본문 바로가기 주메뉴 바로가기
22대 대한민국 국회 국회정보길라잡이 국회의원검색
회원가입 로그인
HOME

정보검색

소장정보 검색

국회도서관 홈으로 정보검색 소장정보 검색

결과 내 검색

동의어 포함

고급검색

인명/단체명

저자정보 상세정보
인명/단체명을 입력하세요.

키워드

  • 대표어

  • 외국어

-

목차보기

Title page 1

Contents 6

Foreword 4

Acknowledgements 5

Abbreviations and acronyms 8

Executive summary 9

1. AI in the Italian financial sector 12

1.1. Introduction 13

1.2. Mapping the deployment of AI in the Italian finance sector, with a focus on financial markets 14

1.2.1. Deployment of AI solutions across the Italian financial system 14

1.2.2. Types of AI use cases in the Italian financial system 16

1.2.3. Breakdown of AI use cases by financial sector 17

1.2.4. Role of third-party AI models and services 18

1.2.5. Use of general-purpose AI models by financial institutions 20

1.2.6. The benefits of AI deployment 23

1.2.7. Use of open-source models and components 24

1.2.8. Challenges and approaches to explainability of AI models 26

1.2.9. Level of action autonomy across AI models 27

1.3. Governance frameworks for AI technologies in the Italian finance sector 28

1.3.1. Governance structures and AI oversight by functions 28

1.3.2. Risk management, operational resilience and cybersecurity 32

1.3.3. Level of understanding of AI technologies and talent needs 34

1.4. Key self-perceived constraints to AI deployment in Italian financial markets 36

1.4.1. Regulatory constraints 36

1.4.2. Non-regulatory constraints 38

References 41

Notes 44

2. Approaches by Italian financial authorities to promote safe AI deployment 45

2.1. Introduction 46

2.2. Monitoring and supervising the deployment of AI applications in the Italian finance sector 46

2.2.1. Monitoring and supervision initiatives by the Italian authorities 46

2.2.2. Use of Supervisory Technology (SupTech) tools 48

2.3. Enabling environment for innovation in Italy 50

2.3.1. Financial regulatory sandbox 50

2.3.2. Other innovation facilitators 51

References 52

3. Policy considerations 54

3.1. Overview of key policy considerations 55

3.1.1. Strengthen recurring data collection on AI adoption trends 56

3.1.2. Promote and support clarity and simplification of the regulatory/supervisory framework 57

3.1.3. Encourage stronger AI governance arrangements for supervised entities 58

3.1.4. Promote safe data-sharing frameworks and practices 59

3.1.5. Foster and support public-private co-operation 60

3.1.6. Highlight and enhance the role of innovation facilitators 61

3.1.7. Support whole-of-government public sector strategic direction for wider AI diffusion in the finance sector 62

3.1.8. Strengthen supervisory capacity 62

3.2. Detailed policy considerations 63

3.2.1. Strengthen recurring data collection on AI adoption and exposure 63

3.2.2. Promote and support clarity and simplification of the regulatory and supervisory framework 65

3.2.3. Require stronger AI governance arrangements for supervised entities 70

3.2.4. Promote safe data-sharing frameworks and practices 76

3.2.5. Foster and support public-private co-operation 81

3.2.6. Highlight and enhance the role of innovation facilitators 83

3.2.7. Support whole-of-government public sector strategic direction for the AI development and use in the finance sector 85

3.2.8. Strengthen supervisory capacity 87

References 92

Notes 100

Annex A. Project background and survey methodology 103

References 109

Notes 109

Annex B. Firms participating in bilateral consultations 110

Tables 7

Table 1.1. Structure of the report by chapter and section 13

Table 2.1. AI-related initiatives of Banca d'Italia, CONSOB and IVASS 46

Table 2.2. Use of SupTech tools by Banca d'Italia and CONSOB 49

Table 3.1. Summary of policy considerations 55

Figures 6

Figure 1.1. Share of respondents currently using AI technologies 15

Figure 1.2. Share of respondents by sector 16

Figure 1.3. Current and expected use of AI by business macro-area 17

Figure 1.4. AI use cases in development and experimentation by firm size 18

Figure 1.5. Use of third-party services for AI deployment 19

Figure 1.6. Types of AI models deployed or in development 20

Figure 1.7. Types of GPAI models used 21

Figure 1.8. Current and future use of General-Purpose AI by business macro-area 22

Figure 1.9. General-Purpose AI use cases in development, experimentation and production 23

Figure 1.10. Benefits of existing AI use cases 24

Figure 1.11. Use of free and open-source AI models and components 25

Figure 1.12. Types of data used to train or fine-tune AI models 26

Figure 1.13. Explainability methods used to interpret AI outputs 27

Figure 1.14. Level of action autonomy 28

Figure 1.15. Choice of AI governance frameworks, controls and/or processes 30

Figure 1.16. Presence of AI-dedicated data science teams 31

Figure 1.17. Designated responsible functions within AI accountability frameworks 32

Figure 1.18. Safeguards for risk management of unintended AI activity 33

Figure 1.19. Implementation of safeguards for emerging AI-specific cyber threats 34

Figure 1.20. Perceived level of understanding of AI technologies by different functions 35

Figure 1.21. AI-related training of employees 36

Figure 1.22. Regulatory constraints to AI adoption and concerns related to clarity and alignment of regulations 37

Figure 1.23. Operational and third party-related resilience rules 38

Figure 1.24. Non-regulatory constraints to the deployment of AI technologies 39

Figure 1.25. Organisational, skills and cultural constraints 40

Figure 1.26. Data-related constraints 41

Boxes 7

Box 3.1. Approaches in other jurisdictions that show positive results 77

Box 3.2. Existing upskilling and capacity-building initiatives for supervisors across Europe 88

Box 3.3. Examples of current SupTech tools and experiments in Europe 91

Annex Tables 7

Table A A.1. Categorisation of AI "purposes" by "business macro-area" 105

Table A A.2. Categorisation of regulatory constraints to AI adoption 107

Table A A.3. Categorisation of non-regulatory constraints to AI adoption 108

Table A B.1. Complete list of participating firms in bilateral consultations 110

추천서가 (다양한 추천 자료를 만나보세요)

권호기사보기

권호기사 목록 테이블로 기사명, 저자명, 페이지, 원문, 기사목차 순으로 되어있습니다.
기사명 저자명 페이지 원문 기사목차

권호

기사명 원문보기 기사목차
닫기