Authors

S Premsagar

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

P Vishal Karthik

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

T Srikanth

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

Abstract

The rapid growth of digital banking, e-commerce, online payment platforms, and financial technologies has significantly increased the volume of electronic transactions while simultaneously creating new opportunities for fraudulent activities. Financial fraud has become one of the most critical challenges faced by banks, financial institutions, and payment service providers, resulting in substantial financial losses, operational risks, and reduced customer trust. Traditional rule-based fraud detection systems often struggle to identify sophisticated and evolving fraud patterns because they rely on predefined rules that cannot adapt to emerging threats in real time. This study proposes a Predictive Analytics and Machine Learning-based Financial Fraud Detection System designed to identify fraudulent transactions accurately and efficiently during real-time transaction processing. The proposed framework integrates advanced data preprocessing techniques, feature engineering, anomaly detection, and supervised machine learning algorithms to analyze historical transaction data and predict fraudulent behavior before financial damage occurs. Various transaction attributes, including transaction amount, frequency, geographical location, device information, user behavior, transaction timing, and spending patterns, are analyzed to build predictive models capable of distinguishing legitimate transactions from suspicious activities. The system continuously learns from newly available transaction data, enabling it to improve detection accuracy and adapt to changing fraud strategies over time. Predictive analytics further enhances the framework by identifying hidden trends, risk scores, and abnormal behavioral patterns that may indicate potential fraud before it is fully executed. The proposed approach aims to minimize false positives while maintaining high fraud detection sensitivity, thereby improving customer experience and reducing unnecessary transaction interruptions. Experimental evaluation demonstrates that machine learning models achieve superior performance compared to conventional rule-based approaches in terms of accuracy, precision, recall, F1-score, and detection speed. The proposed system provides a scalable, intelligent, and cost-effective solution for financial institutions seeking to strengthen transaction security, mitigate financial risks, and enhance regulatory compliance. This research contributes to the advancement of intelligent financial security systems by demonstrating how predictive analytics and machine learning can be effectively integrated to provide reliable, adaptive, and real-time fraud detection in modern digital financial ecosystems.

Keywords

Financial Fraud Detection Predictive Analytics Machine Learning Artificial Intelligence (AI) Real-Time Transaction Monitoring Fraud Prediction Anomaly Detection Classification Algorithms Risk Assessment Financial Security Data Mining Banking Systems Digital Payments Cybersecurity Fraud Prevention.

Citation of this Article

S Premsagar, P Vishal Karthik, & T Srikanth. (2026). Predictive Analytics and Machine Learning for Real-Time Financial Fraud Detection. Current Journal of Engineering and Science Research. 3(6), 10-19. Article DOI: https://doi.org/10.47001/CJESR/2026.306002

Licence Copyright (c) 2026 Current Journal of Engineering and Science Research. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

References

C. C. Aggarwal, Data Mining: The Textbook. Cham, Switzerland: Springer, 2015.

T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2nd ed. New York, NY, USA: Springer, 2009.

I.Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA, USA: MIT Press, 2016.

Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436–444, 2015.

F. Carcillo, Y.-A. Le Borgne, O. Caelen, Y. Kessaci, F. Oblé, and G. Bontempi, "Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection," Information Sciences, vol. 557, pp. 317–331, 2021.

A.Dal Pozzolo, O. Caelen, Y.-A. Le Borgne, S. Waterschoot, and G. Bontempi, "Learned Lessons in Credit Card Fraud Detection from a Practitioner Perspective," Expert Systems with Applications, vol. 41, no. 10, pp. 4915–4928, 2014.

S. Bhattacharyya, S. Jha, K. Tharakunnel, and J. C. Westland, "Data Mining for Credit Card Fraud: A Comparative Study," Decision Support Systems, vol. 50, no. 3, pp. 602–613, 2011.

V. Jurgovsky, M. Granitzer, S. Ziegler, S. Calabretto, P. Portier, L. He-Guelton, and O. Caelen, "Sequence Classification for Credit Card Fraud Detection," Expert Systems with Applications, vol. 100, pp. 234–245, 2018.

A.Ng and M. Jordan, "On Discriminative vs. Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes," Advances in Neural Information Processing Systems, vol. 14, pp. 841–848, 2002.

L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.

J. H. Friedman, "Greedy Function Approximation: A Gradient Boosting Machine," Annals of Statistics, vol. 29, no. 5, pp. 1189–1232, 2001.

T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Francisco, CA, USA, pp. 785–794, 2016.

R. J. Bolton and D. J. Hand, "Statistical Fraud Detection: A Review," Statistical Science, vol. 17, no. 3, pp. 235–255, 2002.

S. Phua, V. Lee, K. Smith, and R. Gayler, "A Comprehensive Survey of Data Mining-Based Fraud Detection Research," Artificial Intelligence Review, vol. 34, no. 1, pp. 1–14, 2010.

O. Abdallah, M. Maarof, and A. Zainal, "Fraud Detection System: A Survey," Journal of Network and Computer Applications, vol. 68, pp. 90–113, 2016.

Singh, J. (2021). The Rise of Synthetic Data: Enhancing AI and Machine Learning Model Training to Address Data Scarcity and Mitigate Privacy Risks. Journal of Artificial Intelligence Research and Applications, 1(2), 292-332.

Narne, S., Adedoja, T., Mohan, M., & Ayyalasomayajula, T. (2024). AI-Driven Decision Support Systems in Management: Enhancing Strategic Planning and Execution. International Journal on Recent and Innovation Trends in Computing and Communication, 12(1), 268-276.

Boddapati, V. N., Galla, E. P., Sunkara, J. R., Bauskar, S., Patra, G. K., Kuraku, C., & Madhavaram, C. R. (2021). Harnessing the Power of Big Data: The Evolution of AI and Machine Learning in Modern Times. ESP Journal of Engineering & Technology Advancements, 1(2), 134-146.

Galla, E. P., Boddapati, V. N., Patra, G. K., Madhavaram, C. R., & Sunkara, J. (2023). AI Powered Insights: Leveraging Machine Learning And Big Data For Advanced Genomic Research In Healthcare. Educational Administration: Theory and Practice.

Anjum, K. N., & Luz, A. Investigating the Role of Internet of Things (IoT) Sensors in Enhancing Construction Site Safety and Efficiency.

Patra, G. K., Rajaram, S. K., & Boddapati, V. N. (2019). Ai And Big Data In Digital Payments: A Comprehensive Model For Secure Biometric Authentication. Educational Administration: Theory and Practice.

Chintala, S. (2019). IoT and Cloud Computing: Enhancing Connectivity. International Journal of New Media Studies (IJNMS), 6(1), 18-25.

Chintala, S. (2020). The Role of AI in Predicting and Managing Chronic Diseases. International Journal of New Media Studies: International Peer Reviewed Scholarly Indexed Journal, 7, 16-22.