fraud detection system that incorporates Single Sign-On (SSO) identity and access management (IAM) framewor

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RATNAKUMARI, Mrs J., SHAIK NAILO ASMIN THAHENATH, TOLUSURI SRI LAKSHMI, PERAVALI NAGA DILEEP KUMAR, and KADIYAM VEERAIAH. "DETECTION OF FRAUDULENT OR DECEPTIVE PHONE CALLS USING ARTIFICIAL INTELLIGENCE." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 15, no. 1 (2024): 96–99. http://dx.doi.org/10.61841/turcomat.v15i1.14546.

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With an increase advancement of technology, fraud phone calls, including spam’s and malicious calls have become a major concern in telecommunication industry and causes millions of global financial losses every year. Fraudulent phone calls or scams and spams via telephone or mobile phone have become a common threat to individuals and organizations. Artificial Intelligence (AI) and Machine Learning (ML) has emerged as powerful tools in detecting and analyzing fraud or malicious calls. This project presents an overview of AI-based fraud or spam detection and analysis techniques, along with its c

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CHAUHAN, ARUN. "Financial Fraud Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43221.

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Detection of financial fraud is now a cause of major concern in the financial and banking industry because fraud techniques are becoming highly sophisticated. Classical rule- based systems are generally ineffective in detecting complex patterns of fraud, which call for more complex machine learning and artificial intelligence processes. The following paper discusses the different methodologies in detecting financial fraud, ranging from supervised and unsupervised learning to anomaly detection and deep neural network models. Also, it discusses how big data analytics and real- time monitoring of

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M.S, Prateeksha, B. Naga Swetha, and Manjula Patil. "CREDIT CARD FRAUD DETECTION USING MACHINE-LEARNING." International Journal of Advanced Research 11, no. 04 (2023): 1559–63. http://dx.doi.org/10.21474/ijar01/16824.

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The recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using ML classifiers: Decision Tree (DT), Logistic Regression (LR), Artificial Neural Network (ANN). To validate the performance, the proposed cred

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Bhavar, Vipul, Vishakha Borkar, Tanvi Hirave, Harsh Vishwanathan, and Ranjita Asati. "Fastag Fraud Detection: A Literature Survey." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1524–28. http://dx.doi.org/10.22214/ijraset.2023.50285.

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Abstract: National Payments Corporation of India (NPCI) has developed the National Electronic Toll Collection (NETC) program to meet the electronic tolling requirements of the Indian market.The use of FASTag is prevalent in modern society. However, it is clear that the number of FASTag frauds in the global integration and existing protection system is constantly increasing. This is why the issue of FASTag fraud detection is very important. Various FASTag frauds occur and there is no means to detect or prevent them. Among the many frauds, we will try to reduce the fraud where drivers of heavy v

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Peterson, Bonita K., and Thomas H. Gibson. "Fraud Detection and Investigation: Microcomputer Consulting Services." Issues in Accounting Education 14, no. 1 (1999): 99–115. http://dx.doi.org/10.2308/iace.1999.14.1.99.

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This nonfictional case of inventory fraud in a university setting exposes students to fraud detection and investigation. These skills are becoming increasingly important for auditors, as evidenced by the alarming rate of fraud. The accounting profession has acknowledged the seriousness of this issue with the issuance of SAS No. 82, Consideration of Fraud in a Financial Statement Audit, developed in part to improve detection of frauds by auditors. The case raises many of the fraud-related issues faced by accountants: recognizing red flags indicative of fraud; the importance of a good system of

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Rebecca, Dr B., Atmakur Divya Sree, and Prattipati Bindu. "Credit Card Fraud Detection." International Journal of Research Publication and Reviews 6, no. 4 (2025): 14854–57. https://doi.org/10.55248/gengpi.6.0425.1681.

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Khattri, Vipin, and Deepak Kumar Singh. "Parameters of automated fraud detection techniques during online transactions." Journal of Financial Crime 25, no. 3 (2018): 702–20. http://dx.doi.org/10.1108/jfc-03-2017-0024.

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Purpose This paper aims to provide information of parameters and techniques used in the automated fraud detection system during online transaction. With the increase in the use of online transactions, the concerns regarding data security have also increased. To tackle the frauds, lot of research has been done and plethora of papers are available on the related topics. The purpose of this paper is to provide the clear pathway for researchers to move in the direction of development of automated fraud detection system to prevent the fraud during online transaction. Design/methodology/approach Thi

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Singh, Ajeet, and Anurag Jain. "An Empirical Study of AML Approach for Credit Card Fraud Detection–Financial Transactions." International Journal of Computers Communications & Control 14, no. 6 (2019): 670–90. http://dx.doi.org/10.15837/ijccc.2019.6.3498.

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Credit card fraud is one of the flip sides of the digital world, where transactions are made without the knowledge of the genuine user. Based on the study of various papers published between 1994 and 2018 on credit card fraud, the following objectives are achieved: the various types of credit card frauds has identified and to detect automatically these frauds, an adaptive machine learning techniques (AMLTs) has studied and also their pros and cons has summarized. The various dataset are used in the literature has studied and categorized into the real and synthesized datasets.The performance ma

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Gunjan, Sahoo, Manjula V. Dr, Anuradha Bhakta K, S. A. Poojashree, and D. Shenoy Divya. "Fraud Detection in Online Transaction." Journal of Network Security and Data Mining 3, no. 2 (2020): 1–8. https://doi.org/10.5281/zenodo.3989160.

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<em>Credit card fraud ranges from larceny and fraud committed involving a payment card, like a credit card or debit card, as a deceitful supply of funds during the payments. The aim may also be to get merchandise by not paying and also getting unauthorized funds from an account. These frauds are also an adjunct to identity theft. Though incidences of credit card fraud are restricted to about 0.1% of all card transactions, they have resulted in financial loss as the transactions have been large sum. In this present digital era there is increased demand for a secure online transaction and there

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Baisholan, Nazerke, J. Eric Dietz, Sergiy Gnatyuk, Mussa Turdalyuly, Eric T. Matson, and Karlygash Baisholanova. "FraudX AI: An Interpretable Machine Learning Framework for Credit Card Fraud Detection on Imbalanced Datasets." Computers 14, no. 4 (2025): 120. https://doi.org/10.3390/computers14040120.

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Credit card fraud detection is a critical research area due to the significant financial losses and security risks associated with fraudulent activities. This study presents FraudX AI, an ensemble-based framework addressing the challenges in fraud detection, including imbalanced datasets, interpretability, and scalability. FraudX AI combines random forest and XGBoost as baseline models, integrating their results by averaging probabilities and optimizing thresholds to improve detection performance. The framework was evaluated on the European credit card dataset, maintaining its natural imbalanc

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Journal, IJSREM. "Secure Scan -Analysis of Credit Card Fraud Detection Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 11 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27368.

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Credit Card usage has been drastically increased across the world , now people believe in going cashless and are completely dependent on online transactions .The credit card has made the digital transaction easier and nowadays credit card frauds are drastically increasing in numbers compared to earlier times . A powerful fraud detection system is required to stop these frauds . Fraud detection is the process of monitoring the transaction behaviour of a cardholder to detect whether an incoming transaction is authentic and authorised or not otherwise it will be detected as illicit .Machine learn

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C Kalal, Vijayalaxmi. "Credit Card Fraud Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48811.

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Abstract —This project is all about finding credit card fraud using machine learning. Credit card fraud is been increasing a lot nowadays, With the criminals using fake tricks and identities in order to steal money So, It is very important to find a way to stop these frauds. Our project majorly carries out at finding illegal activities. As criminals change their mind and methods often, it’s very hard to catch them easier. First, the system starts collecting the data on how these credit cards are used and trains a model using algorithm like Random Forest and Decision Tree. Then, it checks new d

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Yu, Frank, and Xiaoyun Yu. "Corporate Lobbying and Fraud Detection." Journal of Financial and Quantitative Analysis 46, no. 6 (2011): 1865–91. http://dx.doi.org/10.1017/s0022109011000457.

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AbstractThis paper examines the relation between corporate lobbying and fraud detection. Using data on corporate lobbying expenses between 1998 and 2004, and a sample of large frauds detected during the same period, we find that firms’ lobbying activities make a significant difference in fraud detection: Compared to nonlobbying firms, on average, firms that lobby have a significantly lower hazard rate of being detected for fraud, evade fraud detection 117 days longer, and are 38% less likely to be detected by regulators. In addition, fraudulent firms on average spend 77% more on lobbying than

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Atchaya, S. "Credit Card Fraud Detection using ANN." International Journal for Research in Applied Science and Engineering Technology 12, no. 2 (2024): 170–75. http://dx.doi.org/10.22214/ijraset.2024.58284.

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Abstract: Frauds in credit card transactions are common today as most of us are using the credit card payment methods more frequently. This is due to the advancement of Technology and increase in online transaction resulting in frauds causing huge financial loss. Therefore, there is need for effective methods to reduce the loss. In addition, fraudsters find ways to steal the credit card information of the user by sending fake SMS and calls, also through masquerading attack, phishing attack and so on. This paper aims in using the multiple algorithms of Machine learning such as support vector ma

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Elizabeth, Oommen. "Role of Big Data in Regulating and Combating Financial Fraud in India." Young Researcher S14, no. 1B (2025): 256–61. https://doi.org/10.5281/zenodo.14873761.

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<em>F</em><em>inancial fraud is a significant concern for the Indian economy, with the country's banking sector reporting frauds worth over </em><em>₹</em><em>1.85 lakh crore in 2020-21. Big data analytics has emerged as a critical tool for detecting and preventing financial fraud. The implementation of big data analytics within India's financial sector has the potential to yield substantial annual cost savings through enhanced fraud detection and prevention mechanisms. This technological advancement serves to safeguard both consumers and businesses while simultaneously reinforcing the banking

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Ranjan, Ashish, and Sateesh Kumar Awasthi. "Fraud Detection Using Machine Learning and Community Detection Algorithm." International Journal of Microsystems and IoT 2, no. 9 (2024): 1211–17. https://doi.org/10.5281/zenodo.14102397.

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Online fraud has indeed been a growing problem in recent years, causing significant financial losses for individuals, merchants, and banks. Machine learning has demonstrated its effectiveness in detecting and mitigating credit card fraud. This paper aims to review various online credit card techniques for fraud detection utilizing Machine Learning algorithms and evaluate them based on performance metrics such as precision, accuracy, and specificity. Additionally, the paper proposes a Fraud Detection System (FDS) that employs a supervised Random Forest algorithm to enhance fraud detection accur

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Poradova, Monika. "Creative accounting as one of the global tools for detecting fraud in Europe." SHS Web of Conferences 129 (2021): 03024. http://dx.doi.org/10.1051/shsconf/202112903024.

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Research background: The issue of fraud is a real and not an exceptional phenomenon in today’s global economies. Fraud arises in businesses at different levels and from different motivations. However, with the development of fraud, methods are also being developed to help detect such fraud. Therefore, the present paper focused on creative accounting as one of the global tools for detecting these scams. The present paper consists of four parts. The first part deals with the issue of creative accounting. The second part describes fraud techniques such as “windows dressing”, “off-balance-sheet fi

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G, Kavyasri, Keerthana D, Keerthi Reddy B, Keerthi K, KesavaAditya J, and Prof S. Ramesh Kumar. "Deep Learning based Credit Card Fraudulency Detection System." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 477–81. http://dx.doi.org/10.22214/ijraset.2024.61553.

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Abstract: Huge increase in the internet usage has been observed since last decade. It led to the emergence of services like ecommerce, tap and pay systems, online bill payment systems, etc. have proliferated and become more widely used. Due to various online payment options introduced by e- commerce and other numerous websites, the possibility of online fraud has risen drastically. Thus, due to an increase in fraud rates, research on analyzing and detecting fraud in online transactions has begun utilizing various machine learning techniques. The Deep Learning techniques viz., Convolutional Neu

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Lee, Jung-won, Jong-hoon Eom, Ta-hum Park, and Sung-ho Kim. "Study on Fraud and SIM Box Fraud Detection Method in VoIP Networks." Journal of Korean Institute of Communications and Information Sciences 40, no. 10 (2015): 1994–2005. http://dx.doi.org/10.7840/kics.2015.40.10.1994.

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Singh Manku, Amandeep. "The Role of Forensic Accounting in Uncovering Financial Frauds During the 2008 Financial Crisis: Case Studies, Post-Crisis Reforms, and Global Comparisons." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem02816.

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The 2008 financial crisis exposed severe vulnerabilities in the global financial system, characterized by widespread fraud, mismanagement, and deceptive financial practices. Forensic accounting played a critical role in uncovering these fraudulent activities by tracing financial irregularities, detecting false reporting, and providing legal evidence necessary for prosecution. This paper explores the pivotal role forensic accounting played during the crisis, focusing on key case studies such as Lehman Brothers, the Madoff Ponzi scheme, and other notable corporate frauds. Additionally, the paper

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Dhanwani, Prof D. C. "Online Fraud Detection System." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 294–99. http://dx.doi.org/10.22214/ijraset.2024.59557.

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Abstract: Financial services are used everywhere and function with high complexity. With the increase in online transacting, frauds too are increasing alarmingly. An automated Fraud Detection System is thus required. With millions of transactions taking place, it is practically impossible to detect frauds manually with good speed and accuracy. We propose a system is that provides a robust, cost effective, efficient yet accurate solution to detect frauds in both online payment transactions and credit card payments. The proposed solution is a Machine Learning model that will serve the purpose of

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Yadav, Suman, Ruchi Sharma, Annu Dabas, et al. "Improved security in credit cards via duplicitous contract detection." Journal of Information and Optimization Sciences 46, no. 1 (2025): 75–79. https://doi.org/10.47974/jios-1853.

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Credit card fraud is among the most prominent financial frauds in the ever-growing industry. Credit card firms must detect fraudulent transactions to ensure clients are not billed for products they did not purchase. With technological advancements, fraudulent transactions have increased, driven by the proliferation of online payment options. Machine learning algorithms play a pivotal role in detecting fraud by analyzing transaction behavior. This study presents a model achieving 99.92% accuracy using techniques like Random Forest Classifier, SVC, and SGD Classifier. The model employs dataset p

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Yu, Zhizhi, Chundong Liang, Xinglong Chang, Dongxiao He, Di Jin, and Jianguo Wei. "Dynamic Neighborhood Modeling via Node-Subgraph Contrastive Learning for Graph-Based Fraud Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 13115–23. https://doi.org/10.1609/aaai.v39i12.33431.

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Fraud detection that aims to discern frauds from the majority of benigns has become an increasingly prominent research field. Recently, Graph Neural Networks (GNNs) have been widely applied in graph-based fraud detection due to their outstanding data analysis and mining capabilities. However, owing to the inherent homophily-heterophily mixture and class imbalance of fraud graphs, most GNNs with homophily assumption inevitably suffer from local abnormal signal loss during information propagation, posing significant challenges in situations where frauds are rare and valuable. To address the afor

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Amusan, D.G. "Hybrid Design using Counter Propagation Neural Network-Genetic Algorithm Model for the Anomaly Detection in Online Transaction." International Journal of Advances in Scientific Research and Engineering (ijasre) 5, no. 9 (2019): 107–14. https://doi.org/10.31695/IJASRE.2019.33512.

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In e-commerce, credit card fraud is an evolving challenge. The increase in the number of credit card transactions provides more opportunity for fraudsters to steal credit card numbers and execute fraud. Fraud detection is a continuously evolving discipline to tackle ever changing tactics to commit fraud. Existing fraud detection systems have not been so much efficient to reduce fraud transaction rate. Improvement in fraud detection practices has become essential to maintain existence of payment system. This research designed hybrid of Counter Propagation Neural Network and genetic algorithm (C

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Gupta, Amit, M. C. Lohani, and Mahesh Manchanda. "Utilizing mathematical concepts of heat map for an intelligent and secure approach to efficiently detect credit card fraud." Journal of Interdisciplinary Mathematics 26, no. 8 (2023): 1837–54. http://dx.doi.org/10.47974/jim-1761.

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In the current scenario of digital world, every year the financial institutions have to face billions of dollars losses due to fraudulent transactions. Out of different categories of financial frauds credit card transaction fraud is the most common. To reduce the effect of these fraud transactions there is a need for a well-designed and secured fraud detection system with a state of art fraud detection model. Our work’s primary contribution is the creation of a fraud detection system that makes use of some mathematical usage of creating heat maps which is then enhanced with the use of a deep l

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Najmusehar, H., Firdous Zainab, V. Sushma, Shahista Banu M, and Pasha S. Aftab. "A Survey Analysis on Credit Card Fraud Detection Using Machine Learning." Advanced Innovations in Computer Programming Languages 5, no. 1 (2023): 20–24. https://doi.org/10.5281/zenodo.7687672.

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<em>Fraud is an act of depriving a person/organization of ownership or money through willingness, deception, or other unfair means. The number of online payments has increased due to online shopping and numerous other websites, as a result so are online fraud increased. Credit card providers are searching for the best systems and technology to reduce and detect credit card fraud. Here, we want to suggest a survey study that introduces a cutting-edge, machine-learning method for detecting credit card fraud. Machine learning techniques can make detections accurately, quickly and in less cost. Th

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K. Maithili, Et al. "Machine Learning-Based Approaches for Credit Card Fraud Detection: A Comprehensive Reviewz." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1754–61. http://dx.doi.org/10.17762/ijritcc.v11i10.8751.

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The objective of data analytics is to discover hidden patterns and use them to guide wise judgements in a range of circumstances. Theft of credit cards has significantly grown as a result of modern technologies and has become a popular target for scam artists. Publicly available databases on credit card fraud are very unbalanced. As more people conduct business online, Fraud involving credit cards has grown to be a serious problem for both consumers and financial establishments. standard rule-based fraud detection strategies have shown to be insufficient to combat fraudsters' ever-evolving tac

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Rahman, Pura, Mannan Arifuddin, and Ningsih Nurdiana. "Internal Auditor Competence in Covid-19 Budget Fraud Detection." International Journal of Innovative Science and Research Technology 7, no. 6 (2022): 1286–92. https://doi.org/10.5281/zenodo.6849942.

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This study aims to analyze the effect of riskbased audit techniques, the competence and experience of auditors on the ability of government internal auditors to detect the fraud in Covid 19 budget. The survey was conducted on government auditors who work at Regional Inspectorate of Enrekang Regency amounted to 47 auditors. The data were collected by questionnaires and analyzed by multiple regression analysis. The results showed that the risk-based audit techniques, competence and experience of auditors have a significant positive effect on the detection of Covid-19 budget fraud in Enrekang Reg

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Chen, Yuhang, Jiaxin Jiang, Shixuan Sun, Bingsheng He, and Min Chen. "RUSH: Real-Time Burst Subgraph Detection in Dynamic Graphs." Proceedings of the VLDB Endowment 17, no. 11 (2024): 3657–65. http://dx.doi.org/10.14778/3681954.3682028.

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Graph analytics have been effective in the data science pipeline of fraud detections. In the ever-evolving landscape of e-commerce platforms like Grab or transaction networks such as cryptos, we have witnessed the phenomenon of 'burst subgraphs,' characterized by rapid increases in subgraph density within short timeframes---as a common pattern for fraud detections on dynamic graphs. However, existing graph processing frameworks struggle to efficiently manage these due to their inability to handle sudden surges in data. In this paper, we propose RUSH ( R eal-time b U rst S ubgrap H detection fr

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Kanapickienė, Rasa, and Živilė Grundienė. "Actions to detect fraud in financial statements." Buhalterinės apskaitos teorija ir praktika, no. 15A (July 9, 2014): 37–48. http://dx.doi.org/10.15388/batp.2014.15a.3.

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The information provided in financial statements should be neutral and reliable thus enabling the users of the financial statements to make financially legitimate decisions regarding the perspectives of the company’s development. Therefore the reference documents regulating the composition of financial statements determine that the distortion of the assets, income, costs or other data is strictly forbidden. Nevertheless, fraud in financial statements is a relatively frequent phenomenon in business practice. Hence, in order to detect frauds effectively it is essential to systematize the actions

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Balaji Dhashanamoorthi. "Analyzing detection algorithms for cybersecurity in financial institutions." International Journal of Science and Research Archive 11, no. 2 (2024): 558–68. http://dx.doi.org/10.30574/ijsra.2024.11.2.0478.

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Frauds in financial services are an ever-increasing phenomenon, and cybercrime generates multimillion revenues. Even a small improvement in fraud detection rates would lead to significant savings. Traditional rule-based systems have limitations in blocking potentially fraudulent transactions. This chapter explores how machine learning, specifically supervised and unsupervised learning, can address these limitations more effectively. We present a novel AI-based fraud detection system that combines supervised and unsupervised models. In the batch layer, transaction data undergoes pre-processing

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Arora, Gurminder Kaur. "Detection and Reporting of Corporate Frauds in India." VEETHIKA-An International Interdisciplinary Research Journal 11, no. 1 (2025): 51–63. https://doi.org/10.48001/veethika.1101004.

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Corporate frauds are persuasive and complex situations that cast a shadow on the integrity of businesses worldwide. They include, though are not limited to, deceitful practices such as misrepresentation of financial statements, manipulation of financial data, embezzlement of funds, falsification of accounts, or other illegal actions or omissions committed within a corporate entity with the intention of granting an unfair advantage to any individual or group at the expense of the organization and its stakeholders. Corporate frauds reveal shortcomings of the legal systems and pose a significant

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Zhang, Shenghao, Neil Charness, and Walter Boot. "THE RELEVANCE OF COGNITIVE ABILITIES IN FRAUD DETECTION." Innovation in Aging 7, Supplement_1 (2023): 1140. http://dx.doi.org/10.1093/geroni/igad104.3659.

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Abstract Some previous studies suggest that cognitive decline might be responsible for older adults being susceptible to phishing attempts and frauds, but results are not consistent. The current study utilized a structural equation modeling approach to examine the role of different cognitive abilities in fraud detection. We used data from the baseline of the Intervention Comparative Effectiveness for Adult Cognitive Training (ICE-ACT, NCT03141281) Trial. The sample included 230 cognitively normal community-dwelling older adults (Mean age = 72). Reasoning was measured by Raven’s Advanced Progre

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Pricylia, Dhea Aristy, and Martinus Budiantara. "Pendeteksian Fraud Dalam Laporan Keuangan dengan Cash Flow Shenanigans pada Perusahaan BUMN yang Terdaftar di BEI Periode 2018-2022." Reslaj : Religion Education Social Laa Roiba Journal 6, no. 3 (2023): 1835–46. http://dx.doi.org/10.47467/reslaj.v6i3.5485.

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 Indonesia is one of the developing countries where the phenomenon of fraud is not unfamiliar to the public. Types of fraud that occur in Indonesia include financial statement fraud, misappropriation of state assets/wealth, and corruption. Therefore, it is essential to address financial statement fraud. This research aims to prove whether cash flow shenanigans have an impact on detecting fraud in financial statements. The results of the study indicate that days sales outstanding (DSO) significantly influences the detection of fraud in financial statements. Delaying payment of obligations

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Olajide Soji Osundare, Chidiebere Somadina Ike, Ololade Gilbert Fakeyede, and Adebimpe Bolatito Ige. "Application of Machine Learning in Detecting Fraud in Telecommunication-Based Financial Transactions." Computer Science & IT Research Journal 4, no. 3 (2023): 458–77. http://dx.doi.org/10.51594/csitrj.v4i3.1499.

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The increasing integration of telecommunications with financial services has brought about significant advancements in the accessibility and efficiency of financial transactions. However, this convergence has also led to a rise in fraudulent activities, posing substantial risks to both service providers and users. The application of machine learning (ML) in detecting fraud within telecommunication-based financial transactions offers a promising solution to these challenges. This abstract explores the potential of ML techniques to enhance the detection and prevention of fraud in this domain. Ma

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Luo, Yunxin. "Reduce Fraud: From Fraud Motivation to Fraud Avoidance." Advances in Economics, Management and Political Sciences 9, no. 1 (2023): 173–78. http://dx.doi.org/10.54254/2754-1169/9/20230375.

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The development of information technology and digital transformation in recent years, not only brings the innovation of fraud detection and the progress of fraud avoidance but also brings new fraud motivation and more serious fraud impact. This paper mainly studies the motivation, impact, detection, and avoidance of fraud, through the discussion of these aspects to assist to reduce the occurrence of fraud. The research method of this paper is to summarize and analyze the literature related to the fraud in recent 10 years. This paper finds that starting from the motivation of fraud, impact, det

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Gupta, Suraj. "Fraud Detection System Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2423–26. https://doi.org/10.22214/ijraset.2025.68721.

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Abstract: Intoday’s digitalenvironment frauddetectionisamajorproblemthat impactsfinancialservices, e-commerce, banking and insurance.Because online transactions are growing so quickly, scammers are always coming up with new ways to get around established security measures. Because of their restricted feature extraction capabilities and dependence on predefined rules, traditional rule-based and machine learning approaches frequently fall short in identifying complexfraudpatterns. Inthisstudy,weinvestigatetheuseofdeep learningtechniquesforfraud detection, suchasLongShort-TermMemory(LSTM) network

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Metha, Shubham. "AI-Driven Fraud Detection: A Risk Scoring Model for Enhanced Security in Banking." Journal of Engineering Research and Reports 27, no. 3 (2025): 23–34. https://doi.org/10.9734/jerr/2025/v27i31415.

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As technology makes advancements so does the risk of accessing it for wrong doings. In recent years as we moved from traditional banking systems to online banking and the volume of digital transactions has increased eccentric. This also comes up with increasing risk of fraudulent activities like accessing bank accounts, credit card frauds, account frauds, dormant account fraud, and many others. Detecting fraud activities is and crucial part of banking system. This research explores the application of artificial intelligence (AI) in detecting potentially fraudulent activities by generating a ri

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Othman, Intan Waheedah. "Financial Statement Fraud: Challenges and Technology Deployment in Fraud Detection." International Journal of Accounting and Financial Reporting 11, no. 4 (2021): 1. http://dx.doi.org/10.5296/ijafr.v11i4.19067.

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Fraudulent financial reporting and other forms of earnings misstatement are catastrophic and pose a considerable threat to capital market stability. This study reviews the literature on existing technology-based methods of detecting financial statement fraud. The aim is to describe the challenges of predicting a rare fraud event and provide an understanding of the various data-mining based techniques for financial statement fraud detection. Given that fraudsters are becoming more adaptable and are constantly devising new ways to outwit the fraud detection system, the study provides directions

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