Insurance the Traditional Business of Risk Transfer in the New age digital Era

Indranath Mukherjee, Head of Operations, Strategic Analysis, AXA XL, a division of AXA

AXA XL, the P&C and specialty risk division of AXA, is known for solving complex risks. For midsized companies, multinationals and even some inspirational individuals, they don’t just provide re/insurance, they reinvent it.

The Commission on Insurance Termi­nology of the American Risk and In­surance Association has defined in­surance as ‘the pooling of fortuitous losses by transfer of such risks to insurers, who agree to indemnify in­sured for such losses, to provide other pecuniary benefits on their occurrence, or to render ser­vices connected with the risk.’ In layman’s terms, that means insurance allows individuals or companies to ex­change the possibility of a large loss for the certainty of smaller periodic payments, known as premiums. The transfer is completed through a contract, known as an insurance policy, that lays out the responsibilities of both the insurer and the insured.

"The insurance industry is also experiencing a transformation driven by the AI-ML wave and the experts think that AI will drive savings for insurance carriers, brokers and policy holders in three key ways"

The premiums are much lower than the expected payout in case of a loss. Insurers are able to take on the significant risks transferred to them by the insured be­cause of two key concepts:

Pooling: This is the spreading of incurred losses of a few insured over a much larger group of insured.

The Law of Large Numbers: The more exposures an insurer underwrites, the more accurately it will be able to predict the expected number and cost of losses.

Ideally, insurable loss exposures have the following characteristics:
• A large number of similar exposure units;
• Losses that are accidental;
• Losses that are definite and measurable.

Adverse Selection: People who have the greatest probability of loss are the ones most likely to purchase insurance. Insurers are most at risk of being adversely selected against when they are attempting to gain share by lowering the price. When price competition is intense, insured who are poor risks are more able to purchase insurance at attractive rates and terms even though they have greater probabilities of loss than others

Moral Hazard: Dishonest tenden­cies in the character of the insured increase the probability of a loss oc­curring.

From the definition and the fun­damental concepts of insurance, anyone would think that every indi­vidual and business who are exposed to any kind risk that they cannot mitigate themselves would get insur­ance. Hence insurance companies may be considered as extremely de­sired business entities. The reality is rather contrasting.

Insurance is a global marketplace tends to be associated with public distrust. According to Australia’s Reader’s Digest’s ‘Most Trusted Pro­fessions 2013’ poll, insurance came in at 48th position among 50 profes­sions ranked in order of their trust­worthiness, only surpassed by politi­cians and door-to-door salespeople.

The lack of trust primarily comes from the dissatisfaction of claims servicing. No one reads their insur­ance policies and in general, people do not clearly understand the idea of the insurance coverage that is what the insurance policies provide pro­tection against. Also, the premium pricing is way too complex for any common people to understand.

This presents unique challenges to technology innovations. In recent years, with Artificial Intelligence (AI) and Machine Learning (ML) entering a new era, a wide range of applica­tions have emerged, from automated translation, autonomous cars to can­cer detection. Such overwhelming application gives a legitimate rise to hopes for the benefits of this new technology will bring not only to the business but to the larger society. The insurance industry is also expe­riencing a transformation driven by the AI-ML wave. Experts think that AI will drive savings for insurance carriers, brokers and policyholders in three key ways.

1. Behavioral Policy Pricing: Ubiquitous Internet of Things (IoT) sensors will provide personalized data to pricing platforms, allowing safer drivers to pay less for auto in­surance (known as usage-based in­surance) and people with healthier lifestyles to pay less for health insur­ance.

2. Customer Experience & Cov­erage Personalization: A I w ill e n­able a seamless automated buying experience, using chatbots that can pull on customers’ geographic and social data for personalized interac­tions. Carriers will also allow users to customize coverage for specific items and events (known as on-de­mand insurance).

3. Faster, Customized Claims Settlement: Online interfaces and virtual claims adjusters will make it more efficient to settle and pay claims following an accident, while simultaneously decreasing the likeli­hood of fraud. Customers will also be able to select whose premiums will be used to pay their claims (known as peer-to-peer insurance).

Some of the traditional insur­ance giants are trying to move into a ‘payer to partner’ kind of role and provide more support to the insureds than just settling their claims. Set­tling claims faster may satisfy some customers but it may not always be judicious for insurers to do so to re­main sustainable.

Then there are insurers who are continuously innovating. Lemonade insurance in the US, for example, created a new business model for insurance based on technology and behavioral economics. The use of AI heavily, to deliver insurance policies and handle claims. They have also introduced the social good aspect in their business model, where un­derwriting profits go to nonprofits of the insureds’ choice, in the com­pany's annual ‘Giveback’. This may help the industry to get some trust from the customers.

One thing is for sure. The sta­tus quo insurance business’ days are numbered. With more data and advancement in technology, newer types of business models like ala Lemonade insurance will emerge and only the fittest will survive.