Inspecting Credit Card Fraud Identification Via Data Mining Classification Methods And Machine Learning Algorithms - Research Article

Aug 3, 2026

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Increased global fraud cases and substantial financial and personal losses stem from the rapid adoption of online transactional activity. Credit card fraud, a highly prevalent financial crime, poses growing concern for internet shoppers. To analyze patterns and traits of suspicious versus non-suspicious transactions, data mining is applied to normalized and anomaly data. Machine learning classifiers are then used to automatically label transactions as fraudulent or legitimate, enabling a data-driven distinction between real and counterfeit behavior.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages4

Reading Time10 min

AccessFree to download

Introduction

Credit card fraud categories

Data mining and machine learning overview

Background and Related Work

Fraud definition and detection challenges

Fraud types and credit card fraud: online vs offline

Journal of Advanced Zoology ISSN: 0253-7214 Volume 45 Issue-1 Year 2024 Page 508:511 Inspecting Credit Card Fraud Identification Via Data Mining Classification Methods And Machine Learning Algorithms Dr. Narendra Sharma1, Ms. Smita Tripathi2* 1HOD, Computer Science and Engineering, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Madhya Pradesh, India 2 *Research Scholar, Department of Computer Application, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Madhya Pradesh, India, Email: [smita_mca2004@rediffmail.com](smita_mca2004@rediffmail.com) *Corresponding Author: -Ms. Smita Tripathi *Research Scholar, Department of Computer Application, Sri Satya Sai University of Technology & Medical Sciences, Sehore, Madhya Pradesh, India, Email: [smita_mca2004@rediffmail.com](smita_mca2004@rediffmail.com) | Article History
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CC-BY-NC-SA 4.0 | Abstract:
Increased global fraud cases and significant losses for both the financial sector and people are brought about by the quick adoption of online-based transactional activity. While credit card fraud is oneof the most common and concerning financial industry crimes, internet shoppers are concerned about it more than any other. To investigate the patterns and traits of suspicious and non-suspicious transactions using normalised and anomaly data, data mining techniques were mostly used. Nevertheless, classifiers were utilised in machine learning (ML) techniques to automatically determine which transactions were fraudulent and which were not. Thus, by figuring out the patterns in the data, the combination of data mining and machine learning algorithms was able to distinguish between real and pretend transactions.
Keywords: Data Mining; Fraud Detection; Machine Learning | | --- | --- | 1. Introduction The cybercriminals frequently target the massive transactional services with the intention of fabricating credit card fraud. Unauthorised card usage, odd transaction behaviour, or transactions on an inactive card are all considered forms of credit card fraud. Generally speaking, credit card fraud falls into three categories: traditional fraud (such as stolen, phoney, and counterfeit cards), internet fraud (such as fraudulent or bogus websites belonging to merchants), and merchant-related fraud (such as triangulation and merchant cooperation) [1] . As a countermeasure against illicit operations, credit card fraud detection tools must be developed. The process of determining whether transactions are legitimate or fraudulent is generally referred to as credit card fraud detection. The process of extracting meaningful, new, and insightful patterns from massive data sets and identifying comprehensible, descriptive, and predictive models is called data mining. Based on the distinction between typical and suspect credit card transactions, data mining techniques can help detect credit card fraud by extracting valuable information from vast amounts of data through statistical and mathematical methods [2] . Machine learning is based in learning the intelligence and creating its own model for the purpose of classification, grouping, or other purposes, whereas data mining concentrated on finding valuable intelligence [3] . In computer science disciplines like spam filtering, web searching, ad placement, recommender systems, credit scoring, medication design, fraud detection, stock trading, and many more, machine learning techniques are frequently applied. Rather than rigidly following static programme instructions, machine learning classifiers work by creating a model from sample inputs and utilising that to generate predictions or judgements [4] . The technique of teaching instances to belong to predetermined classes is known as machine learning classification. Learning can be classified into various forms, including reinforcement, transduction, unsupervised, semi-supervised, and su

FAQs

What does the paper aim to do in credit card fraud detection?

It investigates patterns in suspicious and non-suspicious transactions and uses data mining with machine learning classifiers to automatically distinguish fraudulent from non-fraudulent transactions.

How are data mining and machine learning used together in the approach?

Data mining extracts meaningful, descriptive, and predictive patterns from large datasets, while machine learning classification models learn from labeled data to produce fraud/non-fraud decisions.

What kinds of credit card fraud are discussed in the related work section?

The paper explains credit card fraud as having online and offline categories, where offline fraud involves using a stolen physical card, such as at a store or contact center.

Aug 3, 2026

Upload by Eliana