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Four New Consumer-centric Business Models in Insurance

The insurance industry is changing and experts predict — nearly one-third of existing insurance models will disappear within this decade. The fierce competition, new opportunities with technologies like AI, and on top of that millennials’ changing preferences sum up to the call for more flexible and consumer-facing business models. Here are four new business models to set the insurance archetype.

Source: The Deloitte Global Millennial Survey 2019 

Social Good & Transparency as a Business Model

Currently, AI is being used to strengthen the capabilities and knowledge of insurers and not consumers, creating information asymmetry. But, the question is — for how long will the consumers accept being a victim of ignorance. 

A possible solution to this situation is bringing information transparency. It’s not like traditional insurers don’t share policy information with their customers. They do. However, lengthy policy documents and customers’ reliance on agents for information shadows the actual coverage, terms, etc. In a way, the information that customers receive becomes dependent on the agents’ knowledge and intentions.

Translating policy, terms and conditions documents into consumable bits of information with a clear distinction between what’s covered and what’s not will help in achieving transparency between insurers and customers.

For instance, Lemonade — the American Insurtech for renters and home insurance, disrupted the industry lately with their instant and transparent end-to-end insurance process. Their consumers are better aware of coverage and claims thanks to simplicity in the user experience. 

Moreover, Lemonade donates the unclaimed premiums to social causes their consumers care about. From its inception in 2015 to date, Lemonade has sold over 1.2 million policies, in complete transparency and all through their AI bot — Maya!

Nearly 46% of millennials are willing to make a positive impact on the society/community. Lemonade has partnered with 92 charities and has donated $8,46,849 from unclaimed premiums. Hence, the answer.

Similarly, Swedish InsurTech Hedvig has successfully deployed it’s “nice insurance” services, giving back 80% of the unclaimed premiums to charities chosen by the customers.

More insights on — millennials and their expectations from insurance ‘beyond’ convenience.

B2B2C or API-based Model

When user acquisition is the top priority, B2B2C or API-based model comes into action. Also known as an open-source platform solution, this business model connects people and processes with technology infrastructure and assets to manage user interactions. 

In the API-based model, apart from traditional distribution channels, 3rd party apps also become a medium for customers to buy/access insurance policies. Automation plays a key role in this insurance model. Here, any other customer-centric digital application can install the API without manual/human intervention.

For example, in January 2018, Allianz announced that it will offer parts of its Allianz Business System (ABS) to other insurance companies for free. Interested organizations can simply install the API (Application Programming Interface, which is nothing but a chunk of software that connects two different apps) and start selling Allianz policies to their customers.

Lemonade — after disrupting the insurance space through transparency, has now stepped into this model. In October 2017, the company launched its public API, allowing anyone to distribute Lemonade’s policies through their websites or apps.

“It takes years to pull together the licenses, capital, and technology needed to offer insurance instantly through an app, which is why it’s almost nonexistent. Today’s API launch changes that. Anyone with a slight familiarity with coding can now include these capabilities in their app, in a matter of hours.”

  • Shai Wininger, Co-founder, President & COO, Lemonade

P2P Insurance

Unclaimed premiums also contribute to conflicts between insurers and policyholders. What if a customer is not interested in donating to charity, unlike mentioned in the above case? 

Peer-to-Peer (P2P) insurance is perhaps an answer to eliminate premium settlement conflicts. It is also an emerging business model to access insurance coverage at lower costs than most of the traditional insurances. 

This insurance model pools the individuals who share at least one relation — friends, family, or interest (community/clubs) and it serves two-fold benefits-

  1. Every member knows other members, funds available, and claims initiated/processed. Therefore, irrespective of the information shared by the insurer, there’s a transparent collaboration among peers.
  2. Since the members know each other socially, there’s a negligible chance of fraudulent claims. For instance, in the US alone, insurance frauds amount to nearly $80 billion/year.

Also read – how behavioral psychology is fixing modern insurance claims

The notion of financial protection for the community has been prevalent in our societies since the 1600s. In the middle ages, the tradesmen followed the guild system (an association of craftsmen and merchants), where participants paid fees as a kind of insurance safety net. Though, the successful conceptualization of P2P insurance in the modern business models dates back to 2010 with German InsurTech — Friensurance. However, the P2P insurance model has credited the success to many more InsurTechs like Guevara, Axieme, TongJuBao (P2Pprotect), and PeerCover

Microinsurance

The greatest limiting factor for the success of microinsurance is distribution. For example, in the US, 18% of the premium represents the distribution cost, set aside marketing and advertising costs. Availability isn’t the issue for microinsurance. 

The new business model for microinsurance focuses on outreaching and distributing policies at scale. Workflow automation solutions like document processing, automated customer query resolution, etc. make microinsurance models more effective. 

  1. Aggregator model: Instead of traditional agents, retailers, utility or mobile network operators, etc. can be intermediaries for the distribution of microinsurance policies. They provide access to a very large consumer base and even more with free and freemium coverages. For example, Check24, a European aggregator together with HDI insurance developed AurumPROTECT that is available exclusively through aggregators channels. 
  2. Harnessing proxy insurance sales force: Banks have been the ideal partners to distribute microinsurance policies at scale for ages. But, for short-term policies, this is a good time to utilize the agents of other products to offer insurance as an ancillary product. For example, Ola — an Indian cab aggregator provides a number of travel-related microinsurance underwritten by Acko General Insurance. 

The Bottom Line

The effectiveness of each of these models drills down to the smart use of technology in their implementations. Moreover, most of these business models are automated, thus, eliminating additional human resources for implementations. For instance, in India, an agent can charge up to 20% of the premium amount as fees, which can reduce significantly if the distribution is automated. Investment in technology for automating operations is also worth it because it makes customer outreach simpler and faster. 

Also, read – 5 Front-office operations in Insurance you can automate with AI.

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Across the Insurance ecosystem, a special fraction within the industry is noteworthy for its adoption of new technologies ahead of others. However slow but sure, uberization of insurance has conventionally demonstrated a greater inclination towards digitization. Insurers now more than ever, need big data-driven insights to assess risk, reduce claims, and create value for their customers. 

92% of the C-Level Executives are increasing their pace of investment in big data and AI.

NewVantage Partners Executive Survey 2019 

Artificial Intelligence has brought about revolutionary benefits in the Insurance industry.

AI enriched solutions can remove the ceiling caps on collaboration, removes manual dependencies and report errors.

However, organizations today are facing a lot of challenges in reaping the actual benefits of AI.

5 Challenges for AI implementation for Insurers

5 AI Implementation Challenges in Insurance

Lack of Quality training data

AI can improve productivity and help in decision making through training datasets. According to the survey of the Dataconomy, nearly 81% of 225 data scientists found the process of AI training more difficult than expected even with the data they had. Around 76% were struggling to label and interpret the training data.

Clean vision, Process, and Support from Executive Leadership

AI is not a one time process. Maximum benefits can be reaped out of AI through clear vision, dedicated time, patience and guided leadership from industry experts and AI thought leaders.

Data in-silos

Organizational silos are ill-advised and are proven constrictive barriers to operational productivity & efficiency. Most businesses that have data kept in silos face challenges in collaboration, execution, and measurement of their bigger picture goals. 

Technology & Vendor selection

AI has grown sharp enough to penetrate through the organizations. As AI success stories are becoming numerous investment in AI is also getting higher. However big the hype is, does AI implementation suits your business process or not – is the biggest question. The insurtech industries have continued its growth trajectory in 2019; reaching a funding of $6B. With the help of these insurtech service firms, Insurance organizations have made progress, tackling the age-old insurance ills with AI-powered innovations.

People, Expertise and Technical competency

‘Skills and talent’ in the field of AI is the main barrier for AI transformation in their business.

Still playing catch-up to the US, China, and Japan — India has doubled its AI  workforce over the past few years to nearly 72,000 skilled professionals in 2019. 

Are you facing challenges with your Insurance process but have no idea where the disconnect is? Is your Insurance business process ripe for AI in the year 2020?

What is the right approach?

Join our Webinar — AI for Data-driven Insurers: Challenges, Opportunities & the Way Forward hosted by our CEO, Parag Sharma as he addresses Insurance business leaders on the 13th of February, 2020.

Register for the live webinar by Parag Sharma (AI Thought Leader & CEO Mantra Labs). 

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Ratemaking, or insurance pricing, is the process of fixing the rates or premiums that insurers charge for their policies. In insurance parlance, a unit of insurance represents a certain monetary value of coverage. Insurance companies usually base these on risk factors such as gender, age, etc. The Rate is simply the price per ‘unit of insurance’ for each unit exposed to liability. 

Typically, a unit of insurance (both in life and non-life) is equal to $1,000 worth of liability coverage. By that token, for 200 units of insurance purchased the liability coverage is $200,000. This value is the insurance ‘premium’. (This example is only to demonstrate the logic behind units of exposure, and is not an exact method for calculating premium value)

The cost of providing insurance coverage is actually unknown, which is why insurance rates are based on the predictions of future risk.  

Actuaries work wherever risk is present

Actuarial skills help measure the probability and risk of future events by understanding the past. They accomplish this by using probability theory, statistical analysis, and financial mathematics to predict future financial scenarios. 

Insurers rely on them, among other reasons, to determine the ‘gross premium’ value to collect from the customer that includes the premium amount (described earlier), a charge for covering losses and expenses (a fixture of any business) and a small margin of profit (to stay competitive). But insurers are also subject to regulations that limit how much they can actually charge customers. Being highly skilled in maths and statistics the actuary’s role is to determine the lowest possible premium that satisfies both the business and regulatory objectives.

Risk-Uncertainty Continuum

Source: Sam Gutterman, IAA Risk Book

Actuaries are essentially experts at managing risk, and owing to the fact that there are fewer actuaries in the World than most other professions — they are highly in demand. They lend their expertise to insurance, reinsurance, actuarial consultancies, investment, banking, regulatory bodies, rating agencies and government agencies. They are often attributed to the middle office, although it is not uncommon to find active roles in both the ‘front and middle’ office. 

Recently, they have also found greater roles in fast growing Internet startups and Big-Tech companies that are entering the insurance space. Take Gus Fuldner for instance, head of insurance at Uber and a highly sought after risk expert, who has a four-member actuarial team that is helping the company address new risks that are shaping their digital agenda. In fact, Uber believes in using actuaries with data science and predictive modelling skills to identify solutions for location tracking, driver monitoring, safety features, price determination, selfie-test for drivers to discourage account sharing, etc., among others.

Also read – Are Predictive Journeys moving beyond the hype?

Within the General Actuarial practice of Insurance there are 3 main disciplines — Pricing, Reserving and Capital. Pricing is prospective in nature, and it requires using statistical modelling to predict certain outcomes such as how much claims the insurer will have to pay. Reserving is perhaps more retrospective in nature, and involves applying statistical techniques for identifying how much money should be set aside for certain liabilities like claims. Capital actuaries, on the other hand, assess the valuation, solvency and future capital requirements of the insurance business.

New Product Development in Insurance

Insurance companies often respond to a growing market need or a potential technological disruptor when deciding new products/ tweaking old ones. They may be trying to address a certain business problem or planning new revenue streams for the organization. Typically, new products are built with the customer in mind. The more ‘benefit-rich’ it is, the easier it is to push on to the customer.

Normally, a group of business owners will first identify a broader business objective, let’s say — providing fire insurance protection for sub-urban, residential homeowners in North California. This may be a class of products that the insurer wants to open. In order to create this new product, they may want to study the market more carefully to understand what the risks involved are; if the product is beneficial to the target demographic, is profitable to the insurer, what is the expected value of claims, what insurance premium to collect, etc.

There are many forces external to the insurance company — economic trends, the agendas of independent agents, the activities of competitors, and the expectations and price sensitivity of the insurance market — which directly affect the premium volume and profitability of the product.

Dynamic Factors Influencing New Product Development in Insurance

Source: Deloitte Insights

To determine insurance rate levels and equitable rating plans, ratemaking becomes essential. Statistical & forecasting models are created to analyze historical premiums, claims, demographic changes, property valuations, zonal structuring, and regulatory forces. Generalized linear models, clustering, classification, and regression trees are some examples of modeling techniques used to study high volumes of past data. 

Based on these models, an actuary can predict loss ratios on a sample population that represents the insurer’s target audience. With this information, cash flows can be projected on the product. The insurance rate can also be calculated that will cover all future loss costs, contingency loads, and profits required to sustain an insurance product. Ultimately, the actuary will try to build a high level of confidence in the likelihood of a loss occurring. 

This blog is a two-part series on new product development in insurance. In the next part, we will take a more focused view of the product development actuary’s role in creating new insurance products.

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