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Credit Card Fraud Detection using Supervised Learning with Feature Engineering

K. T. Krishna Kumar, D. Satvika Reddy, K. Vasantha, P. Ramamohan, D. Veerendra Santosh Sai
Associate Professor & Training and Placement officer, Computer Science And Engineering, Sanketika Vidya
Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India
B.Tech Final Semester, Bachelor of Technology, , Computer Science And Engineering, Sanketika Vidya
Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India

Abstract - Credit card fraud causes significant financial losses for customers and financial institutions worldwide. Detecting fraudulent transactions accurately and at the earliest stage is therefore essential. This work presents a fraud detection framework based on supervised learning combined with effective feature engineering techniques.Historical transaction data containing both genuine and fraudulent operations are used to train predictive models. The objective is to classify new transactions with high reliability while minimizing false alarms. Credit card fraud detection is naturally formulated as a binary classification problem. The dataset is highly imbalanced, with fraudulent cases representing only a small fraction of the total records. To address this challenge, careful preprocessing and transformation of the data are performed. Feature engineering is applied to derive informative attributes that enhance the learning capability of the models. The proposed system evaluates multiple machine learning approaches within a unified pipeline. Model performance is assessed using standard evaluation metrics suited for imbalanced data. The framework aims to improve fraud capture rate without increasing unnecessary
investigations. Experimental analysis demonstrates that engineered features significantly influence predictive accuracy. The study highlights the importance of combining domain knowledge with data-driven methods. The resulting solution provides a practical and scalable approach for real-world credit card fraud detection.
Key Words: Credit Card Fraud Detection, Supervised Learning, Feature Engineering, Imbalanced Data, data preprocessing, Synthetic Minority Over-sampling Technique, Fraud Analytics, Model Evaluation