Explainable AI and Big Data Analytics for Data Security Risk and Privacy Issues in the Financial Industry

This paper is a comprehensive review of the current literature on the importance of data security in the financial industry when working with Explainable Artificial Intelligence (XAI) and Big Data Analytics. Collaborative initiatives in the financial industry have significantly propelled the rapid evolution of e-Commerce with the help of data and have spurred the implementation of advanced technology such as data analysis and explainable AI (XAI) that can provide human-understandable explanations on the reason for providing a good level of transparency and traceability for decisions made [1]. This growth raises concerns regarding secure data processing and preserving privacy within the database used for data mining and cost-effective big data analytics including structured, unstructured, and semi-structured data crucial for decision making along with maintaining the integrity and accuracy of stored data. The reliance on explainable artificial intelligence (XAI) for data analytics has raised critical concerns, highlighting the need for comprehensive strategies to state the importance of maintaining data privacy and security. Ensuring the protection of sensitive information while harnessing the power of analytics for decision making becomes a critical challenge that demands immediate attention. Explainable artificial intelligence (XAI) is the most important technology used in the financial sector for decision-making on applications that require explaining the reasons that made the algorithm model come with the outcome, as customers have the right to know on what basis the result of their application was evaluated. Although much research has been conducted on the technological function of XAI research on how to process these data securely and safely remains very limited [2]. To address these necessities, this research aims to provide insights into the best ways to conduct data analytics in a privacy-conscious world and to ensure that data analytics can be conducted with the help of XAI, which will help maximize the efficiency of automated data processing and provides the result with an explanation for the reason for the result. This research paper will contribute to the existing literature on data analytics with AI and provides analysis for the financial industry to improve its data analytics capabilities, providing insights and guidance in optimizing big data analytics within the financial sector addressing cost-effectiveness, privacy concerns, and efficiency in data model selection for enhanced decision-making and operational performance. The unique insights or advancements the paper offers to the existing body of knowledge are a thorough analysis of current explainable AI techniques and their applications in addressing data security, risk, and privacy challenges specific to the financial sector, a new framework that integrates explainable AI with big data analytics to enhance data security and privacy in financial institutions, introduction of an innovative risk assessment model that leverages explainable AI to identify and mitigate potential security threats in financial data systems, a new innovative risk assessment model that leverages explainable AI to identify and mitigate potential security threats in financial data systems, presenting a new novel privacy-preserving techniques that utilize explainable AI to protect sensitive financial information while maintaining data utility, identification of key areas for future research, highlighting potential advancements in explainable AI that could further improve data security and privacy in the financial industry for future research directions.