Further information
The five demonstrators receiving funding are as follows.
Hazard Impact Tracker (HIT): leveraging new global cyclone data to create a catastrophe portfolio management platform
Lead: Maximum Information Consulting Ltd
When a cyclone hits, the impact can be shattering. This global weather phenomenon brings intense winds, extreme rain and storm surges to coastal regions. A cyclone can have a huge impact on communities, bringing humanitarian and economic devastation.
Yet studies show that the deadliest and costliest natural disasters seen in the past 20 years were forecast. We can anticipate a disaster and take action before it strikes, bringing significant benefits across multiple sectors:
- humanitarian (fewer lives lost)
- infrastructure (lower reconstruction costs post-event)
- finance (effective risk transfer in insurance and reinsurance industries)
However, there are barriers to deploying it at scale, most significantly uncertainty as to when and under what conditions pre-emptive action should be triggered and inaccessible, inconsistent or incoherent data at a global scale.
This consortium aims to overcome these challenges by developing the HIT, a product that develops a continuous global cyclone dataset and makes analytics accessible via a responsive user interface. The objective is to empower private customers (insurers and reinsurers) and aid agencies alike to effectively respond to cyclones worldwide.
Revealing pension members’ ESG preferences using conversational AI
Lead: Wyser Ltd
This project aims to develop a comprehensive, data-driven system to improve awareness of environment, social and governance (ESG) preferences among pension fund members. By giving pension managers advanced tools and insights to align investment strategies with the unique ESG priorities of their clients, they can encourage responsible and sustainable investment practices.
Investor preferences, including ESG priorities, should drive product selection or pension manager’s recommendations. Quantifying preferences becomes even harder when attempted at the scale a pension fund might need. This research will seek to elicit members’ preferences and understand how that interacts with the financial returns they want to achieve.
Off the back of this industrial research, the demonstrator aims to build prototype solutions that will allow members to easily express their ESG preferences for investment opportunities.
Project Odyssey: opening the National Archives’ legal data to AI for A2J
Lead: Tabled Technologies Ltd
There is a growing use of ChatGPT by lawyers and wider society to create legal arguments that are currently not reliable. The National Archives legal dataset is the primary source of legislation and case law data in the UK jurisdiction.
Project Odyssey is addressing the challenge of unreliable AI in three stages:
- by enriching the National Archives Legislation and Find Case Law primary legal datasets with machine-readable metadata to be made available to all
- fine-tuning large language models (LLMs) based on this enhanced data and using prompt-engineering to create standardised LLM inputs for enhanced outputs
- delivering enhanced means of accessing this legal information via an Access to Justice (A2J) app to benefit litigants in person and small and medium-sized enterprises
Without this intervention, model oversight and publicly available outputs, there is a material risk that these AI systems fail to fulfil the technology’s promise to improve A2J.
ESG made relevant and easy: overlay financial and sustainability materiality
Lead: PortF.io Ltd
The project aims to address key challenges associated with:
- data provision
- resource intensive ESG assessment and reporting
- the complexity of supply chain emissions
- a lack of understanding of the regulatory changes around ESG
It brings together partners from across the professional, financial and legal sectors to:
- develop and demonstrate novel digital solutions for more efficient, accurate, and consistent ESG and impact measurement and tracking
- facilitate benchmarking, learning and improvement
PortF.io’s ESG module will include innovative AI-driven tools developed for simplifying ESG data provision, ESG assessment and reporting and a robust carbon calculator module for scope three emissions. This will provide users with a holistic solution for their ESG compliance needs.
ScanSpot: 3D-modelling ‘digital twin’ data for new insurance products
Lead Aegis Energy Ltd
ScanSpot combines cutting-edge multi-modal sensor Light Detection And Ranging, thermal imaging and mmWave radar technology with machine-learning computer vision techniques to generate new risk classification data for the insurance industry. This helps develop and cost new insurance products for renewable energy and freight/logistics customers.
The shift to renewables in Britain has been accompanied by an evolution in the risks facing insurance companies and their customers.
Electric vehicle (EV) battery fires, for instance, are forecast to increase from 9,400 incidents in 2022 to 260,000 annually by 2035 (Thatcham Research, 2023). Police and insurers are also reporting a significant increase in the theft of high-value EV chargers and batteries and copper or componentry from solar farms (Crimestoppers; TT-Club, 2023). New risks add to the soaring problem of cargo theft costing insurers and the freight-forwarding industry upwards of £500 million per year in 2022 (NaCVS).
ScanSpot will deliver a cloud-based software product for the insurance industry and their customers to analyse, classify and mitigate risks such as fires, theft and unsafe vehicle behaviours.
The system compiles 15 months of novel data collected from a new multi-sensor array prototype installed at two British renewables and EV charger facilities. It also applies AI deep-learning models for automated anomalous incident flagging. Data and the results of the project will be shared widely with insurers, police and industry.
