Autoencoder-Driven insights into Credit Card Fraud: A Comprehensive Analysis
Keywords:
Credit Card Fraud Detection(CCFD), autoencoder, Machine Learning(ML), Deep learning(DL), Random forest, Logistic regressionAbstract
Exponential growth of online transactions has posed a serious threat to security of individuals, institutions and for the broader economy. Credit card fraud remains a pervasive and costly issue in the financial industry, necessitating the development and implementation of effective fraud detection algorithms. This research paper provides a comparative analysis of three distinct algorithms, namely Random Forest, Autoencoder, and Logistic Regression, to evaluate their performance in identifying fraudulent transactions in credit card data. The study delves into the specifics of the data preprocessing and feature engineering steps crucial for preparing credit card transaction data, thus highlighting the significance of data quality in algorithm performance. Subsequently, the research paper scrutinizes the three selected algorithms. Random Forest, a powerful ensemble method, is known for its ability to handle complex, high-dimensional data. Autoencoder, a type of neural network, is explored for its ability to capture intricate patterns and anomalies in transaction data. Logistic Regression, a well-established linear classifier, is included for its simplicity and interpretability.
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References
Emmanuel Ileberi, Yanxia Sun ,Zenghui Wang, “A machine learning based credit card fraud detection using the GA algorithm for feature selection” 2022
Maniraj SP, Saini A, Ahmed S, Sarkar D, “Credit card fraud detection using machine learning and data science”. Int J Eng Res 2019;8(09).
Dornadula VN, Geetha S., “Credit card fraud detection using machine learning algorithms”. Procedia Computer Science 2019
B. Wickramanayake, D. K. Geeganage, C. Ouyang, and Y.Xu, “A survey of online card payment fraud detection using data mining-based methods” , arXiv, [2020].
Rejwan Bin Suleiman, Vitaly Schetinin and Paul Sant, “Review of Machine Learning Approach on Credit Card Fraud Detection
Sumit Misra, Soumyadeep Thakur, Manosij Ghosh, Sanjoy Kumar Saha, “An Autoencoder Based model for detecting fraudulent credit card transaction”
Olawale Adepoju; Julius Wosowei; Shiwani lawte; Hemaint Jaiman, “Comparative Evaluation of Credit Card Fraud Detection using Machine Learning Techniques
Amit Gupta, M.C. Lohani and Mahesh Manchanda, “Financial Fraud Detection using Naïve Bayes algorithm in highly imbalanced dataset”
Yakub K. Saheed, Moshood A. Hambali, Micheal O. Arowolo and Yinusa A. Olasupo, “GA Feature selection on Naïve Bayes, Random Forest and SVM for Credit card fraud detection”
N. Malini; M. Pushpa, “Analysis on Credit card fraud identification techniques based on KNN and outlier detection”
Fayaz Itoo, Meenakshi and Satwinder Singh, “Comparison and analysis of logistic regression, Naïve bayes and KNN machine learning algorithms for credit card fraud detection
Shiyang Xuan; Guanjun Liu; Zhenchuan Li; Lutao Zheng; Shuo Wang; Changjun Jiang, “Random Forest for credit card fraud detection”
Hordri, Yuhaniz, Azmi and Shamsuddin, “Handling Class Imbalance in Credit Card Fraud using Resampling Methods”
Fahimeh Ghobadi, Mohsen Rohani, “Cost sensitive modeling of credit card fraud using neural network strategy”
Bora Mehar Sri Satya Teja1, BoomireddyMunendra2, Mr.S. Gokulkrishnan.[2022] A Research Paper on Credit Card Fraud Detection
Shanshan Jaing, Ruiting Dong, Jie Wang and Min Xia, “Credit Card Fraud Detection Based in Unsupervised Attentional Anomaly Detection Network
Hosein Fanai, Hossein Abbasimehr, “A novel combined approach based on deep Autoencoder and deep classifiers for credit card fraud detection[2023]
Bassam Kasasbeh, Balqees Al-dabayah, Hadeel Ahmad, “Multilayer perceptron artificial neural networks-based model for credit card fraud detection”[2022]
Ebenezer Esenogho, Domor Mienye, Theo Swart and Kehinde Aruleba, “A neural network ensemble with feature engineering for improved credit card fraud detection” [2022]
Saleh Albali, Tahira Nazir, Awais Mehmood, Aun Irtaza, Ali Alkhalifah, Waleed Albattah , “AEI-DNET: A novel densenet model with an autoencoder for the stock market predictions using stock technical indicators[2022]
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