VEŠTAČKA INTELIGENCIJA U FINANSIJSKOJ INDUSTRIJI: KAKO SPREČITI CURENJE PODATAKA
Bibliographic record
Abstract
EU zaostaje za glavnim američkim i azijskim konkurentima po izdacima za informacionu bezbednost po IT trošku ili zaposlenom. U poređenju sa drugim oblastima ekonomske aktivnosti, finansijske institucije izgleda da su manje zabrinute zbog potencijalnih povreda sistema. Međutim, one su takođe bolje opremljene za brzo prepoznavanje neslaganja jer im je infrastruktura relativno manje kompleksna. Sa predstojećom primenom NIS 2 direktiva, imperativno je ispitati izazove sa kojima se finansijske institucije suočavaju usled trenutnog porasta mogućnosti VI koje bi hakeri i drugi zlonamerni akteri mogli iskoristiti bez posledica ukoliko odgovarajuće mere i sistemi nisu implementirani. U ovom istraživanju pružamo pregled izazova sa kojima se finansijske institucije mogu suočiti, ali takođe raspravljamo o rešenjima, poput napredovanja u proizvodima generativne VI ili širokoj primeni bihevioralne biometrije, što bi trebalo da poveća pouzdanost onlajn-aktivnosti i spreči zloupotrebu ličnih podataka u širem kontekstu. Osim specifičnih rešenja kompanija, pružamo platformu za diskusiju za donosioce odluka u vladi.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; both teacher heads agree on what is shown here.
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".