problematic patterns of behaviour as part of its fraud prevention strategy , then bad data will diminish the algorithm ’ s ability to effectively spot fraud . This speaks to a much broader theme of bias within AI .
Peppercorn ’ s Nigel Lombard says : “ Currently , risk analysis is a linear experience ; it ’ s a one-size-fits-all approach that ’ s designed to favour the provider . AI on the other hand can collate volumes of data and identify behavioural patterns and trends , allowing providers to listen and react to their customers . In practice , this could mean tweaking the way a provider speaks to a customer based on what mood they ’ re in or creating new products following feedback , for example . Predictive modelling can take this one step further , but it ’ s entirely dependent on the quality of data inputted into the models .” Meghana Nile adds : “ While AI has its potential ethical risks if not used correctly , if applied right , it can be exceptionally powerful . AI can address potential bias in underwriting by identifying and eliminating any potential decision-making disparities due to race , gender , age , or ethnicity , and that ’ s what can make for fairer pricing .
“ Another positive impact AI will have on premiums is its ability to detect fraud and identify high-risk customers . This ability enhances risk monitoring and , in turn , reduces pricing . With regulations like the Financial Conduct Authority ( FCA ) guideline of customer duty , this will steer the industry into taking a more holistic and analytical approach to pricing . Because AI can play a big role to estimate equitable and fair premiums , we ’ re likely to see its presence in insurance massively increase .”
Should insurance technology translate into lower premiums ? When implementing new technologies like AI , customer buy-in is extremely important .
“ While the use of AI in insurance is still in its early days , adoption will accelerate over the next one or two years ”
NIGEL LOMBARD CEO AND CO-FOUNDER , PEPPERCORN AI
88 May 2023