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10 Most Impactful AI-based Insurance Innovations of 2019

Nidhi Agrawal
5 minutes, 5 seconds read

The year 2019 has been a benchmark in insurance innovations that brought in new value propositions to the industry. What’s more remarkable is — both traditional Insurers and Insurtechs are striving to offer simple, convenient, and value-added customer-centric products coupled with technology initiatives. Here are 10 noteworthy insurance innovations that shaped the industry this year.

  1. Augmented Intelligence
  2. AI-based Smart Automation
  3. Digital Insurance Broker
  4. Services Beyond Insurance
  5. Blockchain in Reinsurance
  6. Unconventional Partnerships
  7. Understanding Customers and Delivering Tailored Products
  8. Insurance on Demand Services
  9. Risk Intelligence
  10. Customer Education

10 Most Impactful Insurance Innovations of 2019

According to a recent EFMA-Accenture report, the insurance industry has witnessed growth in digital sales & services, Artificial Intelligence trends — especially machine learning and natural language processing (nlp), big data and analytics, cloud, intelligent automation, and blockchain.

However, insurance players are not just adding convenience through technology but also understanding the ‘actual’ customer needs and developing the products accordingly. Let’s discuss the impactful insurance innovations with their use cases in detail.

#1 Augmented Intelligence

While most insurers are leveraging AI to understand customers and their requirements; another idea that hits the list is to complement the knowledge of insurance employees during sales pitches and customer services. 

For example, Zelros is Augmenting intelligence of sales and customer representatives through real-time best product recommendations, advisory, and pricing based on studying the customer profile.

Zelros - augmented intelligence - insurance innovations

Similarly, Nippon Life Insurance Company has introduced an AI-powered TASKALL tablet for its sales representatives. This tablet identifies suitable prospects from the set of entire salesforce activities, thus enhancing the sales and customer representatives’ services. 

#2 AI-based Smart Automation

Smart automation corresponds to deploying intelligent technologies to gain massive operational efficiency and at the same time create value for the end customer. 

For example, South Korean Kyobo Life Insurance Co. Ltd. has developed an AI system BARO (Best Analysis & Rapid Outcome) to automate underwriting. The system uses NLP to allow sales and customer interactions in natural language.

In the same way, Religare incorporated AI-based chatbot in their workflow. Through this bot, the company has automated a number of operations like customer query resolution, customer engagement, and lead and ticket management.

#3 The Digital Insurance Broker

In 2018, in the US alone, nearly 1.2 million people worked for insurance agencies, brokers, and insurance-related enterprises. This indicates the prominence of the brokerage in insurance. Brokers might not be directly involved in product development, risk evaluation, etc.; but they play a pivotal role in insurance distribution. 

For example, Gramcover, an Indian composite insurance broking firm is leveraging mobile technologies to minimize the inefficiencies and transaction costs in distributing micro-policies.

Also read – The case for a digital brokerage

#4 Beyond Insurance

The year 2019 also witnessed the entry of technology giants like Alibaba entering the insurance space, and people welcoming them made the competition even more fierce. The World Insurtech Report 2019 states that nearly 30% of customers are interested in buying at least one insurance product from BigTech firms like Google, Apple, Facebook, Amazon, and Alibaba. 

Insurers have thus realized to embrace the ecosystem-based digital economy to deliver richer customer experiences. AG Insurance’s Phil at Home is an example of ‘beyond’ insurance services to support customers in their day to day life. The app provides house maintenance services like plumbing, electricity, etc. along with medication reminders, food delivery, etc. to its elderly customers.

Also read – The Belgian Insurance Landscape

#5 Blockchain in Reinsurance

Blockchain or distributed ledger technology (DLT) brings transparency to a range of insurance processes along with the secure sharing of information. The innovative use of blockchain in insurance is to reduce redundant efforts. 

For example, the US-based Aon Benfield along with partners have developed a blockchain-powered reinsurance placement solution to bring brokers and reinsurers on a collaborative platform.

Similarly, the Hong Kong Federation of Insurers in collaboration with CryptoBLK developed MIDAS (Motor Insurance DLT-based Authentication System) to authenticate motor insurance policy documents across the network in real-time.

#6 Unconventional Partnerships

Insurers’ partnerships with Insurtechs, Fintechs, and external players are presenting an opportunity to explore new customer base, test different business models, and get access to new technology frontiers. 

For example, AXA partnered with ContGuard, which provides real-time cargo tracking services. Their product — Connected Cargo Solution gives customers 24/7 monitoring and data to AXA’s risk engineers to develop loss prevention plans. This also helps underwriters to quote the price with increased accuracy.

#7 Understanding Customers and Delivering Tailored Products

Addressing the customers’ demand for personalized services, Insurers have started applying AI to understand their sentiments and requirements. They have realized that real-time digital services unlock values for both carriers and customers.

For example, the UK-based Bought By Many helps people find insurance for uncommon assets like pets, shoes, gadgets, etc. The company also negotiates with insurers for the best deals.

#8 On-demand Insurance models

The World Insurtech report 2019 reveals that nearly 41% of customers are ready to consider usage-based insurance and 37% want to explore on-demand insurance coverage. While usage-based insurance models provide as-you-go premium coverage based on customer’s potential for risky behavior; on-demand insurance allows customers to get cost-effective and convenient coverage depending on their needs.

For example, The Dinghy is an app-based on-demand freelancer insurer. It is also the world’s first on-demand professional indemnity insurance covering public liability, business equipment, legal expenses, and cyber liability.

#9 Risk Intelligence

Insurers are deploying machine learning models for risk assessment and mitigation. It not only makes the underwriting more accurate but also boosts profits by diminishing risks.

For example, ZestFinance uses automated machine learning tools to correlate current and traditional data. It helps to effectively gauge risks and outreach potential new customers.

#10 Customer Education

Pricing still presents a bigger competitive advantage than many other insurance features. Accenture’s 2019 Global Financial Services Consumer Study states – more than 75% of customers can share their personal information for better prices. 

Therefore, educating customers about potential risks isn’t sufficient. Coupling this information with available products’ prices and benefits is a must. For example, Jerry, a California-based personal insurance marketplace checks if the user is paying the best price for the insurance services. Based on an initial questionnaire, their AI-powered tools takes roughly 45 seconds to compare quotes from leading insurers and suggest optimum rate to the user.

Also read “Top 5 smartest AI-powered machines on earth.”

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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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