Spatial datasets can provide considerable information on numerous variables that influence credit and mortgage risk. For instance location of a business, an individual or a property and the access to infrastructure or the exposure to climatic risks like floods can be quantified using geospatial analysis and our flood monitoring data. These when combined with other spatial variables can provide richer insights into credit and mortgage risks.

In 2022 we collaborated with a multinational team of researchers to develop a set of spatial indicators which can be used for credit risk modeling. For this, we used data on population density, land cover, access to roads, highways and rail, apart from other datasets like night-time lights.