Fraud Detection in Credit Card Data Using Supervised Machine Learning Based Scheme

Abstract

In fraud detection, Decision Trees, Random Forest, and XG Boost are utilized for their effectiveness in classifying transactions. Decision Trees create a model that splits data based on featurevalues, forminganintuitivetreestructure thatleadstofinalclassifications.RandomForest improves uponthis byusing multiple DecisionTrees withrandomdata subsets, aggregating their predictions to enhance accuracy and reduce overfitting. XG Boost employs a gradient boosting approach, building trees sequentially and optimizing performance through techniques like regularizationandparallelprocessing. Together,thesealgorithmsformarobustsystemcapableof adapting to complex transaction patterns while minimizing false positives and negatives. The algorithmcreates manydecision trees during training, each trained with a specific random noise. The algorithm then uses the results from all the trees to make a prediction

Introduction

The project focuses on detecting credit card fraud using machine learning to protect financial transactions from fraudulent activities, thereby minimizing financial losses and enhancing trust. The goal is to develop an accurate, efficient model that handles large-scale data and subtle fraud patterns in real-time.

The system analyzes transaction patterns, user behavior, and historical data using algorithms like Decision Tree, Random Forest, and XGBoost. It aims to improve detection accuracy, enable real-time monitoring, adapt to new fraud patterns, reduce costs, and improve customer experience.

The literature review highlights previous studies on neural networks, data mining, anomaly detection, and comparative analyses of various machine learning techniques applied to fraud detection.

The proposed system preprocesses data, trains models, and generates fraud predictions based on user input. Decision Tree provides simple, interpretable classification; Random Forest combines multiple decision trees to reduce overfitting and increase accuracy; and XGBoost offers efficient, scalable gradient boosting with regularization to improve model performance.

Future enhancements include adopting deep learning methods (like RNNs and transformers), real-time anomaly detection, blockchain for secure verification, explainable AI for transparency, and federated learning for collaborative model training without sharing sensitive data.

The final implementation will predict fraud using Decision Tree, Random Forest, and XGBoost algorithms.

Conclusion

The application of machine learning in credit card fraud detection has proven to be an indispensable tool in safeguarding financial transactions. Through the utilization of sophisticated algorithms, the system can efficiently identify fraudulent activities, thereby minimizing the risks associated with unauthorized transactions. The implementation of such technologynotonlyenhancesthesecurityoffinancialinstitutionsandtheircustomersbut also contributes to the overall stabilityand trust within the financialecosystem. As advancements continuetorefinethesedetectionsystems,thefutureholdspromisingprospectsforevenmore robust and reliable fraud prevention measures, ensuring a safer and more secure financial landscape for all stakeholders.

References

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Copyright

Copyright © 2025 T Poornima, T Praveen Kumar, K Munibala Subramanyam, Sannapureddy Prathap, Dasari Puneeth Kumar, V. Gopi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.