Project details
Built on the initial work of ‘Applying AI to building blueprints for insurance risk assessment (AURIE)’, this project aims to develop an ontology digital twinning approach to construct the domain knowledge graphs for improved risk assessment and underwriting capability. It will:

Develop an ontological knowledge framework from CAD drawings/BIM models (building ontology), safety codes (safety/risk ontology), and sensor data (occupant behaviour ontology) for risk knowledge modelling of buildings.

Develop a multi-source knowledge fusion method based on the ontology frameworks for a uniform domain knowledge model.

Develop arrangements for automatically querying the uniform knowledge model for risk assessment of buildings.

Explore the practicalities, including software or tool development, for implementing these techniques using the blueprint and other information for commercial and industrial properties available to WTW and WTW clients.

Its ultimate ambition is to transform property insurance risk analysis and underwriting from present arrangements employing extensive manual processing into an automatic and reliable process using ontology digital twins.

This PhD project will be one of six PhD projects being pursued in a cross-disciplinary mini-College of Doctoral Training (mini-CDT) running at Loughborough University from 2022 to 2026. This mini-CDT is partly financed and supported by the WTW Research Network, the award-winning collaboration between academia, finance and research industries on the understanding and quantification of risk.

This industry collaboration builds on the earlier partnership between Loughborough University and WTW in the recently completed TECHNGI research project on technology and next-generation insurance services. For a full description of the mini-CDT, of the individual PhD projects and of the support provided by the WTW Research Network to the mini-CDT see [Mini-Centre of Doctoral Training in Industrial and Commercial Property Insurance – TECHNGI].