June 2019
Middle East Insurance Review
57
Insights – Technology
AI in insurance: Balancing the promise with ethical dilemmas AI and its applications promise to make insurance more relevant, appealing and affordable to customers. But challenges such as discrimination and the abuse of personal data need to be acknowledged by even the most vocal proponents of AI. Proactively tackling such challenges will reduce the probability of legislative and regulatory backlashes that may prevent AI from delivering its full potential, says Mr Peter Ohnemus of dacadoo.
T
he insurance sector is on the cusp of entering a new era. At the core of its transformation is the sector’s employment of policyholder data generated from the IoT applications. Consumers are widely believed to benefit through more personalised service, faster claims processing and lower premiums. Operators, too, are set to profit from selling more custom-designed policies and upselling other relevant products to their inforce base. One of the key technological driving forces is AI. In healthcare, for example, it is enabling quantum leaps in analysing consumer health data to improve outcomes by suggesting diagnoses and interpreting medical device images such as MRI, among others. In addition to enhancing health, AI promises to address one of the most pressing public policy issues of our times – the spiralling cost of healthcare. Think of advanced diagnostics enabling prevention, early detection or, looking more specifically at insurance, behavioural policy pricing as part of pay-as-you-live products which incentivise people to hopefully adopt healthier lifestyles. In order to pave and sustainably secure the way for AI its proponents, including dacadoo, need to take stakeholder concerns and sensitivities very seriously. Failing to do so could result in regulatory and legislative backlashes with the potential of derailing the triumph of AI and wrecking its many positive effects on life and health insurers.
Potential for discrimination One major stakeholder concern is related to the potential for discrimination. Of course, a powerful argument for more personalised underwriting lies in what is known in insurance as ‘adverse selection’. An insurer whose premiums are based more on a pooled rate than a personalised one will find high risks being attracted to its portfolio and low risks heading for the exit. Generally, this risk-based underwriting approach as enabled for example by the dacadoo risk engine, is seen as fair as long as the personalised rate ref lects individual behaviour. Policymakers may only
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24/5/2019 1:12:39 PM