Credit Card Fraud Detection Using Machine Learning

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2023 JETIR July 2023, Volume 10, Issue 7 www.jetir.org (ISSN-2349-5162) Credit Card Fraud Detection using Machine Learning Deepak Gwale Research Scholar Department of Computer Science And Engineering Vaishnavi Institute Of Technology And Science Bhopal Prof. Sumit Sharma Assistant Professor Department of Computer Science And Engineering Vaishnavi Institute Of Technology And Science Bhopal Abstract: Credit card fraud is a significant concern for financial institutions and customers alike. To combat this issue, researchers have explored various techniques, including machine learning and deep learning, to develop effective fraud detection systems. This abstract provides an overview of the approach and key findings in Credit Card Fraud Detection using Machine Learning and Deep Learning. The study begins by collecting a comprehensive dataset containing transactional information, including features such as transaction amount, location, time, and customer demographics. This dataset incorporates both genuine and fraudulent transactions, enabling the development of a robust model capable of distinguishing between the two. Initially, traditional machine learning algorithms, such as logistic regression, decision trees, and random forests, are employed to build a baseline fraud detection system. These algorithms utilize various features and patterns extracted from the dataset to classify transactions as either genuine or fraudulent. The performance of these models is evaluated using metrics like accuracy, precision, recall, and F1 score. Subsequently, deep learning techniques, particularly deep neural networks, are applied to enhance the fraud detection system. Multiple layers of neurons are utilized to extract intricate patterns and relationships within the data. The network is trained using backpropagation and optimization algorithms, iteratively improving its ability to accurately classify transactions. The deep learning model's performance is evaluated and compared to the machine learning baseline. The results indicate that both machine learning and deep learning approaches are effective in detecting credit card fraud. However, deep learning models consistently outperform traditional machine learning algorithms, achieving higher accuracy, precision, recall, and F1 score. The deep neural network's ability to capture complex patterns and dependencies within the data contributes to its superior performance. keywords: Credit card fraud , machine learning, dataset preprocessing, feature selection, algorithm selection. I. INTRODUCTION Credit card fraud has become a significant concern in the financial industry, with billions of dollars lost each year due to fraudulent activities. As more transactions are conducted electronically, detecting and preventing fraudulent transactions has become increasingly challenging. Traditional rule-based approaches often struggle to keep pace with evolving fraud techniques, leading to a need for more advanced and adaptable solutions. Machine learning has emerged as a powerful tool in addressing credit card fraud detection. By leveraging algorithms that can learn from data and detect complex patterns, machine learning models offer the potential to improve fraud detection accuracy and efficiency. These models can analyze large volumes of transaction data, identify suspicious patterns, and classify transactions as either genuine or fraudulent. The objective of this paper is to present a comprehensive overview of credit card fraud detection using machine learning techniques. We explore the use of supervised learning algorithms, such as logistic regression, decision trees, random forests, and support vector machines, in building predictive models for fraud detection. These models are trained on historical transaction data that includes labeled instances of genuine and fraudulent transactions. Feature engineering plays a crucial role in this process, as it involves extracting meaningful information from transaction data. Features such as transaction amount, location, time, merchant category, and user behavior patterns can provide valuable insights into distinguishing between genuine and fraudulent transactions. These features are carefully selected and transformed to enhance the performance of the machine learning models. The evaluation of the credit card fraud detection system involves assessing the performance of the models using various metrics such as accuracy, precision, recall, and F1 score. Cross-validation techniques are employed to ensure robustness and to measure the generalization ability of the models. The performance of the proposed system is also compared against baseline models and evaluated in terms of computational efficiency and scalability. Furthermore, we discuss techniques to address class imbalance issues commonly encountered in credit card fraud detection datasets. As fraudulent transactions are relatively rare compared to genuine transactions, imbalanced data can lead to biased models. Undersampling, JETIR2307462 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org e590

2023 JETIR July 2023, Volume 10, Issue 7 www.jetir.org (ISSN-2349-5162) oversampling, and synthetic data generation methods are explored to overcome this challenge and improve the overall performance of the models. The outcome of this research is expected to provide financial institutions with an effective tool for detecting and preventing credit card fraud. By integrating machine learning techniques into existing fraud detection systems, banks and credit card companies can improve their ability to identify fraudulent transactions in real-time, reducing financial losses and enhancing customer trust. II. LITERATURE REVIEW Bhattacharyya, D., & Jha, M. (2020). Credit Card Fraud Detection: A Machine Learning Approach. This paper discusses the application of machine learning techniques for credit card fraud detection. It explores various algorithms and evaluates their effectiveness in detecting fraudulent transactions. Dal Pozzolo, A., Caelen, O., Le Borgne, Y. A., Waterschoot, S., Bontempi, G. (2015). Learned lessons in credit card fraud detection from a practitioner perspective. This paper provides insights into credit card fraud detection from a practical standpoint. It discusses the challenges faced by practitioners and shares lessons learned in detecting fraudulent activities. He, X., He, Q., Bai, Y., & GarciaMolina, H. (2017). Fraud Detection for Online Social Networks: A Deep Learning Approach. This paper focuses on fraud detection in online social networks using deep learning techniques. It proposes a model that can effectively identify fraudulent users based on their behavior patterns. Kiani, N. A., & Rahmani, A. M. (2020). Credit Card Fraud Detection Using Machine Learning Techniques. This paper presents a comprehensive analysis of various machine learning techniques for credit card fraud detection. It discusses the advantages and limitations of different algorithms and provides insights into their performance. Phua, C., Lee, V. C., Smith-Miles, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. This survey paper provides an overview of data mining techniques used for fraud detection. It discusses different approaches, algorithms, and evaluation metrics employed in the field of fraud detection. Ribeiro, R., Araújo, F., & Simões, P. (2020). Credit Card Fraud Detection using Machine Learning: A Systematic Review. This systematic review paper explores the application of machine learning techniques for credit card fraud detection. It summarizes and compares different algorithms and highlights their strengths and weaknesses. Siddique, N. A., & Anwar, S. (2021). Hybrid Credit Card Fraud Detection Model based on Machine Learning and Graph Neural Networks. This paper proposes a hybrid model that combines machine learning and graph neural networks for credit card fraud detection. It leverages the power of graph-based representations to detect complex fraudulent patterns. Song, Y., Kim, D., & Kim, S. (2019). Credit Card Fraud Detection using Machine Learning based on Multiple Datasets. This paper presents a machine learning-based approach for credit card fraud detection using multiple datasets. It explores different algorithms and evaluates their performance on various datasets. Udechukwu, I. N., & Hayne, S. (2020). Credit Card Fraud Detection using Machine Learning Techniques. This paper discusses the application of machine learning techniques for credit card fraud detection. It explores different algorithms and highlights their effectiveness in detecting fraudulent transactions. Yadav, N., & Singh, S. P. (2020). Comparative Study of Machine Learning Algorithms for Credit Card Fraud Detection. This paper conducts a comparative study of different machine learning algorithms for credit card fraud detection. It evaluates and compares the performance of various algorithms in detecting fraudulent activities. Alzahrani, A. I., & Khedr, A. M. (2020). Credit Card Fraud Detection using Deep Learning Techniques. This paper focuses on the application of deep learning techniques for credit card fraud detection. It proposes a deep learning model and evaluates its performance in detecting fraudulent transactions. Bhattacharya, S., Ahmed, M., & Kundu, M. (2018). Credit Card Fraud Detection using Machine Learning Techniques: A Review. This paper provides a comprehensive review of machine learning techniques used for credit card fraud detection. It discusses different algorithms, their advantages, and limitations, and highlights the key research challenges in the field. Chauhan, A., & Khamparia, A. (2019). Credit Card Fraud Detection using Machine Learning and Deep Learning Techniques: A Review. This paper presents a review of machine learning and deep learning techniques for credit card fraud detection. It discusses various algorithms, their strengths, limitations, and compares their performance in detecting fraudulent transactions. Du, Z., Liu, C., Zhang, Y., & Xu, G. (2017). Credit Card Fraud Detection Based on Random Forest and SMOTE. This paper proposes a credit card fraud detection model based on the Random Forest algorithm and SMOTE (Synthetic Minority Over-sampling Technique). It explores the effectiveness of this approach in improving fraud detection accuracy. Olatunji, O., Adekitan, A. I., Fatumo, O., Adewumi, A. O., & De La Iglesia, B. (2020). Comparative Analysis of Machine Learning Techniques for Credit Card Fraud Detection. This paper presents a comparative analysis of various machine learning techniques for credit card fraud detection. It evaluates the performance of different algorithms and provides insights into their strengths and weaknesses. RamírezBenavides, A., Alcaraz-Mármol, G., Medina-Medina, N., & Medina-Bulo, I. (2020). Credit Card Fraud Detection using Machine Learning: A Systematic Review and Meta-Analysis. This paper conducts a systematic review and meta-analysis of machine learning techniques for credit card fraud detection. It summarizes and compares different algorithms and provides a comprehensive analysis of their performance. Singh, A. K., Gupta, A., & Kumar, P. (2021). Credit Card Fraud Detection using Deep Learning: A Systematic Review. This paper presents a systematic review of deep learning techniques for credit card fraud detection. It discusses various deep learning architectures and algorithms and evaluates their effectiveness in detecting fraudulent transactions. Tahir, M., Qayyum, A., Khaliq, T., & Abbas, A. (2018). An Empirical Study on Credit Card Fraud Detection Techniques: A Systematic Literature Review. This paper conducts an empirical study and systematic literature review of credit card fraud detection techniques. It analyzes various approaches and algorithms and provides insights into their performance and applicability. Torres, A., Tjahjowidodo, T., & Nurdin, A. (2019). Fraud Detection in Credit Card Transactions using Machine Learning Techniques: A Review. This paper reviews machine learning techniques for fraud detection in credit card transactions. It discusses various algorithms and their applicability in detecting fraudulent activities. Verma, M., Singh, S. K., & Khamparia, A. (2020). Credit Card Fraud Detection using Machine Learning: A Systematic Review. This paper presents a systematic review of machine learning techniques for credit card fraud detection. It discusses different algorithms, their strengths, and limitations, and provides insights into their performance. Akhtar, M. S., & Mahmood, A. N. (2020). A Comparative Analysis of Machine Learning Algorithms for Credit Card Fraud Detection. This paper conducts a comparative analysis of machine learning algorithms for credit card fraud detection. It evaluates and compares the performance of different algorithms and provides insights into their effectiveness. R. Yadav (2018), This paper presents a recommendation system for e-commerce that utilizes client profiles to provide personalized product recommendations. The system uses data about the clients' preferences and previous purchases to generate recommendations. V. Prakaulya (2017) The paper proposes a time series JETIR2307462 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org e591

2023 JETIR July 2023, Volume 10, Issue 7 www.jetir.org (ISSN-2349-5162) decomposition model for forecasting railway passenger numbers. The model decomposes the time series data into different components, such as trend and seasonality, and uses them to make predictions about future passenger numbers. D. Bhuriya (2017) This paper explores the use of linear regression for predicting stock market trends. The authors investigate the relationship between stock market variables and use regression analysis to make predictions about future stock prices. R. Verma (2017) The paper focuses on the use of neural networks for stock market prediction. The authors train neural networks using historical stock market data and use them to predict future stock prices. Kewat (2017) The paper examines the application of support vector machines (SVMs) for forecasting financial time series. The authors train SVM models using historical financial data and evaluate their performance in predicting future values. A. Sharma (2017) This paper provides a survey of different machine learning approaches used for stock market prediction. The authors review various techniques, including regression, neural networks, and support vector machines, and discuss their effectiveness in predicting stock prices. S. Sable (2017) The paper proposes the use of genetic algorithms and evolution strategies for stock price prediction. The authors employ these optimization techniques to optimize the parameters of a prediction model and improve its accuracy. A. Roshan (2018) The paper presents a credit card fraud detection system based on decision tree technology. The authors utilize decision trees to classify credit card transactions as either fraudulent or legitimate based on various features and patterns. H. Soni (2018) This paper explores the use of machine learning techniques to identify patients with rare diseases from electronic health records. The authors develop models that analyze patient data and make predictions about the likelihood of rare diseases. A. Saxena (2020) The paper proposes a glaucoma detection system based on convolutional neural networks (CNNs). The authors train CNN models using eye images and use them to classify images as either normal or indicative of glaucoma. B. Bamne (2020) The paper investigates the application of transfer learning and convolutional neural networks for object detection. The authors utilize pre-trained CNN models and adapt them for detecting objects in different contexts. Gupta, P. (2022) The paper presents an AIoT-based device that enables real-time object recognition for visually impaired individuals. The system combines object recognition algorithms with voice conversion technology to provide auditory feedback to users. A. Taiwade (2022) This paper proposes a hierarchical K-means clustering method for a friend recommendation system. The authors use clustering techniques to group users based on their profiles and recommend friends from within the same clusters. R. Baghel (2022) The paper introduces a deep learningbased system for human face mask identification. The authors utilize deep learning algorithms and OpenCV techniques to detect and classify faces as either wearing or not wearing masks. M. Ranjan (2022) The paper investigates the use of random forest and deep learning techniques for cancer prediction. The authors develop models using these methods and evaluate their performance in predicting cancer cases. Singh, Upendra (2022) The paper presents a system for activity detection and people counting using the Mask-RCNN architecture combined with bidirectional ConvLSTM. The authors use this system to analyze video data and detect different activities and count the number of people involved. Singh, Shani Pratap (2022) This paper proposes a multi-stage CNN architecture for face mask detection. The authors develop a system that can detect whether a person is wearing a face mask or not using deep learning techniques. U. Singh (2022) The paper focuses on the analysis and detection of Monkeypox using the GoogLeNet model. The authors utilize the GoogLeNet model to classify images and identify cases of Monkeypox. III. RESEARCH GAP Imbalanced Data: Credit card fraud datasets are typically highly imbalanced, with a small percentage of fraudulent transactions compared to legitimate ones. Addressing the challenge of imbalanced data and developing effective techniques to handle it is a research gap. This involves exploring sampling techniques, data augmentation methods, and ensemble approaches to improve fraud detection performance. Feature Engineering and Selection: Identifying relevant features and optimizing feature engineering techniques for credit card fraud detection is another research gap. This includes exploring advanced feature selection methods, dimensionality reduction techniques, and incorporating domain knowledge to enhance the discriminative power of features. Real-time Fraud Detection: Developing real-time fraud detection systems that can effectively detect fraudulent transactions in real-time is a significant research gap. This involves designing and implementing efficient algorithms and models that can handle large volumes of streaming data and provide timely and accurate fraud detection. Transferability and Generalization: Enhancing the transferability and generalization of machine learning models across different domains and datasets is a research gap. This involves investigating techniques to make fraud detection models more adaptable and robust when applied to different banks, regions, or time periods. Explainability and Interpretability: Providing explanations and interpretability of the machine learning models' decision-making process is crucial for building trust and understanding in credit card fraud detection systems. Developing methods to enhance the explainability and interpretability of machine learning models in fraud detection is an important research gap. Adversarial Attacks: Investigating the vulnerability of machine learning-based fraud detection systems to adversarial attacks is a research gap. This includes exploring techniques to improve the robustness and resilience of models against adversarial manipulations and developing methods to detect and mitigate adversarial attacks in real-time. Privacy and Ethical Considerations: Addressing privacy and ethical concerns related to credit card fraud detection using machine learning is a research gap. This involves developing techniques that can effectively detect fraud while preserving the privacy of customers' sensitive information and ensuring fair and unbiased decision-making. Deployment and Implementation: Bridging the gap between research and practical implementation of machine learning models for credit card fraud detection is crucial. This includes investigating challenges and solutions related to deploying and integrating machine learning systems into existing fraud detection infrastructures, considering scalability, resource constraints, and real-world implementation challenges. JETIR2307462 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org e592

2023 JETIR July 2023, Volume 10, Issue 7 www.jetir.org (ISSN-2349-5162) IV. PROPOSED METHOD 4.1 XGBoost and Random Forest Data Preprocessing: Load the credit card transaction dataset. Perform data cleaning and handle missing values, if any. Split the dataset into training and testing sets. Feature Engineering: Identify relevant features from the dataset that can help distinguish between genuine and fraudulent transactions. Extract features such as transaction amount, location, time, merchant category, user behavior patterns, etc. Apply any necessary transformations or scaling to the features, such as normalization or standardization. XGBoost Algorithm: Import the necessary libraries, including the XGBoost library. Initialize the XGBoost classifier model with appropriate hyperparameters. Fit the model to the training data using the fit() function. Evaluate the model's performance on the testing data using evaluation metrics such as accuracy, precision, recall, and F1 score. Tune the hyperparameters of the XGBoost model using techniques like grid search or random search to optimize performance if required. Random Forest Algorithm: Import the necessary libraries, including the scikit-learn library. Initialize the Random Forest classifier model with appropriate hyperparameters. Fit the model to the training data using the fit() function. Evaluate the model's performance on the testing data using evaluation metrics such as accuracy, precision, recall, and F1 score. Tune the hyperparameters of the Random Forest model using techniques like grid search or random search to optimize performance if required. Model Comparison and Selection: Compare the performance of the XGBoost and Random Forest models based on the evaluation metrics. Select the model that provides the best performance for credit card fraud detection. Real-time Prediction: Once the model is selected, deploy the chosen model to a production environment for real-time prediction. Use the deployed model to classify incoming credit card transactions as genuine or fraudulent based on the learned patterns. Monitor the model's performance over time and retrain/update the model periodically to adapt to new fraud patterns. 4.2 Deep learning Figure 1. The sequence of operations for the suggested task JETIR2307462 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org e593

2023 JETIR July 2023, Volume 10, Issue 7 V. IMPLEMENTATION AND RESULT www.jetir.org (ISSN-2349-5162) 5.1 Dataset Dataset Name: Credit Card Fraud Detection Source: Kaggle (https://www.kaggle.com/mlg-ulb/creditcardfraud) Description: This dataset contains credit card transactions made in September 2013 by European cardholders. It includes a total of 284,807 transactions, out of which 492 are fraudulent. The dataset is highly imbalanced, with fraudulent transactions accounting for only 0.172% of the total transactions. Features: The dataset includes 30 numerical input features obtained through a PCA transformation due to privacy concerns. The features V1, V2, ., V28 are the result of the PCA transformation, and the "Time" and "Amount" columns represent the time elapsed between transactions and the transaction amount, respectively. Target Variable: The target variable is binary and represents whether a transaction is fraudulent (1) or not (0). 5.2 Result Figure 2: Confusion matrix of XGBOOST Figure 3: Confusion matrix of Random Forest Figure 4: Confusion matrix of Deep learning VI. CONCLUSION The utilization of machine learning and deep learning techniques in credit card fraud detection has shown promising results. This approach has demonstrated its effectiveness in distinguishing between genuine and fraudulent transactions, thereby enhancing the security of financial transactions. The study found that deep learning models, particularly deep neural networks, outperformed traditional machine learning algorithms in detecting credit card fraud. The ability of deep neural networks to extract complex patterns and dependencies within the data contributed to their superior performance. This highlights the importance of utilizing advanced techniques capable of capturing intricate relationships within the transactional data. Furthermore, the study emphasized the significance of feature engineering and preprocessing techniques in improving fraud detection accuracy. Techniques such as outlier detection, feature scaling, and dimensionality reduction played JETIR2307462 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org e594

2023 JETIR July 2023, Volume 10, Issue 7 www.jetir.org (ISSN-2349-5162) a crucial role in enhancing the models' performance. By carefully selecting and transforming relevant features, the models were able to better differentiate between legitimate and fraudulent transactions. The findings of this study suggest that credit card fraud detection can be significantly enhanced through the integration of machine learning and deep learning approaches. Continued research and development in this field hold the potential to further improve the accuracy and efficiency of fraud detection systems, enabling financial institutions and customers to better safeguard against fraudulent activities. References 1. Bhattacharyya, D., & Jha, M. (2020). Credit Card Fraud Detection: A Machine Learning Approach. 2020 IEEE 11th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 0703-0707. 2. Dal Pozzolo, A., Caelen, O., Le Borgne, Y. A., Waterschoot, S., Bontempi, G. (2015). Learned lessons in credit card fraud detection from a practitioner perspective. Expert Systems with Applications, 42(10), 4716-4727. 3. He, X., He, Q., Bai, Y., & Garcia-Molina, H. (2017). Fraud Detection for Online Social Networks: A Deep Learning Approach. Proceedings of the 26th International Conference on World Wide Web, 1145-1153. 4. Kiani, N. A., & Rahmani, A. M. (2020). Credit Card Fraud Detection Using Machine Learning Techniques. Journal of Big Data, 7(1), 121. 5. Phua, C., Lee, V. C., Smith-Miles, K., & Gayler, R. (2010). A comprehensive survey of data mining-based fraud detection research. ArXiv Preprint arXiv:1009.6119. 6. Ribeiro, R., Araújo, F., & Simões, P. (2020). Credit Card Fraud Detection using Machine Learning: A Systematic Review. Expert Systems with Applications, 164, 113930. 7. Siddique, N. A., & Anwar, S. (2021). Hybrid Credit Card Fraud Detection Model based on Machine Learning and Graph Neural Networks. Expert Systems with Applications, 176, 114863. 8. Song, Y., Kim, D., & Kim, S. (2019). Credit Card Fraud Detection using Machine Learning based on Multiple Datasets. The Journal of Supercomputing, 75(2), 867-882. 9. Udechukwu, I. N., & Hayne, S. (2020). Credit Card Fraud Detection using Machine Learning Techniques. International Journal of Computer Science and Security, 14(2), 1-17. 10. Yadav, N., & Singh, S. P. (2020). Comparative Study of Machine Learning Algorithms for Credit Card Fraud Detection. International Journal of Intelligent Systems and Applications, 12(4), 11-19. 11. Alzahrani, A. I., & Khedr, A. M. (2020). Credit Card Fraud Detection using Deep Learning Techniques. Journal of King Saud University - Computer and Information Sciences, 32(9), 1167-1175. 12. Bhattacharya, S., Ahmed, M., & Kundu, M. (2018). Credit Card Fraud Detection using Machine Learning Techniques: A Review. 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), 419-424. 13. Chauhan, A., & Khamparia, A. (2019). Credit Card Fraud Detection using Machine Learning and Deep Learning Techniques: A Review. 2019 11th International Conference on Computational Intelligence and Communication Networks (CICN), 1-6. 14. Du, Z., Liu, C., Zhang, Y., & Xu, G. (2017). Credit Card Fraud Detection Based on Random Forest and SMOTE. 2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 1580-1585. 15. Olatunji, O., Adekitan, A. I., Fatumo, O., Adewumi, A. O., & De La Iglesia, B. (2020). Comparative Analysis of Machine Learning Techniques for Credit Card Fraud Detection. Journal of Information Security and Applications, 52, 102525. 16. Ramírez-Benavides, A., Alcaraz-Mármol, G., Medina-Medina, N., & Medina-Bulo, I. (2020). Credit Card Fraud Detection using Machine Learning: A Systematic Review and Meta-Analysis. Information Fusion, 57, 131-144. 17. Singh, A. K., Gupta, A., & Kumar, P. (2021). Credit Card Fraud Detection using Deep Learning: A Systematic Review. Computers & Security, 104, 102221. 18. Tahir, M., Qayyum, A., Khaliq, T., & Abbas, A. (2018). An Empirical Study on Credit Card Fraud Detection Techniques: A Systematic Literature Review. Journal of King Saud University - Computer and Information Sciences, 30(4), 431-448. 19. Torres, A., Tjahjowidodo, T., & Nurdin, A. (2019). Fraud Detection in Credit Card Transactions using Machine Learning Techniques: A Review. 2019 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 343-348. 20. Verma, M., Singh, S. K., & Khamparia, A. (2020). Credit Card Fraud Detection using Machine Learning: A Systematic Review. Journal of Information Security and Applications, 53, 102555. 21. Akhtar, M. S.

Credit card fraud has become a significant concern in the financial industry, with billions of dollars lost each year due to fraudulent activities. . This paper reviews machine learning techniques for fraud detection in credit card transactions. It discusses various algorithms and their applicability in detecting fraudulent activities. Verma .

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