Tag Archives: use cases

Pioneering Use Cases for IoT in Insurance

We are living in a hyperconnected world, and the presence of IoT devices has already been more pervasive than many of us have realized. Mobile phones in our pockets are full of sensors. Their software is updated over the air. And, when we lose them, we can remotely track their position. Meanwhile, restaurants are using simple QR-codes to comply with COVID safety measures, warehouses are employing robots to automate certain manual activities, etc. The spread of the IOT continues.

Although IoT has not yet been systematically addressed by the large majority of insurers, several early adopters have already concretely demonstrated the potential of using this technology. I have had the privilege to directly support many of these players through the activity of the IoT Insurance Observatory, an insurance think tank that has aggregated almost 60 insurers, reinsurers and tech players between North America and Europe.

Today, there are international insurance companies with millions of policies priced with telematics in their auto portfolios, millions of customers using an IoT-enabled wellbeing reward systems in their life insurance portfolios and thousands of workers protected with real-time risk mitigation solutions in their workers’ compensation portfolios. The level of maturity is higher on insurance personal lines; however, a new wave of IoT-based initiatives is occurring in commercial lines.   

These successful player journeys show IoT’s extraordinary potential to generate value for insurers, policyholders and even the entire society. Indeed, IoT allows an insurer to connect with its clients and their risks, providing benefits on four axes:

  1. Improving customer experience by enhancing proximity and frequency of interaction with them, therefore moving beyond the traditional risk transfer. Many players are selling additional services for a monthly fee; others have found new ways to sell insurance coverages thanks to IoT; 
  2. Enhancing core insurance activities (assessing, managing and transferring risks) by using IoT solutions for continuous underwriting, claims management and risk reduction. Using the insight generated by the analysis of the flow of IoT data has promoted less risky behaviors in real time; 
  3. Generating knowledge about policyholders and their risks, to insure them in a different way, to enable up- and cross-selling and to insure new risks;
  4. Providing positive externalities to society.

Unfortunately, many players in different markets have not understood the strategic nature of this innovation. They have considered IoT adoption as an IT project or the creation of a product. Instead, best practices show that IoT adoption is a strategic choice that requires a multi-year commitment to develop needed, specialized IoT competencies and leadership competencies. 

Each of the successful pioneers has designed its vision and strategy for IoT usage within its business processes.

A common mistake is to focus on the “thing,” such as a smart device. However, IoT is about data, not things. Even a focus on data is a mistake. What really matters is the usage of the data. The transformation of the business processes – through data usage – has been the secret sauce of any successful IoT insurance program. 

Some international success stories – from auto telematics to property insurance for smart commercial buildings – have already shown robust ROI. However, there is not much low-hanging fruit where a single use case generates enough value to cover all the emerging IoT costs. Typically, IoT insurance programs need deep functional competencies and a multi-functional approach to have multiple use cases that contribute to the return on the technology investment.

The opportunities for using IoT data in the insurance sector are summarized by the following framework, which has been developed within the IoT Insurance Observatory over the last five years.  

See also: 4 Connectivity Trends to Watch in 2021

Each of these use cases has been successfully implemented by tens of pioneers in different international markets and in different insurance business lines. 

These use cases don’t change the nature of the insurance business, but they allow insurers to do their job better. However, this paradigm requires moving beyond the traditional insurance economics (premiums, claim costs, administrative costs) integrating service fees, partners contributions, benefits generated by the usage of IoT data, IoT costs and value-sharing with policyholders (cashback, discounts, etc.). 

Insurance IoT is a new way of thinking about the activity of assessing, managing and transferring risks that fits with a world that is going to be more and more hyperconnected, a trend that insurers can neither stop or ignore.  

This article was originally published by Technology Magazine – IoT Edition

How Insurers Are Applying AI

AI is everywhere. Insurers are piloting various AI projects, insurance technology vendors are building it into their solutions, some insurtech startups are all AI-powered and horizontal tech vendors are creating AI platforms that sit underneath it all. Insurers that haven’t experimented with AI yet are benefiting from the technology through third-party relationships, even if they don’t realize it. 

Unfortunately, the broad scope covered by the umbrella term “AI” can cause confusion for insurers — especially because some technology providers use this label to better position their offerings in the marketplace.  

Usage of AI in the insurance world can typically be broken down into four categories:  

  • Machine Learning. The goal of machine learning, a process where an autonomous system learns from a data set to identify novel patterns, is often to refine underwriting or claims algorithms. Applications include advanced predictive modelling and analytics with unstructured data. 
  • Image Recognition. Until recently, images were a type of unstructured data better resolved by humans. Image recognition leverages AI to extract insights from digital image analyses. Applications include photo analysis and handwriting processing. 
  • Audio Recognition. AI-enhanced audio recognition captures any sound (from human speech to a car horn) and turns it into a rich, usable data source. Applications include speech recognition and non-voice audio recognition. 
  • Text Analysis. AI-powered text analysis is pulling out meaningful insights from a body of text (structured or unstructured). Applications include form reading and semantic querying. 

Justifying the Use of AI in Insurance

Novarica’s Three Levers of Value framework can help conceptualize the business value of each AI use case for insurers. Each of these levers — Sell More, Manage Risk Better and Cost Less to Operate — is applicable to a specific AI technology use case. 

Helping insurers identify upsell/cross-sell opportunities, for example, falls under sell more, while accelerating underwriting risk assessment could be categorized as managing risk better and enabling more efficient help desk support helps insurers cost less to operate. 

These are just a few examples of the value AI can bring insurers; AI use cases span categories such as product/actuarial, marketing, underwriting, customer service, billing, claims and compliance. Key use cases include: 

  • Deploying better pricing models. This machine learning use case chiefly falls in the domain of product owners and actuaries, as it applies to the area of predictive analytics. In this case, AI can help actuaries make better decisions when pricing products, thus managing risk better. 
  • Improving marketing effectiveness. This machine learning marketing use case involves using third-party or internal tools to analyze vast amounts of raw data and identify the media channels and marketing campaigns with the greatest reach and engagement levels. Here, big data analytics can help insurers sell more. 
  • Performing better property risk analysis. Using AI-powered photo analysis, underwriters can generate faster and more accurate roof damage estimates. Ultimately, this helps insurers manage risk better. 
  • Leveraging smart home assistants to deflect calls from call centers. Through a voice prompt to their smart home assistants, customers can get quotes, request policy changes and even start a home insurance claim thanks to AI-powered audio recognition. By offering another avenue to help answer customers’ FAQs, insurers free their call center employees to address more complex customer inquiries, decreasing operating costs. 
  • Increasing invoice processing speeds. Through use of text analysis and image recognition technology, AI can help billing staff eliminate error-prone human invoice handling. Using AI-powered form reading leads to greater process efficiencies, which lowers operating costs. 
  • Identifying and mitigating claims fraud. Here, machine learning can help identify potentially fraudulent claims faster. This processing speedup gives claims staff more time to focus on higher-value transactions and leads to better risk management. 
  • Enabling automatic handling of compliance requirements. Machine learning can help team members improve compliance and reporting by automatically handling complex compliance requirements. This results in lower operating costs as compliance staff can direct their attention to tasks requiring human review. 

See also: 4 Post-COVID-19 Trends for Insurers

The AI ecosystem is evolving quickly, with new technology applications emerging every day. We may soon even see further AI and ML processing speedups with the advent of quantum artificial intelligence and machine learning.  

Insurers should not invest in technology-driven projects; instead, governance should search for use-case-driven projects that most benefit the company. However, in the case of important emerging technologies — like AI and ML — it’s valuable to look for ways to deploy that technology and build up skill sets (and culture) within the organization. Additionally, many insurers have an innovation group whose sole purpose is to future-proof the organization by seeking out opportunities to deploy emerging technologies. In these cases, it’s important to refer to actual business use cases and elucidate the concrete value they provide to specific business units.

To learn more on this topic, check out Novarica’s brief, Artificial Intelligence Use Cases in Insurance.

Blockchain Adoption Starts Accelerating

Blockchain has grown to be way more than just a tech underpinning cryptocurrencies. It is opening up transformative business opportunities, even in industries that are notorious for resisting change.

And for good reason. Blockchain offers data security, reduced transaction costs, increased efficiency, trust, transparency, fraud prevention and data provenance. It’s no wonder that many businesses are already rising to the occasion with exciting use cases, even though full-scale adoption remains elusive.

Here are some of the startups spearheading the adoption of blockchain in the insurance industry:


Founded in 2015, Tradle leverages a blockchain-based framework to bridge the gap between consumers and companies. Its applications span multiple industries.

In insurance, Tradle is focused on know-your-customer (KYC) procedures to build worldwide trust and enable faster allocation and access to customer data. After the KYC data is verified on the blockchain, it would be easily accessible by other authorized companies, eliminating cumbersome data entry and verification processes.

See also: Blockchain’s Future in Insurance  


This is a platform that facilitates true peer-to-peer risk contracts to enable the affordable and efficient transfer of risks on a global scale. With the current insurance system, you have to purchase an insurance policy by sending your funds to the insurance company, which takes care of your money until you make a claim.

With RiskBazaar, however, there is no single insurance policy or agency. You send your cryptocurrency to a digital lock-up box, whose key is then assigned to multiple (two or more) people. Upon agreement, the other parties can unlock the digital box with these keys, and, if you make a valid claim, you receive the compensation from the newly unlocked box.

Essentially, anyone in the world can become an insurer, and the person can’t take off with the funds because no single person has full control over the box.


The German-based insurance company is applying the Ethereum blockchain to create insurance apps. In 2016, it demonstrated the concept with an experiment that allowed people to obtain flight delay insurance cover that pays out automatically.

See also: The Problems With Blockchain, Big Data 

SafeShare Global

This is the first company in the world to launch a blockchain-based insurance solution that satisfies the needs of a shared economy. It allows private homeowners to rent out an extra room. Through blockchain technology, the system provides a time-stamped, immutable record of insurance in real time and at significantly reduced costs.

The insurance industry is but one sector set to feel the effects of the rising blockchain technology. Take a look at the infographic below to learn about many other industries that are benefiting from its attributes.

You can find the infographic here.