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Top 5 Insurance Technologies & Their Use Cases in 2024

Cem Dilmegani
Updated on Jan 13
5 min read
Top 5 Insurance Technologies & Their Use Cases in 2024Top 5 Insurance Technologies & Their Use Cases in 2024

Digital transformation is on the agenda in all industries, including insurance. Using technology as a lever, insurtech companies have initiated a rapid transformation in the insurance sector. Incumbents are taking swift action to integrate this new environment. However, there are so many technology solutions and insurers’ technology investments vary.

In this article, we assess the top 5 insurance technologies and introduce their impact on core insurance practices such as underwriting, insurance pricing, claims processing, and fraud detection, for helping insurance executives to perform their digitization initiative with greater confidence.

1- Deep learning (DL)

Deep learning is a new generation of machine learning that more closely mimics the human knowledge acquisition process. Deep neural networks improve the analytical capacity of models that can benefit insurers. By using data and certain algorithms, DL detects patterns between incidents and makes effective predictions about the cost of risk.

Enhanced risk assessments

There is a large amount of structured and unstructured data in every policy that insurance companies can interpret for risk assessment. When insuring a vehicle, insurers can use data such as average driving speed, location, and age of driver to better measure risk. DL models with more computing power can calculate each parameter value more accurately compared to humans and increase underwriting and premium price setting efficiency of insurance companies. 

Improved customer satisfaction via Direct to Consumers (D2C) Insurance

Consumers demand fast service. This impacts the insurance sector by slowly eliminating intermediaries such as brokers and agents and by helping buyers purchase coverage directly from the insurance company. Improved data interpretation capabilities of DL models enable insurers to immediately assess risk and serve insurance customers with D2C insurance models. Thus, DL models ensure customer satisfaction and help to be part of the $24 billion D2C insurance market.

Lemonade, for example, used an AI model to give customers with property and casualty insurance coverage in less than 90 seconds.

2- Natural language processing (NLP)

NLP is an insurance technology that improves claims processing and customer service. NLP helps to infer the meaning of texts and respond logically to them when needed. It is a useful technique in the insurance sector, where there is a large amount of contextual data to analyze (see Figure 2).

Figure 2: Use of NLP on insurtech

Nlp is one of the insurance technologies that speed up claims processing and automate customer service.
Source Accenture

Better claims processing 

NLP directly improves claims processing, which is one of the most important insurance practices. For instance, around 85% of customers who were dissatisfied with their last claims processing tend to switch providers. NLP automates claims processing via:

  • Automating first notice of loss (FNOL): Chatbots that are the subdivision of NLP, facilitate FNOL submissions, as such tools can guide policyholders to take photos and videos of the damage.
  • Automating initial claims investigation: NLP driven Optical Character Recognition (OCR) models derive meaning from handwritten documents. Thus, insurers do not have to spend time extracting data from policy coverage, police and customer reports that contain details about the claim.
  • Automating payment arrangement: Chatbots can be deployed to inform customers regarding payment arrangement.

Metromile, an American auto insurance provider, for instance, deployed AVA, a chatbot, to handle and check claims. AVA can approve 70-80% of claims on the spot.

To learn more about which technologies improve which specific process of claims handling, you can read our 7 technologies that improve claims processing article.

Improved customer service 

Insurance chatbots also reduce the cost of customer service by automating it. Consequently, insurance companies can use their employees for tasks that are not suitable for automation yet.

3- Internet of Things (IoT)

IoT provides the data that insurers need. IBM predicts that the number of connected devices will be around 100 billion by 2025. We are already surrounded by connected smart devices such as smartphones, smartwatches, home assistants, that instantaneously provide data regarding us.

Predict probable losses more accurately 

IoT supplies data to deep learning models for more accurate predictions (e.g. in the case of underwriting).Computing power and algorithms without a large volume of data is like a car without gasoline. Thanks to surrendering smart devices insurers can benefit from new data sources for underwriting (see Table 1).

Table 1: Pre-IoT/post-IoT period data for underwriting.

Pre-IoT variablesPost-IoT variables added on Pre_IoT variables
Health Insurance
1) Age of Insured 2) Answers to the questionnaire 3) Doctor report
1) Daily exercise data 2) Sleep quality data 3) Heartbeat data
Automobile Insurance
1) Segment of car 2) Previous accidents/ Police reports 3) Driver's age
1) Number of full breakes per mile 2) Miles Driven 3) Location of driving and average speed
Commercial Property Insurance1) Answers to the questionnaire
2) Industry
1) Heat of equipments used
2) Frequency of equipments used

John Hancock has been collecting health data from customers via smartphones and wearables since 2018 and performing a data-driven risk scoring for their health insurance products.

Personalized insurance instruments 

Insurance companies no longer need to rely on segmentation (e.g. risk categories) to make risk assessment. Risk assessment can be personalized so customized services can be offered. Peer-to-peer (P2P) and pay-as-you-go (PAYG) are examples of customized insurance. 

By Miles, for example, offers pay-per-mile car insurance, with premiums decreasing based on the amount of time the vehicle is parked.

Faster claims processing 

IoT is also helping insurers with claims processing. For example, autonomous things (AuT) have recently taken over some of the claims processing. Insurers send directly smart drones equipped with computer vision technology to determine the loss. This makes claims processing faster.

Improved insurance fraud detection

IoT helps detect insurance fraud. In the past, fraudsters skewed data in their favor to collect more money from the insurance company by notifying insurers of a claim late. Today, telematics and smart devices notify insurers and other necessary contacts immediately (see Figure 3). This gives fraudsters less time to alter the data.

Figure 3: Examples of IoT devices

Image shows how IoT increases data availability for insurers.
Source: PwC

4- Blockchain

Blockchain is a virtual protocol system that stores information in such a way that it is almost impossible to change, falsify or hack the protocol. It is duplicated and shared with an entire network of devices on the blockchain, which is why it is not only secure but also transparent.

Smart Contracts

Blockchain technology is suitable to create smart contracts which,

  • simplify contract negotiations
  • automate execution of claims processing
  • reduce insurance fraud

Thus, blockchain automates claims processing (see Figure 4).

Figure 4: Smart contracts and its impact on insurance.

Image shows how blockchain technology automates claims processing.
Source: PwC

Privacy preserving

Blockchain can also support the underwriting process for insurers. Some data is difficult for insurers to find due to confidentiality requirements. For health insurers, for example, it is impossible to obtain data from doctors. However, because the Blockchain is cryptographically secured, it is possible to share such information with insurers without breaching patient confidentiality.

5- Digital twins

The digital twin is a computerized representation of any physical object such as people, houses, or cars. It helps companies organize simulations or exercises that allow them to predict the future more accurately. Thanks to such technologies, it is possible to predict the damage of situations that have not yet occurred, but might. For example, car accidents, earthquakes, etc.

Improved underwriting capabilities

Digital twins can directly improve insurers’ underwriting ability (see Figure 5). For example, due to the frequency of large earthquakes, insurers may not have sufficient knowledge about the losses from such an earthquake. Despite their low frequency, large earthquakes pose a financial threat to insurance companies because a single event triggers thousands of liabilities. Simulations can help insurers prepare for such tragic events.

Figure 5: Impact of digital twins on insurance operations

Source: Cognizant

Fraud detection

Digital twins can also be instructive in detecting fraud, as insurers can simulate accidents or events to calculate the policyholder’s loss.

Further readings

Here are five more articles we have selected about the latest trends in insurance:

If you more information regarding insurance technologies or help for finding an insurtech that can help your digital transformation we can help:

Find the Right Vendors

Source: Figure 1

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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