Insurtech in 2024: The Trends That Will Blow Your Mind and Change Your Life

Customers are changing and so are their needs. Insurers have to stay on top of their game and use the latest and greatest insurance technologies to wow clients and become more efficient.

This is Part Two of my previous article. Here are the remaining 24 in 24

14. Low-code / No-code Insurance Technology Platforms 

Only 15% of customers are satisfied with their insurer’s digital experience and 41% of customers say they are likely to switch providers due to a lack of digital capabilities. 

Traditionally, digital transformation relied on expensive IT talent to both implement and manage various digital channels. With the growth of low-code and no-code platforms, however, insurers can deploy digital applications more quickly with little or no computer programming. 

Low-code/no-code software can reduce application deployment time for insurance technology from several months to a few hours. 

In 2024, this will be more pertinent than ever as the low-code development market is projected to reach $187 Billion by 2030. Moreover, research from Appian shows IT departments across industries are losing control over their growing digital infrastructure, and project backlogs are outpacing the addition of new IT resources. Low-code/no-code gives IT breathing room to deploy their technical resources more strategically.  

This does not mean business units should deploy software solutions independently of IT. Low-code and no-code platforms may run the risk of encouraging “shadow IT” environments – that is, IT projects managed outside of the IT department. 

This could result in security and workflow issues, inconsistencies in business logic, and other unforeseen problems. Low-code/no-code solutions should be implemented following the software development lifecycle and architectural best practices in collaboration with IT. 

Gartner finds low-code development is one of the most rapidly growing trends in application development and is expected to account for over 65% of application development activity this year. 

15. Predictive Analytics for Competitive Benchmarking and Modeling 

Benchmarking has always been critical to quoting insurance policies but is only as good as the data available. In 2024, insurers and distribution partners will be able to do much more with their data using predictive analytics. 

Predictive analytics works by taking historical data and feeding it into models that are trained over time (machine learning), generating predictions about trends and behavior patterns. This enables insurance companies to make informed decisions about quoting, workload optimization, product recommendations, and more. 

According to recent data from Willis Towers Watson, 60% of insurers reported an increase in sales due to predictive analytics, and 67% reported a reduction in expenses. 

This is especially important in employee benefits sales and underwriting. 

During quoting, insurers can leverage machine learning algorithms to process historical or synthetic data to identify the most successful sold plan designs for particular group sizes and industries, speeding up the sale of a new plan. Using artificial intelligence to generate a recommended alternative quote provides a valuable benchmark based on reliable data and reduces the guesswork. 

16. Expansion of Accelerated Underwriting Programs in Life Insurance 

In traditional insurance underwriting, it was common for customers to take in-person evaluations. However, now many insurers are investing more in accelerated underwriting supported by digital self-service tools to eliminate tedious in-person medical tests. 

In its simplest terms, accelerated underwriting means that some lower-risk applicants can accelerate through the underwriting process without taking traditional tests requiring body fluid (blood, urine, etc.). In addition, because these applicants are at lower risk, they usually do not have severe health conditions that would require an insurer to seek additional requirements. 

This makes it easier and faster for customers to get life insurance coverage by skipping tedious underwriting processes. Accelerated underwriting programs can reduce policy wait times for life insurance from 27 days to just 24 hours. 

The availability of big data, growing population statistics, and limited face-to-face interactions have helped accelerated underwriting programs become most prolific in the life insurance industry, where 41% of American adults are currently considered underinsured. 

According to a LIMRA study, three out of four life insurance companies in the U.S. and Canada have automated or accelerated underwriting programs.

17. Open APIs Enable Growth of Insurance Technology 

A report by Accenture says 82% of insurance executives agree that open ecosystems allow them to grow in ways that are not otherwise possible, and 58% are actively seeking ecosystems and new business models. 

Open APIs (Application Programming Interfaces) are publicly available application programming interfaces that give other developers access to a software application or web service. They also manage how applications can communicate and interact with each other.  

Unlike an open API, A private API is an application programming interface hosted by its in-house developers. They are mainly used for back-end data and application functions. 

Open APIs allow insurance companies to showcase their services to the outside world so external partners can use them and bring added value to their customers. 

Companies interconnected through APIs can create an insurance technology ecosystem to offer a best-of-breed customer experience by intertwining digital services provided by multiple companies. Providing open APIs to different industry applications can help insurers acquire new customers. 

For example, airlines that use open APIs in their applications could partner with a travel insurer to help sell travel insurance through the airline's app. This makes it easier for customers to book a flight and buy insurance simultaneously. 

Additionally, Ben Wood, Chief Analyst at CCS Insight, predicts Apple will use APIs to enter the health insurance market in 2024 by leveraging fitness and health data gathered from millions of Apple Watch users to create personalized insurance. 

18. Insurance Technology for Proactive Risk Management 

With 52% of organizations agreeing that proactive risk mitigation is as significant as an effective risk response, insurance companies are tasked to find new ways to prevent and mitigate risks for their clients. 

Life and health insurance companies are increasing their use of AI and other predictive analytics to develop more preventative risk measures for their clients. 

For example, big data offers revolutionary insight into a customer’s lifestyle, diet, and general health. It enables insurers to understand potential risk factors better and even offer preventive and proactive recommendations, such as encouraging healthy habits to avoid future health issues. Potentially, an insurer could recommend the insured go to an emergency room because of the acute risk of a heart attack. 

Additionally, big data collected from wearable devices can provide critical health and fitness information for life and health insurers. This information is crucial to developing interactive life insurance policies that track fitness and health data on wearable devices and smartphones. 

In P&C insurance, many significant insurers leverage data collected from sensors to prevent risks.  

For example, State Farm is giving customers free Ting sensors to help prevent electrical fires. Denise Garth, Chief Strategy Officer at Majesco, believes these sensor-based policy improvements are only the beginning: 

As insurers begin to understand and monitor sensor data points in real-time, they will start putting the pieces of data together. For example, an insurer might link sensor data with sub-zero temperature data, vacationing homeowner data, and smart-home thermostats to change the circumstances of risk. 

19. Embedded Insurance 

Insurance should be uncomplicated. 

Embedded insurance will remain a significant form of digital distribution in 2024. In property and casualty alone, embedded insurance could account for over $700 Billion in Gross Written Premiums by 2030, or 25% of the total market worldwide.  

Denise Garth, Chief Strategy Officer at Majesco, says 40% of insurance will be embedded in the next 10 to 20 years. 

Even major non-insurance companies like Amazon are beginning to offer embedded insurance. Different levels of embedded insurance are already pervasive in the market.  

For instance, there are soft embedded services where customers can opt-in to purchase travel insurance; hard embedded services that come with an included warranty; or invisible embedded services, as is the case with Tesla, which offers customers Tesla’s own insurance instantly with vehicle purchase. 

For banks, car manufacturers, and other distributors, implementing embedded insurance as part of a sale can help increase revenue and improve the overall value of their products or services. This is a win-win for both insurers and distributors, as insurers can save money on distribution costs by implementing their products directly into the distributor’s platform. 

Embedded insurance technology can also help make insurance easier to understand because, with a few clicks, a customer can get coverage. No complicated process – they can get the right policy they need from day one.

20. Machine Vision in Insurance 

Machine vision refers to the AI-based analysis (machine learning) of images from sources such as smartphones, satellites, or drones. In simple terms, machine vision is the eyes of applications and machines. It uses software algorithms to assess visual images based on existing data sets already assessed by humans. 

Machine vision can help P&C insurers simplify property assessment for claims processing. Traditionally, a claim adjuster would go on-site and assess the situation. By using drones programmed with machine vision, this process becomes more simple and safer, as the drone can use machine vision to obtain images and create 2D and 3D models for claims assessments. 

In auto insurance, machine vision can help improve the speed and accuracy of damage assessment and claims evaluation. For example, when a customer damages their vehicle, they can simply send a picture of the damaged area to their auto insurer, and the AI’s machine vision will analyze the images to determine the damage and claim amounts. 

In group insurance, machine vision can greatly streamline the quoting process. Many requests for proposals still come in as images and PDF documents that cannot be interpreted as text by a typical computer. Moreover, client information cannot be copied and pasted from this format into the quoting tool, requiring manual rekeying of information by a human underwriter whose time is better spent elsewhere. 

This is where a machine vision technique called optical character recognition (OCR) comes in. OCR is the conversion of images to text (e.g., a photo of an RFP) into a machine-readable format. This enables insurers and distribution partners to generate a shell quote with information pulled from the RFP and begin working on a quote immediately. 

21. Health Wearables 

The demand for health wearables is booming as advanced insurance technology allows people to monitor their health progress and get rewards for healthy living. 

These services track a wealth of data, such as daily steps, sleeping patterns, activity levels, heart rates, calories consumed, UV levels, temperature preferences, when people are home and not, distance traveled in cars, etc. 

Many Gen Zs and Millennials are comfortable exchanging personal health data (i.e., step counts, sleep data, etc.) from their Fitbits or Apple Watches for discounts and personalized policies. Additionally, the growth of wearables will continue over the next few years, with shipments expected to reach almost 380 million devices in 2025. 

Indeed, 95% of underwriters want to improve the quality and accuracy of data around underwriting submissions. 

Data collected from wearables can provide critical health and fitness information. This information is vital to developing interactive life insurance policies that track fitness and health data through wearable devices and smartphones. In addition, the data gathered can give complimentary coverage or improved rates for both individual and employee benefits using health and risk scores.  

Wearables can also help insurers mitigate claims fraud and, more importantly, enable them to transmit data to warn customers of possible dangers in real-time. For instance, some IoT wearables can proactively alert people with diabetes on possible odd joint angles, foot ulcers, and excessive pressure so they can get treatment before things get worse. 

Life insurance policyholders pay their premiums on average for 20 years. However, with the adoption and use of fitness trackers, they may be able to live healthier and longer lives. Lower mortality and morbidity can help insurers boost profits while improving insured health and wellness with predictive care and early diagnosis. 

22. Automated Renewal 

Automated renewal capabilities provide insurers with an opportunity to help existing clients renew their policies faster than ever before. 

A report by Deloitte finds AI is estimated to increase labour productivity by about 37% by 2025 by eliminating or minimizing more manual tasks and freeing up current workers to add more value. 

Automated renewal applications can limit the need for carrier intervention for stock quotes, automatically queuing quotes for manual review, and auto-generating policy renewal packages. 

Additionally, automated renewal applications can connect with policy administration and claims systems by leveraging data for re-calculations at the anniversary of a policy’s renewal. This allows insurers to not worry about tracking renewals and the manual preparation of renewal quotes and letters.  

This has proven to be especially beneficial for employee benefits insurers as they can reduce renewal turnaround and touchpoints by 75% with automated renewal. 

23. Automated Workload Balancing for Quotes 

During high-load periods like open enrollment for employee benefits, the high volume of quotes requiring underwriter review can slow down processes due to an inefficient allocation of human resources. 30-40% of an underwriter’s time is spent on administrative tasks, such as rekeying data or manually executing analyses. 

With AI, workload recommendations can now be generated automatically. Carriers can train machine-learning models to assist sales and underwriting managers in suggesting the most effective distribution of quotes across the underwriting team. 

AI can take an individual underwriter’s current capacity and performance history into account when making recommendations. Additionally, it can be used to prioritize quotes with the highest chance of closing based on past successes.  

Identifying these resource efficiencies with AI is essential to improving sales and underwriting productivity. 49% of insurance executives say AI has helped them operate more efficiently, and 35% say it has helped them increase revenues. 

24. Digital Twins 

A “digital twin” refers to a virtual portrayal of a physical object or process situated within a digital emulation of its surroundings. Digital twins can help insurers across many lines of business simulate practical situations and their results, allowing them to make better decisions.  

For example, a digital twin can create a digital replica of people, houses, cars, entire cities, business operations, and healthcare procedures, to name a few. 

Digital twins integrate big data from the Internet of Things, artificial intelligence, and software analytics to enhance the output. 

As a result, digital twins are becoming a staple in the P&C and auto insurance industries to derive virtual data and evaluate and predict risk scenarios before they happen. In fact, most insurance companies are missing out on anywhere between 35% to 65% in value by not realizing the full potential of digital twin investments. 

Digital twins can help insurers expand their datasets for everyday risks such as car accidents, heart attacks, and catastrophic event damages before they happen. As a result, digital twins can improve risk assessment and underwriting accuracy. 

For example, P&C insurers could create a digital twin of a prospect’s home and enable simulations to see how different categories of hurricanes would affect it. Then, based on the simulations, the insurers could identify and evaluate risks for potential losses before they happen, therefore enabling them to price premiums more accurately.. 

Digital twin technology can also create a replica of an insurance company’s organization by simulating an insurer’s firm’s activities and day-to-day tasks. This can help insurers evaluate their operations and provide insights into which processes they can automate or be more efficient in. 

Future Thoughts 

Trends and new technologies are emerging constantly, Insurers will keep things exciting by deploying some of the most inventive solutions in what was a very conservative industry. Insurtech will help insurers improve their products, services, and operations in many ways.

All the best in 2024!