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AI initiatives within life/health distribution

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Artificial intelligence, IoT and big data have become inescapable buzzwords but the notion that software will perform functions typically associated with the human mind is fast becoming a reality. However, a clear separation exists between AI assisted user journeys for general insurance lines including chatbots and intelligent product recommendations, and the long term life/health insurance that still requires banks and agents to distribute.

Despite this distinction, several startups and insurers are looking at the intersection of offline distribution and online assistance to optimize the agency channel. One of these is Beijing based Xuequibao who’s agency management app has combined the standard agency management workflow with AI based recommendations that are pushed to agents in real time.

Legacy insurers are also hard at work to evolve distribution. Taikang Life has devised an intelligent product recommendation engine using ‘parallel inference’ techniques, a method of statistical reasoning based on imprecise information, to generate product recommendations and provides repeat visitors with a pre-populated user journey. Elsewhere, Ping An, China’s pre-eminent digital insurer has pioneered a facial recognition feature which it uses to authenticate pending policy holders (those who have applied for life/health insurance but are waiting for the policy to be activated). Since launch, the biometric identification feature has lowed the rate of customers who withdraw their application during this window from the industry average of 4% to 1.4%.

Finally, Beijing based AIbroker (17broker.cn) is working on digital employee benefits and has combined a Zenefits inspired business model with advanced analytics to tailor health insurance plans that are tax-deductible under newly released income tax regulations which has spurred several new brokers to enter China’s EB market.

Ultimately, although AI and advanced analytics have successfully been applied to general insurance lines, the ability to upgrade an offline agency force with AI based tools to lower agent churn rate and maximize conversion rate, and bring visibility into individual agent practices that have historically been difficult for insurers to implement. Fortunately, recent advances in decision tree-based AI tools, combined with previously absent regulatory support, is heralding a new era for agency management practices in China, one that is characterized by service and renewal rates as opposed to premium per agent.

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