ML & DL
“ Carriers have only started to realise the benefits of using AI and ML across their organisations ”
JOHN BEAL SENIOR VICE PRESIDENT FOR DATA SCIENCE , LEXISNEXIS RISK SOLUTIONS
controversy is the effect on the future labour market , but “ it is implausible that AI will replace humans entirely ,” says Oliver Tearle , Head of Innovation Technology at The ai Corporation ( ai ).
“ The role of a human – interpreting , analysing , understanding , and compensating for data and events outside of the realms of the model – is still central to many processes . As a result , there is a role for humans and machines in any business process , and instead of man versus machine , the approach should be man and machine .”
Tearle continues : “ AI models live or die by the data they are trained on . Up to 80 % of the time it takes to develop an AI model is devoted to ensuring the data is of a high standard and packed with helpful information . Often , smaller and more information-dense data sets perform better than larger , untreated ones . AI is still bound by the limitations of the data , as well as the ‘ ground truths ’ it ’ s fed in a classification modelling scenario .”
As an example , he says that “ a typical fraud model will only utilise what is known and confirmed to be fraud , as this ensures the model can detect that fraud correctly in the future ”. Tearle recommends that ML-driven strategies are routinely checked and overseen by humans to ensure the integrity of the decisions .
Generally speaking , the hurdles to effective ML and DL adoption are many and complex : they range from the cost of adoption and the sourcing of talent to potential regulatory implications and data challenges ( such as ensuring the quality of data , normalising data across sources , and handling the need for increased volume of data ).
“ AI and ML are more than just buzzwords in the insurance industry ,” John Beal
82 September 2023