Effect of Adopting AI to Explore Big Data on Personally Identifiable Information (PII) for Financial and Economic Data Transformation
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".