  There is growing recognition among financial institutions, financial
regulators and policy makers of the importance of addressing nature-related
risks and opportunities. Evaluating and assessing nature-related risks for
financial institutions is challenging due to the large volume of heterogeneous
data available on nature and the complexity of investment value chains and the
various components' relationship to nature. The dual problem of scaling data
analytics and analysing complex systems can be addressed using Artificial
Intelligence (AI). We address issues such as plugging existing data gaps with
discovered data, data estimation under uncertainty, time series analysis and
(near) real-time updates. This report presents potential AI solutions for
models of two distinct use cases, the Brazil Beef Supply Use Case and the Water
Utility Use Case. Our two use cases cover a broad perspective within
sustainable finance. The Brazilian cattle farming use case is an example of
greening finance - integrating nature-related considerations into mainstream
financial decision-making to transition investments away from sectors with poor
historical track records and unsustainable operations. The deployment of
nature-based solutions in the UK water utility use case is an example of
financing green - driving investment to nature-positive outcomes. The two use
cases also cover different sectors, geographies, financial assets and AI
modelling techniques, providing an overview on how AI could be applied to
different challenges relating to nature's integration into finance. This report
is primarily aimed at financial institutions but is also of interest to ESG
data providers, TNFD, systems modellers, and, of course, AI practitioners.
