AI
for a while now , especially in car insurance , and at the heart of this is the accurate assessment of individual risk .
This has paved the way for a rise in drivingbehaviour classification models , driven by telematics data , that capture the capabilities of connected vehicles and edge computing . Access to real-time data is enabling insurers to reimagine their insurance products and deliver hyper-personalised insurance offerings .
Another emerging trend – which we have seen in other industries , too – is the use of AI to speed up data processing . This reduces the human time needed to process claims or investigate fraud , which allows insurers to concentrate their human resources on validation , correction and non-trivial investigation .
However , it is worth noting that some of the AI trends that were starting to emerge – like computer vision – could get stifled by much-needed regulation , such as the recently published proposal for a regulation laying down harmonised rules on AI by the EU . Examples of this include Lemonade ’ s ML model , which uses facial recognition to assess if a claimant is trustworthy , or applications that include the use of computer imagery to instantly assess damage to a car or help advise a customer on the medical services to seek based on their symptoms .
Hyper-automation is emerging as the new technological system of choice . What makes it so unique ? Wayne Butterfield : Hyper-automation is just the combination of lots of useful technologies , ranging from those that assist in the discovery of opportunity , a separate set that helps to automate processes and additional capabilities that prove value
has been created . It ’ s this end-to-end that differentiates it from standalone machine learning , RPA and AI , and it ’ s why it has become so useful to organisations .
Guy Kirkwood : Hyper-automation can reach more workflows than RPA can alone . Take processing documents as an example . RPA can scan and extract structured data that you have taught it to look for . However , when you apply Document Understanding and Computer Vision , the software robots can also read and understand unstructured data such as handwritten notes , scans or images . As a result , automation ’ s potential grows .
96 July 2021