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Research Article Open access CC BY 4.0

Effect of Adopting AI to Explore Big Data on Personally Identifiable Information (PII) for Financial and Economic Data Transformation

Samuel Oladiipo Olabanji, Oluseun Babatunde Oladoyinbo, Christopher Uzoma Asonze, Tunbosun Oyewale Oladoyinbo, Samson Abidemi Ajayi, Oluwaseun Oladeji Olaniyi

Asian Journal of Economics, Business and Accounting · pp. 106–125 · Published 26 Feb 2024

10.9734/ajeba/2024/v24i41268

Abstract

The integration of Artificial Intelligence (AI) into big data analytics represents a pivotal shift in the management of Personally Identifiable Information (PII) within the financial sector. This study was prompted by the increasing reliance on AI for handling sensitive financial data and the consequent rise in data security concerns, exemplified by the 2019 Capital One data breach which compromised the PII of over 100 million individuals, highlighting the vulnerabilities inherent in digital data storage and management systems. Aiming to critically evaluate the effects of adopting AI in exploring big data on PII within the financial and economic sectors, the study focused on assessing how AI can transform data management processes, enhance data security, ensure compliance with regulatory requirements, and maintain data integrity. Employing a quantitative research methodology, data was gathered from 532 professionals in the financial sector through surveys distributed via LinkedIn. The hypotheses were tested using multiple regression analysis. The study's findings revealed that the adoption of AI in managing big data significantly enhances the security and privacy of PII in the financial sector. However, it also increases the risk of sophisticated cyber-attacks such as adversarial attacks and data poisoning. Significantly, financial institutions that integrate AI into their data management systems demonstrate higher compliance with data protection regulations, and AI-driven cybersecurity strategies were found to markedly improve the performance of cybersecurity systems in the sector. Based on these insights, the study recommends best practices and guidelines for financial institutions to effectively integrate AI into their data management systems. These include prioritizing data security and privacy, ensuring regulatory compliance, investing in AI-driven cybersecurity, and managing the inherent risks of AI integration. The study advocates for a balanced approach in AI adoption, emphasizing the need for robust security measures, continuous monitoring, and adapting to the evolving regulatory and technological landscape.

Artificial intelligence (AI) big data analytics personally identifiable information (PII) financial sector data security regulatory compliance cybersecurity risks capital one data breach GDPR CCPA AI-driven cybersecurity strategies

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