Report Industry Investment Rating - Not provided in the content Core Viewpoints of the Report - The report presents the research results of a dedicated module on generative AI in the 2024 Italian Survey of Consumer Expectations (ISCE). As of April 2024, 75.6% of the Italian population aged 18 - 75 were aware of gen AI, 36.7% had used it in the past 12 months, and 20.1% reported monthly use. Socio - economic factors significantly influence adoption rates, with men, those with a university degree, and young people (especially students) having higher usage rates. Gen AI is expected to be used more frequently in education and leisure activities in the coming months, and the income return rate associated with gen AI use is about 2% [9]. Summary According to Relevant Catalogs 1. Product Introduction - Modern generative AI is expected to have a profound socio - economic impact on society. Since the end of 2022, tools like ChatGPT and Google Gemini have influenced daily activities. The adoption of generative AI tools can change employment in the labor market, and its adoption rate is faster compared to early breakthrough technologies. The report explores who uses generative AI, how much they use it, the purposes of use, and the potential returns [13]. 2. Survey - The data comes from the Italian Survey of Consumer Expectations (ISCE), a representative survey of the Italian population aged 18 - 75. It is benchmarked with national employment and income estimates. The 3rd wave in April 2024 included a special part on AI - related topics, such as knowledge, use, and potential use in different fields. The survey questions were presented in a random order to ensure unbiased responses [14][26][29]. - The sample includes 5005 respondents. 75.6% of respondents were aware of AI, and 35.7% reported using it. Leisure and education/training are the areas with the highest expected future use [30]. 3. Factors Affecting AI Awareness and Use - Gender: Men are about 8 percentage points more likely to be aware of AI than women, and about 7 percentage points more likely to use it in the past year. This gender gap exists even after considering other factors [17]. - Education: Higher education levels are associated with higher AI awareness and use. Respondents with a high - school diploma or university degree are about 10 and 16 percentage points more likely to be aware of generative AI respectively, and having a university degree significantly increases the likelihood of use [38]. - Age: Young people (especially those aged 18 - 34) are more likely to be aware of and use AI. There is a significant "digital divide" between young and old people [18]. - Occupation: Students have higher AI awareness and use levels, while teachers have lower awareness but relatively high adoption and use rates once aware [40]. - Income: Higher income is positively correlated with AI awareness and use, but the effect is moderate [41]. - City size: Living in larger cities has little and statistically insignificant impact on AI awareness and use [20]. - Social factors: Social activities are significantly positively correlated with AI awareness and use, while trust mainly affects awareness and has little impact on use [42]. 4. Returns on AI - Using a Mincer - type income function, the use of generative AI tools is associated with a 1.8 - 2.2% increase in income, which is equivalent to the return on an additional half - year of education investment and about 10% of the return on computer use in the early 1990s [50]. - Micro - level evidence shows significant productivity improvements after AI adoption, but at the macro - economic level, the contribution to total factor productivity growth is relatively modest [52]. - AI use may exacerbate the gender income gap. Men have larger and more precisely estimated returns from using generative AI, and people in high - exposure industries and occupations also have relatively higher returns [53][54]. 5. Conclusion - There are significant differences in AI awareness and use among genders, ages, and education levels. Targeted measures are needed to bridge the digital divide [57]. - The economic benefits of AI application are obvious, but its adoption may widen the gender gap in the labor market. Policy - makers should invest in digital literacy projects, include AI training in education courses, and develop labor training programs to ensure the wide distribution of AI benefits [58][60].

Investment Rating - The report does not provide a specific investment rating for the industry Core Insights - The increasing adoption of artificial intelligence (AI) in financial institutions is transforming operations, risk management, and customer interactions, but the limited explainability of complex AI models poses significant challenges for both financial institutions and regulators [7][9] - Explainability is crucial for transparency, accountability, regulatory compliance, and consumer trust, yet complex AI models like deep learning and large language models (LLMs) are often difficult to interpret [7][9] - There is a need for robust model risk management (MRM) practices in the context of AI, balancing explainability and model performance while ensuring risks are adequately assessed and managed [9][19] Summary by Sections Introduction - AI models are increasingly applied across all business activities in financial institutions, with a cautious approach in customer-facing applications [11] - The report highlights the importance of explainability in AI models, particularly for critical business activities [12] MRM and Explainability - Existing MRM guidelines are often high-level and may not adequately address the specific challenges posed by advanced AI models [19][22] - The report discusses the need for clearer articulation of explainability concepts within existing MRM requirements to better accommodate AI models [19][22] Challenges in Implementing Explainability Requirements - Financial institutions face challenges in meeting existing regulatory requirements for AI model explainability, particularly with complex models like deep neural networks [40][56] - The report emphasizes the need for tailored explainability requirements based on the audience, such as senior management, consumers, or regulators [58] Potential Adjustments to MRM Guidelines - The report suggests potential adjustments to MRM guidelines to better address the unique challenges posed by AI models, including the need for clearer definitions and expectations regarding model changes [59][60] Conclusion - The report concludes that overcoming explainability challenges is crucial for financial institutions to leverage AI effectively while maintaining regulatory compliance and managing risks [17][18]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - Generative AI has the potential to significantly boost global productivity, with estimates suggesting annual gains between $2.6 trillion and $4.4 trillion, and an output increase of 15-20% over 15 years post-adoption [3][4] - A survey indicates that over 40% of corporations report a return on investment from advanced Generative AI initiatives within the range of 11-30% [3] - The adoption rate of Generative AI among firms is rapidly increasing, with 65% of international corporations using it regularly by early 2024, nearly double the percentage from 2023 [6][5] - The amount of data created globally is projected to grow from 149 zettabytes in 2024 to over 394 zettabytes by 2028, fueling AI development [7] Summary by Sections Workshop Goals and Focus - The workshops aim to showcase projects, share expertise among central banks, and reduce reliance on external service providers, with the latest workshop focusing on Generative AI applications in central banking [2] AI Applications in Central Banking - AI enhances forecasting and nowcasting capabilities, regulatory compliance, financial supervision, and legal analysis, indicating its growing ubiquity in central banking [9][11] Workforce and Governance - A sound AI governance framework is essential, emphasizing policy preparedness and the need for firms to adapt their workforce towards IT, engineering, and mathematics expertise [19][23] - Training and reskilling are crucial for successful AI adoption, addressing resistance to change among employees [23] Cross-Institutional Cooperation - Encouragement of cross-institutional cooperation is vital due to the blurred regulatory boundaries in data-intensive technologies like AI, with a focus on cross-border data sharing [24][25]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - Understanding the evolution of AI's capabilities is crucial for shaping policies that ensure equitable growth and employment stability, as AI may complement or substitute human skills [5] - The report highlights that the impact of AI on the labor market varies across different wage levels, with lower AI capabilities affecting around 7% of skills, while higher capabilities could expose up to 45% of skills in the highest wage quartile [114] - The findings suggest that while AI may initially affect occupations uniformly, the long-term effects could lead to increased income inequality, particularly benefiting high-wage occupations [71] Summary by Sections Measuring AI Exposure - The report constructs the AI Share Automatability (AISA) Index, which combines the time spent on computer interactions and the skills within AI's capabilities to assess the potential for automation across various occupations [20][32] - The AISA index shows that industries with high computer interaction, such as Legal and Engineering, have a higher potential for AI integration, while low-paid, labor-intensive jobs face challenges due to their working modalities [33][48] Skills and Occupations - The analysis indicates that cognitive skills involving computer interactions are more susceptible to automation, with lower-wage occupations experiencing a saturation of side skills at lower AI capabilities [90] - The report differentiates between core and side skills, finding that side skills are affected by AI advancements at lower capabilities, while core skills see a more gradual impact [64][90] Complementarity and Substitution - The report emphasizes that AI's role in the workplace is not solely as a substitute for human labor but also as a tool for augmenting human efficiency, particularly in high-wage occupations [68][71] - As AI capabilities increase, the complementarity effect becomes more pronounced in higher-wage occupations, suggesting a potential for significant productivity gains [97] Robustness and Comparisons - The report conducts robustness tests by comparing different measures of AI exposure, including the AIOE index, and finds that higher-wage occupations benefit more from increased AI capabilities [80][101] - The findings indicate that the impact of AI on occupations varies significantly across the wage spectrum, with higher capabilities leading to greater exposure in high-wage jobs [84][97]

Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - Project Nexus aims to enhance cross-border payments by interlinking domestic instant payment systems (IPS), enabling transactions to be completed within 60 seconds in most cases [14][18] - The initiative is aligned with the G20 Roadmap for Enhancing Cross-border Payments, which prioritizes speed, cost, transparency, and accessibility [14][31] - Nexus is designed to standardize the connection between various IPS, reducing the complexity and costs associated with bilateral links [16][30] Summary by Sections Executive Summary - Nexus is a project by the Bank for International Settlements Innovation Hub that seeks to improve the speed, cost, transparency, and accessibility of cross-border payments by linking domestic IPS [13][14] - The project has been validated through a successful proof of concept involving the Eurosystem, Malaysia, and Singapore [16][37] Benefits of Linking Instant Payment Systems - Linking IPS can enable cross-border payments to be processed in seconds, significantly improving user experience compared to traditional methods [20][22] - The interlinking of IPS is already in progress in several regions, including Southeast Asia, which has seen multiple successful linkages [22][23] Key Takeaways - Nexus aims to facilitate cross-border payments that are fast, cost-effective, and transparent, addressing the challenges of existing bilateral linkages [18][19] - The project has developed a comprehensive governance framework and technology blueprint to support its implementation [38] How Nexus Works - Nexus operates as a service that allows IPS operators to connect and communicate using standardized APIs and ISO 20022 messages, simplifying the process of cross-border payments [27][29] - The Nexus scheme rulebook helps manage differences in domestic payment systems, minimizing operational complexity [29][30] User Experience - Nexus is designed to enhance the user experience for both senders and recipients, ensuring transparency in fees and exchange rates [33][35] - Payments through Nexus can be initiated by individuals or businesses, supporting various use cases including person-to-person and business-to-business transactions [43][44] Progress to Date - The project has made significant strides, including the development of a working prototype and collaboration with central banks in the ASEAN-5 region [37][38] - Ongoing efforts focus on finalizing governance structures and a sustainable business model for Nexus [38]