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71 articles for “Fraud Detection”
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Enhancing Credit Card Fraud Detection Using Device Fingerprinting and Behavioral Biometrics
Abstract: Credit card fraud is a growing global concern, with financial losses projected to reach $ 43.47 billion by 2028. Credit card fraud poses a major challenge in the financial industry, resulting in substantial financial losses and security risks. This research introduces a Machine Learning-based Credit Card Fraud Detection System designed to improve the accuracy of fraud identification. Due to the imbalanced nature of fraud datasets, SMOTE (Synthetic Minority Over-sampling Technique) …
Published in Journal Of Network security · Vol. 13, Issue 2, 2025 · pp. 40–50 Read article
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Vehicle Insurance Fraud Detection Using Supervised Neural Network Model
Abstract: Vehicle insurance fraud is a serious problem that causes huge financial losses to insurance companies. In recent years, developing fraud detection models using machine-learning techniques has been of great interest. This study proposes a novel approach for vehicle insurance fraud detection using Supervised Neural network model on the Kaggle Vehicle Insurance Fraud Detection dataset. The initial step in this study involves performing data pre-processing and feature extraction on the dataset, …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 10, Issue 1, 2023 · pp. 32–39 Read article
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A Comprehensive Analysis of Machine Learning Models for Credit Card Fraud Detection
Abstract: This paper presents an indepth comparison of various machine learning models—Logistic Regression, Support Vector Classification (SVC), and Neural Networks (NN)—in the context of credit card fraud detection. The analysis spans multiple performance metrics, including accuracy, F1 score, precision, recall, and computational efficiency. Logistic Regression demonstrates competitive performance in terms of accuracy, but its poor precision renders it unsuitable for fraud detection tasks. Conversely, the Neural Network exhibits balanced precision and …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 Read article
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Fraud Detection in Government Procurement Using Machine Learning
Abstract: Fraud represents a significant challenge in the realm of procurement, with estimates indicating that between 12 and 30% of global procurement budgets are lost to fraudulent activities (OECD, 2023). The pervasive nature of procurement fraud, which may encompass a range of deceptive practices such as bid rigging, invoice fraud, and procurement kickbacks, not only undermines the integrity of financial operations but also results in substantial losses for organizations. These losses …
Published in Journal of Open Source Developments · Vol. 12, Issue 2, 2025 · pp. 19–34 Read article
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Cloud-driven Fraud Detection: Evaluating Decision Tree and Random Forest Classifiers for Credit Card Transaction Security
Abstract: With the alarming rise in global financial fraud, necessitating substantial annual losses, modern techniques for fraud detection are continuously evolving across various business domains. Fraud detection involves constant monitoring of user activities to estimate, perceive, or prevent undesirable behaviour. Cloud Computing emerges as a promising solution, accelerating application deployment, fostering creativity and innovation, reducing costs, and enhancing overall business acumen. This study introduces a cloud-driven approach to fraud detection, specifically …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 1, 2024 · pp. 13–27 Read article
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A Survey of Fraud Detection Techniques for Credit Card Based Transaction Processing
Abstract: The wide emergence of electronic-commerce has widened the extensive usage of credit card for online transactions. However, there is also a high rise in malicious transaction and fraudulent associated with the credit cards. In this study, we present several models and algorithm used in data mining for the detection of such malicious fraudulent or thefts. Such algorithm learns the transaction patterns and clusters the pattern of sequences usually involving with …
Published in Recent Trends in Parallel Computing · Vol. 2, Issue 1, 2015 · pp. 10–15 Read article
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Detect Fraud Bank Checks with Convolutional Neural Network Processing Algorithm
Abstract: AbstractThe purpose of this study is to design and build a fraud check detection system with a Convolutional Neural Network (CNN) algorithm to quickly and easily detect a fraud or altered bank check in real time. A MATLAB Deep Learning Toolbox with related CNN algorithm is used to assist this study to quickly detect any fraud or altered bank check when it is deposited and scanned by a scanner in …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 7, Issue 3, 2020 · pp. 18–29 Read article
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Comparative Analysis of algorithms for Credit Card Fraud Detection using Data Mining: A Review
Abstract: AbstractData mining is a technique to extract new useful information from existing dataset. It is used to predict various patterns from present dataset. It has a great application in the field of bank and finance. In today’s world, everything is being online, means online shopping, online banking and online payment. The problem faced by them is credit card fraud. In this, we use the data mining techniques such as classification, …
Published in Journal of Advanced Database Management & Systems · Vol. 6, Issue 2, 2019 · pp. 12–17 Read article
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A Secure Approach for Cross-chain Transactions Using Machine Learning Model
Abstract: The ability to conduct transactions or transfer assets between different blockchain networks is referred to as cross-chain transactions. It enables users to transfer assets from one blockchain network to another. In the Cryptocurrency ecosystem, the risk of fraudulent activities has become a significant concern. Due to these fraudulent activities, the cross-chain transactions have encountered challenges in terms of security and integrity. The need for robust fraud detection mechanisms becomes crucial …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 10, Issue 3, 2023 · pp. 17–24 Read article
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Credit Card Fraud Detection Using Deep Learning Techniques
Abstract: Credit card frauds have become very common nowadays and lot of cases has been reported in the recent past with the increase in cybercrimes. Online credit card frauds can be efficiently detected exploiting deep-learning techniques. This paper proposes two unsupervised learning algorithms namely Auto encoder and RBM (Restricted Boltzmann Machine) that acquire information from new transactions and estimate anomalous patterns from these transactions to accurately predict number of credit card …
Published in Journal of Web Engineering & Technology · Vol. 7, Issue 1, 2020 · pp. 30–47 Read article
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Card Fraud Detection Using Artificial Neural Network and Multilayer Perception Algorithm
Abstract: Fraud has posed a significant challenge for merchants, especially in the online business sector, over the course of many years. This is primarily due to the advancements in technology that have made credit card transactions a common method of payment. Credit card fraud refers to the unauthorized use of a credit card by an individual for personal purposes, without the owner's consent and with no intention of paying for the …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 1, Issue 1, 2023 · pp. 21–30 Read article
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Credit Card Fraud Detection Using Machine Learning Techniques
Abstract: Credit card fraud has become a significant concern in today's digital economy, with billions of dollars being lost annually to fraudulent transactions. Conventional rule-based approaches frequently prove inadequate in addressing the constantly changing strategies employed by fraudsters. Machine learning methods have emerged as robust solutions for detecting credit card fraud, presenting the capability to accurately identify fraudulent transactions promptly. In this study, we investigate the efficiency of three widely used …
Published in Journal of Open Source Developments · Vol. 11, Issue 1, 2024 · pp. 1–7 Read article
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A Review: Identification of Credit Card Fraud Using Machine Learning and Anomaly Detection Approach’s on Imbalanced Data
Abstract: Credit cards are among the most widely used payment methods in today's culture, and online purchases have become quite popular. Credit card fraud has emerged as a problem in this industry as a direct result of its popularity. The issue of credit card fraud is becoming a global problem. Credit card fraud has increased due to the widespread use of these payment methods. Thanks to credit card use, e-commerce has …
Published in Recent Trends in Programming languages · Vol. 10, Issue 3, 2023 · pp. 27–40 Read article
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Fraudulent Data Detection of Credit Card by Using Random Forest Method to Calculate ROC and Confusion Matrix
Abstract: Credit card fraud defaults in trillions of dollars online merchants. Researchers have more and more complex methods of detecting fraud using algorithms for machine learning, but there are rare reports of hands-on implementations. We describe the implementation of an extortion discovery framework in a huge e-tail retailer. The paper looks at the combination of manual and robotized classification and offers experiences into the whole handle of advancement. The paper can …
Published in Journal of Advanced Database Management & Systems · Vol. 7, Issue 1, 2020 · pp. 15–22 Read article
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Enhancing Security of UPI Payments Through Artificial Intelligence
Abstract: The integration of Artificial Intelligence (AI) into modern digital payment systems has brought about a significant transformation in the banking and financial sector. Among these systems, the Unified Payments Interface (UPI), developed by the National Payments Corporation of India (NPCI), has emerged as one of the most successful real-time payment mechanisms due to its simplicity, accessibility, and efficiency. With the incorporation of AI technologies, UPI has witnessed notable improvements in …
Published in International Journal of Mobile Computing Technology · Vol. 3, Issue 2, 2025 · pp. 7–12 Read article
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Detecting Fake Images with Python: A Simple Approach Using OpenCV and MD5
Abstract: In today’s modern era, ensuring the authenticity of pictures is crucial. This article presents a straightforward method for identifying fake pictures using the widely used OpenCV and MD5 technologies. OpenCV helps us examine pictures for irregularities such as abnormal colors or shapes, while MD5 is a unique digital fingerprint for each image. Our approach combines these tools to create a robust fraud detection system. OpenCV carefully examines various aspects of …
Published in Journal of Operating Systems Development & Trends · Vol. 11, Issue 1, 2024 · pp. 45–49 Read article
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Exploring Artificial Intelligence in the Finance Sector
Abstract: Artificial intelligence (AI), machine learning (ML), and progressive algorithms illustrate a substantial technological leap with wide applications across sectors like automobiles, healthcare, gaming, finance, entertainment, and more. The foremost objective of AI is to produce intelligent, independent systems capable of self-sustaining decision-making. This study delivers a concise summary of AI, concentrating on its transformative influence on finance, especially within banking, asset firms, derivatives markets, and insurance enterprises. It summarizes the …
Published in E-Commerce for Future & Trends · Vol. 11, Issue 3, 2024 · pp. 16–23 Read article
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CABGENIE: AI-Powered Multi-Provider Cab Booking and Ride Management System
Abstract: Urban transportation today demands far more than a simple cab booking interface. CABGENIE is an AI-powered, multi-provider ride management platform that brings together Uber, Ola, Rapido, and BlaBlaCar under one intelligent roof. Rather than forcing passengers to juggle multiple apps and compare prices manually, CABGENIE does the thinking for them. Its embedded AI Agent analyses real-time availability, fare competitiveness, driver ratings, estimated arrival times, weather conditions, and personal ride history …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 13, Issue 3, 2026 Read article
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Cyber Security Implementation Through Deep Learning Based Fraudulent URL Detection
Abstract: A malicious URL, sometimes known as Spam, malicious ads, and drive-by vulnerabilities may all be found on a site that is malevolent. To find rogue URLs, you need to be quick. Researchers previously used blacklisting, regular expression searching and signature matching in their investigations. These strategies are worthless for discovering new malicious URLs or updated versions of previously problematic URLs. A machine learning-based approach may be developed to overcome this …
Published in Recent Trends in Programming languages · Vol. 9, Issue 2, 2022 · pp. 35–42 Read article
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Predicting the Computer Access Needs of Employee Using Data Mining Algorithms
Abstract: Every business relies on data to better organize and access the information to improve their business operations and to identify opportunities for improvement. A better framework improves the ability to valuable products and better services to their customers. The wide swaths of data that every organization gathers requires a proper methodology to gain knowledge about the latest trends in the market. Data mining is the process that analyses the data …
Published in Journal of Advances in Shell Programming · Vol. 10, Issue 1, 2023 · pp. 12–16 Read article