Machine Learning-Based Approaches for Credit Card Fraud Detection: A Comprehensive Reviewz

The objective of data analytics is to discover hidden patterns and use them to guide wise judgements in a range of circumstances. Theft of credit cards has significantly grown as a result of modern technologies and has become a popular target for scam artists. Publicly available databases on credit card fraud are very unbalanced. As more people conduct business online, Fraud involving credit cards has grown to be a serious problem for both consumers and financial establishments. standard rule-based fraud detection strategies have shown to be insufficient to combat fraudsters' ever-evolving tactics. Machine learning algorithms have thus developed into a powerful tool for real-time unsupervised learning, and anomaly detection., is then explored in detail in order to accurately identify fraudulent transactions. Furthermore, we explore the various data features utilized by machine learning algorithms, including transaction history, transaction amounts, merchant information, and geographical locations. “For people, companies, and financial institutions, A significant financial danger is credit card fraud. In order to detect theft, robust methods for machine learning must be developed. researchers can help minimize financial losses associated with fraudulent activities”. In this research we will be using weighted product method. Taken as Alternative parameters is “Fraud detection using Game theory for M1, Hybrid Approach For Fraud Detection Using Svm And Decision Tree for M2, Fraud Detection Using Som & Psofor M3, Dempster Shafer Theory Along With Bayesian Learning For Detecting Fraudfor M4, Cardwatchfor M5”. Taken as Evaluation parameters is “Sum of Squared Error, Mean Squared error, Root Mean Square error, Mean Absolute error, Root Mean Square Prediction Error, and Accuracy”. Model 1 outperformed the other 4 models when a machine learning algorithm was used to identify credit card frauds. With Weighted Product Method we are able to find the best way of detection of credit card frauds by machine learning algorithm which has been evaluated with various parameters and methodology

Info-Communicative and Protective Function of the State as Combating Fraud using Sberbank Bank Cards

Financial crimes are defined as unfair activities that have become widespread in banking structures. The activities of financial fraudsters often have negative consequences before public rules are created that prohibit them. Intensive transformation processes in financial markets, their automation and virtualisation, the spread of remote interaction between banks and their clients, the influence of unauthorised persons on the software and hardware systems of banks, an increase in the number of cases and trading volumes determine the relevance of clarifying the essence of this phenomenon and the peculiarities of its manifestation in banking structures. The novelty of the study is determined by the fact that financial violations can be represented both in the structure of the current activities of banks and the process of interaction with clients and in the structure of expanding the list of services provided. The leading method to study this problem is the method of analysis, which allows to identify and comprehensively consider ways to counter financial crimes in banks to improve the level of financial security. The authors show that structurally, one should take into account, first of all, countermeasures on the part of customers, which often serve as a source of obtaining personal data. In this case, the state function is considered only as a security function for the purpose of possible punishment for fraudulent actions. The practical significance of the study is determined by the possibilities of structural implementation of combating financial fraudulent actions in the context of the development of the information society