Middle East Insurance Review - AI in insurance

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

Tech.indd 57

24/5/2019 1:12:39 PM

58 Middle East Insurance Review June 2019

Insights – Technology tolerate higher prices for certain groups of people if the underlying technology contributes to mitigating moral hazard, ie, makes people behave more prudently and consciously. In order to do so, correlations established by algorithms must make policyholders’ risk behaviour transparent. In addition, this behaviour must be controllable. Otherwise, regulators may clamp down on price discrimination which they consider unfair.

Another obvious ethical concern arises from machinebased profiling, ie, being judged a higher or lower insurance risk based on demographic characteristics. AI can dissect risk into hundreds of factors which algorithms scan to identify clusters of previously unrecognised correlations. The results, however, may unintentionally discriminate and amplify stereotypes.

The (data) power shift Genome sequencing arguably poses the most complex set of challenges involving the ethics of data use and disclosure. Since the health record of an individual is largely determined by his or her genetics and lifestyle, there has always been a massive interest in thoroughly understanding humans’ genetic makeup. Progress remained subdued, however, due to the complexity and enormity of the data that had to be evaluated.

AI and machine learning applications have enabled quantum leaps in terms of interpreting and acting on genomic data as well as in reducing the cost of genome sequencing which has fallen to a small fraction of what it used to be at the beginning of the 21st century. As a result, the number of genetic tests has sky-rocketed, shifting the balance of data power towards the individual.

For insurers, this shift is presenting major challenges.

Insurance is primarily based upon the principle of pooling homogenous risks. When a potential policyholder has information about his or her health that is not shared with the insurer, anti-selection could be inevitable, calling into question the viability of the insurance offering. On the other hand, if insurers were entitled to request information from previous genetic tests, an entirely different dynamic could unfold. Some argue that, as a result, people may postpone or even cancel a planned genetic test that might be vital to their well-being.

The challenge of anti-selection versus the potential denial of insurance is most acute in life, disability, critical illness and long-term care insurance. It is less relevant in health insurance where government schemes or compulsory insurance requirements are the norm, at least in advanced economies.

Toeing the regulatory line Existing regulations concerning the use of genetic information generally apply to employment and health insurance. For life, disability, critical illness and longterm care insurance, regulations differ widely, ranging from no regulation whatsoever to prohibitions on using results from existing tests. Therefore, in certain regulatory environments, the insurer is at a severe informational disadvantage, with potentially adverse effects on its financial viability. dacadoo’s health score,

Tech.indd 58

in combination with its imputation engine, helps mitigate this problem which could lead to massive disruptions in insurance markets. The health score enables a win-win-approach for both the insurer and the policyholder, with fairer pricing for both sides while protecting the consumer’s personal data.

Data processing is at the very heart of the insurance business model. As a result, data protection and usage legislation is of utmost importance to insurers – and it is becoming more relevant in light of a heightened regulatory focus on how businesses gather and handle consumer data.

In the EU, for example, a comprehensive legislative reform in the form of the General Data Protection Regulation (GDPR) has been enacted, largely driven by the widespread public perception that more needed to be done to mitigate the significant personal data risks associated with online activity. Big data and the role of machinebased underwriting are among the most frequently discussed topics. An increasing number of non-European jurisdictions have started studying GDPR as a potential blueprint. Given the prospect of GDPR going global and growing public sensitivities also outside of Europe, insurers need to make data protection a priority in terms of customer education and communication.

Chance to thrive In summary, AI holds the promise of revolutionising the way insurers manage and transfer risk. However, as with any powerful new technology there are challenges which need to be reconciled with the opportunities. Put crudely, insurers, in collaboration with their stakeholders, have to ‘manage the risk of managing risk’ in a future that might be reshaped by the predictive capabilities of AI. First and foremost, insurers have to convince customers that the upside of AI outweighs the downside by far.

This is primarily about transparency and diligence in terms of capturing, processing and using personal data, as well as building and overseeing algorithms – and it should go beyond mere compliance with GDPR and other legislative requirements. At the same time, AI must be shown to make a measurable contribution to an enhanced customer health and overall positive lifestyle experience, less onerous claims procedures and lower premiums that reward individual behaviour.

Once the ethical and moral hurdles described in this article have been cleared, AI solutions, on a sustainable basis and with a ‘licence to operate’ from the public, will deliver game-changing breakthroughs (eg, in diagnostics) and improvements in quality-of-life and lifestyle navigation. Ultimately, AI will be instrumental in making insurance more relevant, appealing and affordable to customers. However, just like in mobile communication where governments defined the GSM standard which enabled a global explosion in mobile communication, we need the right ethical framework for AI to prosper in the insurance industry and the digital societies of the future at large.

Mr Peter Ohnemus is the founder and CEO of dacadoo, a Zurich-based global technology company driving digital transformation in healthcare.

23/5/2019 5:34:31 PM