INSURANCE

ALEX JOHNSON QUANTEXA

The state of ML and DL within the insurance industry “ The richer the context generated as a feed into an ML / DL algorithm , the more accurate the decision you can obtain from each model ,” Johnson says . “ For example , in deciding whether to pay a claim , a handler would have to gather insight from multiple sources in an iterative way , related to more than just the claim itself . This may include looking at the policy history ( such as adjustments ), understanding information regarding circumstances , thirdparty involvements , locations , IOT signals , estimations , suppliers , payee information and so on . However , this is time consuming , costly , and subject to human error .

“ What DL enables , when combined with contextual analytics , is the ability to automatically ingest , connect and analyse multiple siloed sources of information ( both real-time and historic ), aggregating the context and iterating a human thought process with a rapid and deep analytical simulation . This effectively imitates human decisions albeit faster and more accurately , and can also be applied to net new decisions with far less ‘ model training ’.”

Johnson acknowledges that traditional ML approaches have limitations in terms of the breadth of data context being analysed and require large numbers of ‘ good ’ historical outcomes – something that contextual analytics and DL can overcome .

John Ardelius , CTO and Co-Founder of Hedvig , agrees with claims as a good area of application : “ Claims service automation is a clear use case for embracing ML , thanks to the ability to reduce manual tasks by up to 50 %. By using ML to unlock smarter pricing and underwriting based on non-trivial combinations of features and patterns , insurers can decrease their loss ratio by several percentage points . Integrating ML across onboarding processes means smarter flows , which can increase conversion rates as well as cross-sales , increasing portfolio premiums .”

Ardelius pinpoints another prominent area of focus : “ We ’ re seeing great potential for improved pricing strategies and service offerings as a result of improved pattern recognition powered by large quantities of structured data , such as fraud detection and customer segmentation .”

Have insurers been too slow to embrace ML and DL ? Clearly , then , to take advantage of the possibilities within ML and DL , insurers must get their house in order when it comes to data . insurtechdigital . com 67