Road traffic accidents are rare incidents with severe consequences. This creates a fundamental challenge for traffic safety work: decisions must be made with limited data, constrained expert capacity and a strong risk of acting too late. While the ambition is zero fatalities and serious injuries (Vision Zero), priorities have traditionally been reactive, driven by fragmented analyses and local practices. The Risk Curve solution was developed to address this by providing a shared, data-driven and objective foundation for prioritizing preventive traffic safety measures before accidents occur.
The solution is a national risk model for road curves on both national and county roads. By combining statistics, machine learning and domain expertise, it estimates which curves are most likely to see serious accidents in the future. Instead of focusing on where accidents have already happened, the model helps identify where preventive action is most needed. It aggregates road geometry, speed limits, traffic volume (ADT) and historical accident data into a unified dataset, classifies curves into risk categories and ranks them accordingly. In practice, the top 3–5 percent of curves capture a significant share of severe accidents. The output is delivered as a standardized, geospatial dataset on the national data platform and is directly accessible for decision support.
The Risk Curve solution directs attention to where inspections and assessments are most likely to have the greatest preventive effect, enabling more efficient use of limited expert capacity. Traffic safety work has shifted from reactive to proactive and preventive, improving speed, consistency and transparency in decision making. Østfold County was among the first adopters and has integrated the model into planning and budgeting, with plans for safety measures at 14 curves in 2026.
Beyond individual measures, the solution has established a shared national language for risk. Decisions can be explained using transparent, data-driven risk scores, strengthening trust and governance. Ultimately, the value of the Risk Curve solution lies in its societal impact: by increasing the likelihood that resources are directed to where they prevent the most serious injuries and fatalities, the solution directly supports the ambition behind Vision Zero, that everyone gets home safely.
The Riksbank is transforming into a modern, open and resilient central bank that uses data and AI as core capabilities. This development has been driven through a three‑ to four‑year data and analytics transformation, which has now successfully transitioned into the permanent organisation. New ways of working and a fully cross‑functional governance model are firmly established.
A major part of the transformation has been building a modern data and analytics ecosystem that functions reliably under strict regulation. Since the Riksbank cannot freely use public cloud services, the data platform is built on premises using open source components, strong security controls and container based technologies. This ensures flexibility between environments and providers while keeping sensitive information protected and enabling advanced analytics and AI.
To strengthen analytical capacity, the Bank defined four ambitions:
1. Carry out an analytical shift based on more appropriate, high quality and timely data.
2. Use new tools and data to increase the capacity for data driven decision making.
3. Create a new technology infrastructure enabling stability, sovereignty and flexibility.
4. Explore new analytical opportunities using modern technologies, including AI.
A unified environment for data, code and reports enables efficient collaboration, reproducibility and version controlled analytical work. Teams work in a fully integrated value stream that brings together data engineers, platform engineers, data scientists, AI engineers, software developers, IT security specialists and architects with economists, analysts, legal experts and subject matter experts.
The underlying architecture uses open metadata standards for governance and a structured data approach inspired by the medallion model, ensuring traceability, quality and well managed data flows from raw ingestion to analytical and policy ready products.
Seven dedicated data teams focus on core policy areas of the Riksbank’s mission, including Monetary Policy, Financial Stability, Payments and Markets. These teams work side by side with domain experts to produce high quality data products, models and insights that support critical policy functions.
Security is integrated throughout. DevSecOps practices, supply chain security and continuous testing ensure safe development from the start. Sensitive data remains within protected on prem environments, and the platform architecture supports innovation without compromising trust or compliance.
AI readiness under development:
The Riksbank is also building secure, sovereign and long term AI capabilities. While still under development, the Bank is establishing:
• Secure on prem computational capacity to support modern AI models without cloud dependency.
• Isolated and controlled environments for large language models, ensuring confidentiality and regulatory compliance.
• AI supported internal knowledge retrieval, using advanced techniques to help employees access and navigate internal policies and documents.
• Automated tools that improve accessibility, documentation and analytical workflows.
• A systematic plan for building AI competence, including training, knowledge sharing and practical adoption in day to day work.
Together, these developments ensure that the Riksbank is well on its way to becoming AI ready, enabling future use cases even for sensitive data.
Skatteetaten has implemented one of the most comprehensive and responsible AI programs in the Nordic public sector. Through strategic and long term use of artificial intelligence, the Agency has improved public services, strengthened operational efficiency, enhanced decision making, and increased transparency across the entire tax ecosystem. Both traditional machine learning and generative AI are now embedded as core capabilities, at the centre of out product and services, fundamentally transforming how we design, deliver, and govern tax related services. This approach has raised compliance levels, improved user experiences, and generates an estimated 7.5 billion NOK in yearly revenue. This effectivization through the use of Data and AI directly supports the Norwegian welfare state and provides value and benefits directly and tangibly for all citizens.