Logistic regression on banking fraud

Bank fraud has been an increasing concern for banks as fraudsters use more advanced methods to take advantage of weaknesses in online transactions. Banks use machine learning algorithms to detect fraud, and logistic regression has been among the most popular methods used to detect fraud. This paper examines the use of logistic regression for the detection of banking fraud and its benefits, use, and limitations.
The article begins with a background of banking fraud, listing common types such as credit card fraud, identity fraud, loan fraud, and insider fraud. It then goes on to logistic regression, explaining why it is suitable for fraud detection and how it compares to other classification models. Data gathering, data preprocessing, and principal features that affect fraud classification are treated in the article.
Moreover, the paper discusses logistic regression model building and assessment using performance metrics such as accuracy, precision, recall, and F1-score. Some of the issues such as imbalanced data, false positives, and privacy concerns are taken into consideration, and ethical and legal concerns informing fraud detection systems are discussed. How banks optimize fraud detection by integrating logistic regression with cutting-edge methods such as deep learning and blockchain technology is also explored in the paper.
Finally, the paper discusses the future of banking fraud detection with an emphasis on AI innovation and emerging technologies that will shape the future of financial security. Through the adoption of machine learning and new fraud prevention strategies, financial institutions can mitigate fraud risks while providing a secure and seamless banking experience.