Their principal challenge was that they had no robust method to identify the probability and severity of these events over the lifetime of the mortgages across the whole of the United States - at a granular property level, and across multiple climate hazards. They also needed a solution that was scalable and dynamic across thousands of underlying properties and their mortgages, which integrated directly with their workflow.

Sust Global’s climate data analytics were able to provide transparency on climate risk across the portfolio, at the RMBS and individual mortgage level, starting with only the RMBS identifiers (such as CUSIP or ISIN). The location and value of each mortgage was extracted from the identifier via a platform integrated with Sust’s API, which returned real-time RMBS climate risk intelligence, enabling portfolio managers to conduct prospective analyses of investment opportunities as well as for portfolio risk assessments. Climate risks were quantified in terms of probability and severity across six climate hazards: wildfire, floods, hurricanes, heatwaves, water stress and sea level rise.

To ensure that this data could be fully integrated with the existing investment process, these risks were then converted into financial loss projections, using algorithms developed using massive datasets of historic climate events and the resulting financial losses. These algorithms provide annual financial loss projections, hazard by hazard, over the life of each RMBS and the underlying mortgages.

As a result the Head of MBS Portfolios, and the portfolio managers are able to proceed with confidence and clarity in their decision making with respect to increasing climate risks - across both. In doing so they are improving their risk-adjusted returns and fulfilling their fiduciary responsibilities. It also means that they are now fully compliant, if and when climate reporting legislation is introduced by the SEC.

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