Authors

Y Mohan Das

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Gadidam Glory Srujana

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Kontham Ganagadhar

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Chagam Uttam Kumar Reddy

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Ediga Dinesh

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Kurnool Sohel Ahamad

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Abstract

This project highlights the complexities and requirements of detecting and understanding credit card fraud. It showcases how machine learning algorithms, specifically deep learning models, can be employed to classify and verify suspicious transactions. A credit card transaction is analyzed, and using a deep neural network, our detection and identification system precisely determines the legitimacy of the transaction. In this project, we'll create a predictive model that will categorize credit card transactions into legitimate or fraudulent groups. With the help of this model, financial institutions can identify and prevent credit card fraud, which is a crucial task for secure online transactions.

Keywords

Deep Learning Predictive Modeling Credit Card Fraud Detection and Prevention

Citation of this Article

Y Mohan Das, Gadidam Glory Srujana, Kontham Ganagadhar, Chagam Uttam Kumar Reddy, Ediga Dinesh, & Kurnool Sohel Ahamad. (2025). Predictive Modeling for Credit Card Fraud Analysis. Journal of Artificial Intelligence and Emerging Technologies. 2(3), 12-17. Article DOI: https://doi.org/10.47001/JAIET/2025.203003  

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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