Council Post: The Evolution Of Insurance In An AI-Driven World

Kannan is SVP, Global Head – Insurance and Industry Head – Financial Services at Infosys.

We are all familiar with the axiom, “time is money.” As we go into the future, it is evident that time will come to hold so much more than monetary value alone. In the business world, time is used as a currency for the exchange of essential organizational commodities: customer loyalty, intra- and inter-organizational trust, the efficiency of processes and communication and more. While time and money are both fleeting, the intellectual and social capital accrued by an organization will remain engraved in the collective memories of customers, employees, partners and leaders for a considerable length of time.

Hence, I believe cultivating a distinct brand identity with an insignia of exceptional customer service is nonnegotiable.

Insurance’s Checkered Past

The insurance industry is one whose existence depends almost entirely on the goodwill of its customer base. But for too long, the industry has been plagued by complicated claims processes, anemic claim-to-settlement ratios and a lack of trust and accessibility. Going into the future, I believe AI will change all of that.

While the importance of AI has been impressed upon the leaders of insurance firms for quite a few years now, tailored AI-applied technologies are yet to permeate into core functional areas such as underwriting, claims handling, fraud detection, customer engagement and actuarial science.

The Promise That AI Holds

The most covetable features of AI-based insurance solutions are the speed and accuracy with which they can execute time-sensitive, data-rich tasks. Combined with the sensitivity and confidentiality of the information that insurers handle on a daily basis, AI may be better equipped for keeping data breaches at bay than traditional firewalls and antiquated security systems.

Another lucrative aspect of AI adoption is the brand visibility it can fetch: Incorporating descriptive metrics about AI’s success into marketing campaigns can attract discerning customers, and something as seemingly simple as a quick and helpful AI chatbot session can help spread the word of brand’s service through the power of social media and microblogging.

The Psychology Of Customer Experience

Now, I would like to address the customer’s side of the AI equation. Apart from improving customer service, how else does one benefit from the insurance sector’s foray into AI-powered transformation?

To start with, I believe that all financial matters are deeply connected to our sense of personal worth, stability and peace of mind. When I put myself in the shoes of the typical, well-informed customer, my expectations of any brand I choose to interact with are based on my estimation of their ability to be as efficient and unintrusive as possible. While personalization in brand interactions is indeed essential, customers can end up facing many frustrations while interacting with financial services personnel. These frustrations have a disproportionately negative impact on the image of this sector on the whole—a bane that I believe can be remedied through the strategic use of AI solutions.

Changing The Insurance Narrative

Claim settlements in the present day, for example, can be inordinately tedious processes involving long hours of conversation, form-filling, data verification and multiple follow-ups. Such processes take a toll not only on the minds of customers but also on the faith they place in the insurance industry to be able to address and resolve their concerns. It can also create a power imbalance wherein a customer can feel trapped, misunderstood and at the mercy of an emotionless fiscal facade.

None of these phenomena are conducive to the integration of financial services into the lives of future citizens of the world. If AI were to take over the back end and front end of many core processes, much of this negative sentiment could be converted into positivity, trust and brand advocacy.

Here is a detailed list of insurance activities that I believe hold great potential for AI implementation. I have divided them into categories for easier identification.

Expense Ratio: Improving the efficiency of time-consuming tasks such as information gathering, risk grading and risk selection can accelerate the process of obtaining new quotes. Additionally, by improving the time allocation of underwriters, businesses can further streamline their operations and reduce the overall expense ratio.

Loss Adjustment Expenses Ratio: Extracting key claim elements automatically, such as loss description, ICD codes and claim amounts, can save time for claims adjusters when dealing with complex tasks. This can help accelerate claim settlements, improving the overall loss adjustment expenses ratio.

Loss Ratio: Several strategies can help reduce claim leakage, including improving underwriting models, standardizing payouts, early mitigation, detecting fraud patterns and policy alignment. By adopting these strategies, businesses can improve their loss ratio and better manage their overall risk.

Customer Experience: Businesses can utilize robo-advisors and virtual assistants to assist individuals in assessing their financial needs. Additionally, on-demand services and wearable technology, such as smart home devices and connected health capabilities, can improve treatment plans and return-to-work options for customers. These strategies can help improve customer satisfaction and loyalty.

Challenges In Adopting AI In Insurance

AI transformation in the industry won’t come without challenges. Below are a few hurdles to overcome before widespread adoption of AI in insurance:

Innovation Hesitation: Insurance has always lagged in technology adoption as firms want technology to mature before they can trust it with their business. AI is still maturing in some areas, and trusting it with complex insurance decisions that directly impact customers’ lives is still evolving.

Data Silos: The data exists in silos. There are multiple systems for each business function and line of business that make it that difficult to co-relate and make absolute sense of intelligence.

Regulations: Insurance is a very regulated industry, and every decision made has to be explained with proofs. While insurance AI models—especially in claims and underwriting—are evolving, the confidence levels on decisions are slowly but surely improving.

Legacy Technology: Given its age of existence, insurance still operates on legacy infrastructure and technology in many places. To make the best use of AI, technology needs to be modernized, which requires significant funding.

Privacy Concerns: Given the personally identifiable information nature of the data sets in insurance, there are challenges in creating quality training data sets in insurance, which is core in building models.

Finally, I would like to emphasize that the insurance industry, like all other areas of human endeavor, is a work in progress. Companies in this sector must consistently strive to open their channels of communication to customer input of every kind so as to champion inclusivity and empathy in financial services. This is my vision for the future of insurance.

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