The Bank for International Settlements (BIS) is launching Project Danu, which aims to leverage digital twin technology to monitor emerging risks to financial stability. The project is a collaboration between the BIS Innovation Hub's Eurosystem, Hong Kong and Singapore Centres in conjunction with Banque de France, De Nederlandsche Bank, Hong Kong Monetary Authority and the Monetary Authority of Singapore.
Emerging risks such as natural disasters can have a significant and immediate impact on the financial system. Digital twin technology provides an effective solution, enabling real-time monitoring and scenario simulation that offer both immediate assessments and forward-looking insights.
While digital twin technology is conceptually useful to monitor financial stability across various domains, Danu will showcase its application in the context of extreme weather events and natural catastrophe risks. The latter are examples of emerging risks with growing financial repercussions. Indeed, globally, natural disasters are estimated to have caused economic losses of around USD 131 billion in the first half of 2025, well above the ten-year average. These events affect insurers through large claims and banks through increased credit risks, property devaluation and rising default rates.
A major challenge in assessing such emerging risks is the lack of sufficient data, methodologies and tools to quantify the financial impact. Digital twin technology addresses this by integrating real-time data and providing valuable insights into the impact of these events on financial stability. It allows central banks to connect, integrate and model relevant data more effectively.
Project Danu aims to develop a digital twin framework that supports this approach, providing insights into how emerging risks may affect financial stability.
Emerging risks, such as floods and wildfires, can have a sudden and significant impact on the financial system.
Challenges with emerging risks are:
- Data availability is limited
- Limited knowledge or access to resources to develop methodologies
- Lack of interactive analyses, real-time insights and predictive capabilities
Developing a digital twin framework:
- Real-time monitoring & scenario simulation
- Combining various data sources in one framework
- Continuous data ingestion and impact calculation
- Modular framework that is adaptable to different use cases and needs
- Dynamic and user-friendly
A digital twin framework that is designed to be scalable and adaptable for use by the central banking and supervisory community:
- a codebase that users can implement within their own technical environments to deploy a digital twin
- a general methodology, which will be documented and delivered alongside the prototype
