SAI Brazil conducted an audit of the use of funds of the Brazil’s Severance Pay Fund (Fundo de Garantia do Tempo de Serviço) between 2020 and 2024. The audited funds amounted to 743 billion reals (USD 142.43 billion). The audit results revealed a low level of the Fund budget execution in the areas of urban infrastructure and basic sanitation. Out of the 28 billion reals (USD 5.37 billion) allocated for urban infrastructure development, only 3.4 billion reals (USD 0.65 billion) were used. This hinders the achievement of goals for improving the quality of life in cities. Within the "Sanitation for All" programme (Saneamento para Todos), 46.5% of the planned 24 billion reals (USD 4.6 billion) were used, with the majority allocated to the southeastern region. However, only 33% of resources were directed to the development of the northern and northeastern regions, which are in need of access to sanitation services and investments. *The Brazil’s Severance Pay Fund is a Brazilian public fund for employers to deposit 8% of an employees monthly salaries. Its purpose is to protect workers and promote investments in housing, sanitation and infrastructure to improve the quality of life for the Brazilian population.

The instrument is designed to resolve tax disputes between states and prevent double taxation. It is noted that the expansion of cross-border economic activity and the strengthening of measures to combat base erosion have led to growing divergences in the interpretation and application of tax rules across jurisdictions. In this context, effective dispute resolution mechanisms are becoming increasingly important, as they contribute to a more predictable business environment. The updated version of the document focuses on practical aspects of implementing the procedures, including enhancing transparency, enabling early dispute prevention, clearly delineating the functions of tax audit bodies and competent authorities, and ensuring their institutional independence. The OECD recommends making greater use of preventive instruments, including bilateral and multilateral advance pricing agreements.

Artificial intelligence (AI) is being increasingly integrated into the financial sector. Key challenges include the lack of transparency in algorithmic decision-making, as well as the growing dependence of financial institutions on external software providers. Difficulties in overseeing AI use largely reflect broader compliance weaknesses within financial organisations, particularly in the area of data quality risk management. One possible solution is the introduction of risk-based supervision, which involves prioritising supervisory resources and control measures depending on the risk profile of AI systems and institutions. OECD experts also consider it advisable to establish regulatory sandboxes where AI deployment can take place with continuous feedback from supervisory authorities, facilitating the testing and refinement of oversight mechanisms.

Experts examined how upper secondary school leaving examination systems are organised across OECD countries and how they affect access to higher education and labour market outcomes. Completing upper secondary education has become a basic standard, with attainment rates among young people in OECD countries reaching 80–85%, yet exam formats and certification requirements differ significantly across countries. High-stakes exam models tend to increase stress and exacerbate inequality risks, while systems relying heavily on internal assessment face challenges related to comparability and public trust. The OECD concludes that effective certification frameworks should combine external validation of learning outcomes, assessment of a broad range of skills, and transparent rules that are clearly understood by higher education institutions and employers.

As noted by KPMG, companies are moving towards the systematic integration of AI into business processes and are increasingly facing skills shortages and various organisational barriers. Based on a survey of 2,500 executives from technology companies across 27 countries, priorities are shifting towards scaling AI solutions, deploying AI agents, modernising IT infrastructure, and embedding AI into operational workflows. KPMG recommends that companies not only accelerate workforce training to build new competitive advantages, but also strengthen trust in AI, enhance organisational adaptability through corporate culture, and develop a future-ready workforce.