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Strategic Technology Trends in Insurance

Tuhina Chattopadhyay
3 minutes, 47 seconds. read

“Strategic technology trend is one with substantial disruptive potential, that is beginning to break out of an emerging state into broader impact and use, or which are rapidly growing trends with a high degree of volatility reaching tipping points over the next five years”, says Gartner.

These technology trends shall enable insurers to expand into more ecosystems than ever before. Let us explore such strategic technology trends, which will impact the insurers in the near future.

1. AI & RPA helps insurance find a digital edge:

AI and RPA are already a reality for insurance. AI has found its way into vehicles, homes, and businesses and in the Insurance industry as well, it solves the necessary day-to-day tasks of running a business by the automation of routine patterns. It is able to tailor solutions for individual customers and replace the one-size-fits-all products currently available.AI in insurance will allow carriers to deliver scalable and customized solutions for members and policyholders,” says Ramon Lopez, Vice President of Property & Casualty Claims and Innovation at USAA.

RPA tools currently occupy the Peak of Inflated Expectations in the Gartner Hype Cycle for Artificial Intelligence, 2018. RPA is widely adopted in various industries, insurance included. “End-user organizations adopt RPA technology as a quick and easy fix to automate manual tasks,” said Cathy Tornbohm, vice president at Gartner. In the insurance industry automation of the day-to-day tasks would potentially reduce cost, time consumption and increase accuracy, quality and competency.

2. Augmented Analytics- future of data analytics:

One of the latest advancements for business development tools is the advent of augmented analytics. As per a report from Deloitte “Augmented analytics marks the next wave of disruption in the data analytics market”. It is an approach that automates insights using machine learning and natural language generation. Gartner predicts “by 2020, more than 40% of data science tasks will be automated”, resulting in increased productivity and broader use by data scientists. According to Accenture, “1 out of 3 insurers globally now uses Big Data from IoT technologies, such as Fitbit, Samsung Gear or Apple watch to collect lifestyle data from insureds”. Augmented Analytics will help reap business value from those data by automating Big Data insights. The insurance industry is expected to be the biggest beneficiary as it will help increase the accuracy and end the traditional “gut-feeling” decision-making approach.

3. Blockchain for war on fraud:

Blockchain is one of the biggest fourth industrial revolutions for many industries, including insurance. Insurance fraud costs more than $40 billion a year. The insurance companies can use “the distributed ledger” to potentially lower fraudulent claims, cost, transaction settlement time and improve cash flow.EY, Guardtime, A.P. Møller-Maersk, Microsoft, and ACORD collaborated and launched blockchain-powered marine hull insurance platform Insurwave in 2018. The platform is now in commercial use and handled risk for more than 1,000 commercial vessels and 500,000 automated transactions in its first twelve months of operation. More than 38 insurance companies have embarked on an initiative called the B3i to explore Blockchain applications in insurance.In the past decade, technological advances from artificial intelligence to Blockchain have transformed business models in every sector and insurance is no exception. Dubai World Insurance Congress embraced the future of the industry with insights from the sector’s most established and innovative leaders,” said Arif Amiri, Chief Executive Officer of DIFC Authority.

International Data Corporation (IDC) analysis shows “worldwide spending on Blockchain solutions could reach $11.7 Bn in 2022”. Blockchain gives the insurance company an independently verifiable data set so they don’t have to rely on the customer’s version. It is emerging as the central repository of truth for many blockchain use-cases. According to Gartner reports, “Blockchain will create $3.1T in business value by 2030”.

4. Quantum Computing:

Quantum computing is rising on the Gartner Hype Cycle. It is expected to become one of the greatest disruptions of the age. Quantum computing has the ability to process huge datasets and models that would have previously taken days and weeks. It can help calculate risks, of almost any nature, such as the impact of an approaching hurricane on a specific region.

According to a recent Novarica executive report, “Quantum Computing and Insurance: Overview and Potential Players,” by Mitch Wein and Tom Kramer offer various use cases of quantum computing. However, not many insurers are working with quantum algorithms. They are still seen as technologies that are on the distant horizon and not in their face like artificial intelligence.

The insurance industry has a complex infrastructure and legal restrictions. However, with investments in these Strategic Technology trends, insurers can become more customer-centric, achieve growth and lower cost.

https://www.futureblockchainsummit.com/news/dubai-world-insurance-congress-calls-for-faster-digitisation

https://www.gartner.com/en/newsroom/press-releases/2018-10-15-gartner-identifies-the-top-10-strategic-technology-trends-for-2019

https://www2.deloitte.com/content/dam/Deloitte/it/Documents/technology/09%20-%20Dataviz%20-%20Qlik%20proposition_Deloitte%20Italy.pdf

https://www.gartner.com/en/newsroom/press-releases/2017-01-16-gartner-says-more-than-40-percent-of-data-science-tasks-will-be-automated-by-2020

https://www.linkedin.com/pulse/case-study-insurance-industry-denis-mwarania

https://tractable.ai/blog/together-towards-ai-notes-from-insuretech-connect-2017

https://www.dig-in.com/list/top-5-insurance-quantum-computing-use-cases

https://www.cbinsights.com/research/blockchain-insurance-disruption/

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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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