Introduction: This paper highlights the outcome of the comparative study of “Various Machine learning algorithms namely K-NN, Naive Bayes, and Logistic Regression for Credit Card Fraud Detection” carried out based on a dataset taken from UCI.com in 2022-23 at Manav Rachna International Institute of Research and Studies. Problem: Credit card fraud is still rife today and the modes are increasingly varied. Quite often we hear of fraud cases that cause irreplaceable injury to banks and financial institutions which cannot be compensated in terms of costs. To avoid scams with various modes of credit cards, we must be able to identify and find out the modes often used by fraudsters. This scheme liberates such financial institutions and banks with complete and appropriate information using Machine Learning Techniques, not only about the modes that scammers or fraudsters often use but also ways to protect against such frauds. Objective: The present paper discusses the various machine learning models based on classification and regression, namely K-Nearest Neighbors, Naïve Bayes, and Logistic Regression, which are successfully able to achieve the classification accuracy of 80% using Logistic Regression with a Precision of 78%, Recall of 100%, and F1-Score of 88% for fraudulent credit card transactions. Methodology: The comparative analysis demonstrates that for Precision, Recall, and Accuracy parameters, the K-Nearest Neighbor is a better approach for detecting fraudulent transactions than the Logistic Regression and Naïve Bayes. Results: The accuracy is marginal high in Logistic Regression but the False Positive parameters are not able to identify the imbalanced data; therefore, they disguise the results and accuracy of Logistic Regression and K-Nearest Neighbor deems fit for such cases. Conclusion: This scheme depicts the automated fraud classification systems using machine learning techniques, namely K-Nearest Neighbor, Logistic Regression, and Naive Bayes, to produce a model that can distinguish valid and invalid credit card transactions. Originality: Through this research, the most relevant features are used to go through the visualization of accuracy with the confusion matrix, and accuracy calculations are obtained from the dataset used. Limitations: Deep learning techniques could have been used to fetch even better results.

AI-powered analytics and conversational systems designed for data mining and other manipulative tasks raise concerns about who can access data and how to utilize it. This data progression has brought forth significant ethical considerations concerning privacy and ethics. AI-powered conversational agents, such as chatbots and virtual assistants, collect and process vast amounts of personal data to enhance user experience and provide tailored responses. Many industries have been transformed by artificial intelligence (AI)-based analytics systems, and some have unthinkable analytical data in terms of better decision-making, highly personalized customer experiences, and improved operational efficiency. While conversational AI offers convenience for users, this technology is also associated with privacy and data protection threats. This research seeks to understand the ethical issues with AI-driven analytics, focusing on data privacy and ethics in verbal and written interactions. This work will look at the current state and potential threats in depth and demonstrate the implementation of differential privacy using the Laplace mechanism in the query output so that the document has no special meaning and does not distort the released results significantly.

Artificial intelligence, blockchain, cloud computing, and Internet of Things (IoT) are indispensable technologies navigating the way for digitized era. The conflux of all these technologies can bring out phenomenal modifications in the world of business and industry. Artificial intelligence with machine learning can train data and these data can be stored safely on cloud and used by blockchain for financial and other purposeful transactions which in turn gives leverage to IoT. It can be further proclaimed that the amalgamation of all these technologies will also head way toward business and industrial digitization too. This chapter discusses all these concepts in detail to give an insight of how business and corporations can take advantage of this conflux. <!----> <!----> <!----> <!---->