Enhancing Data Privacy in National Business Infrastructure: Measures that Concern the Analytics and Finance Industry

Annotation The rise in adoption of big data in making decisions has raised concern over protection of individuals' datä in financial and analytics industries. This paper focuses on analyzing the way of using privacy-preserving technologies to protect personal financial information and its use for analysis. The research is centered round anonymization, data masking, encryption, and differential privacy methods that were performed on what was referred to as the Financial Risk Assessment dataset that had attributes such as credit scores, income and amount borrowed. The goal here is to balance good data protection with the need to have the dataset as relevant for the analysis of financial risks. A comparative cross-sectional design was employed where the effectiveness of the established communication techniques was assessed using both qualitative and quantitative comparative approaches. Findings also show that prescribed PETs can be used effectively in preserving privacy of individuals but, at the same time, do not reduce the utility of the generated dataset. Original scatter plot, bar chart, and box plot results reveal systematic patterns in creditworthiness, income level, and loan amount by risk rating and employment status, despite the application of anonymization interventions. A privacy risk assessment performed after the implementation of the new IT system proved that the organization adhered to legal frameworks like GDPR and CCPA in that no private data was leaked. The paper also states that privacy friendly methods can be applied in the finance sector as a way of solving data privacy predicament thus helping the organizations to continue using the personal information in their analysis and decision making in line with the set regulatory standards. The outcomes of the study provide a real-world framework containing suggestions on how to improve data privacy while maintaining or improving data usefulness in financial organizations.