Embracing #InsurTech : The solution for adapting to an ever changing insurance consumer

Today’s digital-first generation thrives in the online space. Insurance industry is evolving and striving to converse with these customers in a language that they prefer. Adopting a data-centric approach and offering new-age products and services form the premise for the insurance industry’s evolution.

The Shift

The driving force behind this upward trend is the changing nature of the customer. There is a distinct shift in customer expectations from a service-oriented approach to product-based approach. Till now, motor insurance discussions were mainly centred on the premium payable. Today, we see a new breed of customers that are better informed. Going ahead, we can expect more inquisitiveness about new insurance features, add-on services, and better access. The tech-savvy and digitally-oriented customers consider convenience and comfort as a priority and are pushing insurers for that ultimate experience.

Changing Trends in Motor Insurance

Technology and customer-servicing are coming together to create the desired customer experience in motor insurance. Here are some of the latest trends:

The Rise of #InsurTech

We will soon see a time where our insurer is connected technologically to our vehicles and to us through IoT, mobile devices, social networks etc. Insurance companies are enabling seamless communication right from ‘purchase’ to ‘claims’. With technological enablement, many Fintech companies are discarding the retail model and going virtual to cut costs and enhance service delivery. This way, they can then pass on this cost advantage to customers and offer better-priced insurance services.

Better Insights and Intelligent Pricing

Insurers will be able to better understand their customers through data-backed customer insights. They will apply data and analytics to accurately determine the risk and premium. Currently, this is being determined by vehicular factors like the make, year of manufacture, engine capacity, etc. Ideally, risk assessment should primarily be an outcome of factors like insurer’s age, medical condition, claim history, etc. We are gradually seeing this shift thanks to data availability.

New and Personalized Offerings

Customers are openly expressing their needs through various channels like personal interactions, social media and so forth. Insurers are using this data to better understand their customer’s needs and are thus able to personalize their services.

Better Accessibility and Connected Communication

Seamless and instant communication is a crucial part of pre-sales, sales and post-sales service. To engage customers at an initial stage, many insurers are using chatbots that ‘talk’ to the customer for pre-sales and support. Mobile apps and remote monitoring are other tools that enhance accessibility.

Flexible Financing

Insurers are implementing flexible financing, where the customers can choose from various payment models like e-wallets and digital payment methods that suit their profile. This way, customers are empowered and can pay their premiums conveniently.

The Way Ahead

With insurers turning to insuretech-based approaches to propel superior operational efficiencies and innovative offerings, ‘Faster, Cheaper and Better’ will become the norm in the near future. This will help create a smooth customer experience for the digital-natives.

About Varun Dua

Varun is the founder and CEO of Acko General Insurance and was a co-founder of Coverfox Insurance and has brought in technology practices to the Insurance industry in India. He has been featured on TechCrunch, Inc42, and Economic Times earlier and is committed to create a hassle-free experience for everyone with the help of advancements in insurance tech.

Share22

Tweet3

Share3

28 Shares

“Insurers will be able to better understand their customers through data-backed customer insights. They will apply data and analytics to accurately determine the risk and premium. Currently, this is being determined by vehicular factors like the make, year of manufacture, engine capacity, etc. Ideally, risk assessment should primarily be an outcome of factors like insurer’s age, medical condition, claim history, etc. We are gradually seeing this shift thanks to data availability.”

The “accuracy” part is the biggest question mark. Just from my own personal experience, four attempted premium hikes I’ve received have been based on faulty data or presumptions about data due to poorly designed algorithms. In two cases, they involved credit/insurance scores. In the first, they used the credit information from a family with our last name that lived 1,000 miles from us. In the second, the “reason codes” they used were bogus. In both cases, I got the premium hikes reversed because I knew what happened and how to fix it. How many other consumers would/could do that?

In the other cases, I received premium invoices based on assigning our son to the highest rated vehicle of the 3 in our household. The problem is…our son moved out 3 years ago. However, because he experienced mail problems in a large multi-occupancy building, he continues to have his vehicle registration renewal sent to our house. That information was obtained by our insurer from Lexis Nexis and the presumption was that he still lives here. He doesn’t. Our agent knows he doesn’t because they write his auto, condo, and umbrella insurance.

The problem with “big data” (and this exists beyond my small, anecdotal world) is the vetting of the data and its application. The sources and/or quality of the data is too often suspect and the algorithms used are poorly designed and/or administered. A discussion I had with a data analytics consultant convinced me that many carriers have no idea what they’re doing in developing predictive modeling and underwriting systems using “big data.”

Historical rating factors have worked fine. Insurers have been able to largely underwrite risks with them profitably for centuries. They are usually easy to verify, easy to explain to consumers, and easy to manipulate via loss control measures. However, there is a price associated with them that insurers believe is too great compared to the cost of using cheap “big data” and black box predictive modeling systems. The problem, though, is that the data is not easy to verify, not easy to explain (forget consumers…try explaining them to underwriters and agents), and not easy to manipulate so that a consumer can improve their loss experience and premium.

Insurtech — technology and data — is a tool. If you have defective tools, you’ll have defective products and unhappy customers who will move to superior products and service, even if they cost more.

Of course, I could be wrong.

1 thought on “Embracing #InsurTech : The solution for adapting to an ever changing insurance consumer”

Bill Wilson

April 12, 2018 at 12:22 pm

“Insurers will be able to better understand their customers through data-backed customer insights. They will apply data and analytics to accurately determine the risk and premium. Currently, this is being determined by vehicular factors like the make, year of manufacture, engine capacity, etc. Ideally, risk assessment should primarily be an outcome of factors like insurer’s age, medical condition, claim history, etc. We are gradually seeing this shift thanks to data availability.”

The “accuracy” part is the biggest question mark. Just from my own personal experience, four attempted premium hikes I’ve received have been based on faulty data or presumptions about data due to poorly designed algorithms. In two cases, they involved credit/insurance scores. In the first, they used the credit information from a family with our last name that lived 1,000 miles from us. In the second, the “reason codes” they used were bogus. In both cases, I got the premium hikes reversed because I knew what happened and how to fix it. How many other consumers would/could do that?

In the other cases, I received premium invoices based on assigning our son to the highest rated vehicle of the 3 in our household. The problem is…our son moved out 3 years ago. However, because he experienced mail problems in a large multi-occupancy building, he continues to have his vehicle registration renewal sent to our house. That information was obtained by our insurer from Lexis Nexis and the presumption was that he still lives here. He doesn’t. Our agent knows he doesn’t because they write his auto, condo, and umbrella insurance.

The problem with “big data” (and this exists beyond my small, anecdotal world) is the vetting of the data and its application. The sources and/or quality of the data is too often suspect and the algorithms used are poorly designed and/or administered. A discussion I had with a data analytics consultant convinced me that many carriers have no idea what they’re doing in developing predictive modeling and underwriting systems using “big data.”

Historical rating factors have worked fine. Insurers have been able to largely underwrite risks with them profitably for centuries. They are usually easy to verify, easy to explain to consumers, and easy to manipulate via loss control measures. However, there is a price associated with them that insurers believe is too great compared to the cost of using cheap “big data” and black box predictive modeling systems. The problem, though, is that the data is not easy to verify, not easy to explain (forget consumers…try explaining them to underwriters and agents), and not easy to manipulate so that a consumer can improve their loss experience and premium.

Insurtech — technology and data — is a tool. If you have defective tools, you’ll have defective products and unhappy customers who will move to superior products and service, even if they cost more.

Of course, I could be wrong.