Fraud Detection in Financial Transactions Using Machine Learning Techniques

Fraud Detection in Financial Transactions Using Machine Learning Techniques

Sivasubramanyam Medasani

Prof. Dept. of Computer Science & Engineering,

KSSEM Bengaluru, India

sivasubramanyam.m@kssem.edu.in

Kushmitha T A

Dept. of Computer Science

& Engineering, KSSEM Bengaluru, India kushmitha8904@gmail.com

Kasturi Poornima

Dept. of Computer Science

& Engineering,

KSSEM Bengaluru, India kasturipoornima@gmail.com

Kusuma B

Dept of Computer Science & Engineering,

KSSEM Bengaluru, India kusumashiva523@gmail.com

Nikitha L

Dept. of Computer Science & Engineering,

KSSEM Bengaluru, India nikitha.lakshmana47@gmail.com

Abstract—Through integrating data warehousing, data visualization, information retrieval, and stream processing analytics, this project seeks to create a strong fraud detection framework for financial institutions. Significant volumes of transactional data are efficiently processed and stored, allowing for rapid retrieval and in-depth analysis. To detect trends and irregularities, advanced machine learning models such as Random Forest, Decision Tree, Logistic Regression & Naïve Bayes are used. To evaluate these models, we use precision, recall, F1-score, and AUC-ROC. To help ensure practical deployment in financial environments, the system places a strong emphasis on scalability and real-time processing. This improves fraud prevention and strengthens confidence in economic systems.

Keywords: K-Means, Financial Transactions, Random Forests, Decision Trees, Ensemble Learning, Logistic Regression, Fraud Detection, and Classification.