IT Security and Privacy Issues in Global Financial Services Institutions: Do Socio-Economic and Cultural Factors Matter?
Bibliographic record
Abstract
Financial services institutions (FSIs) around the globe know they must proactively work toward protecting customer data and thwarting emerging security threats. Deloitte Touche Tohmatsu (DTT), an international firm that provides audit, consulting, and financial advisory services has used its networks and reach to investigate security and privacy issues in FSIs around the world. DTTpsilas first survey appeared in 2003 and four others have followed since then. This present article draws from last survey. Given that the literature has shown that socio-economic and cultural factors are important considerations for organizations when accepting innovations and new practices. This study was designed to provide a layer of understanding not seen in the DTTpsilas study by examining whether socio-economic and cultural indicators matter in how IT security and privacy issues are being perceived in global FSIs. Two relevant hypotheses were developed to test our assertions. The main finding of the study was that such contextual factors may not be sufficient in differentiating how global FISs view or respond to key IT security and privacy issues. However, our study found one item related to security awareness training for FISspsila employees to vary significantly across the surveyed regions when the gross domestic product (GDP per capita) variable was used in the analysis. It is hoped that our studypsilas findings and conclusion will be beneficial to practitioners and researchers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".