InsurTech Guide

InsurTech Growth Insights

• Internet of Things (IoT): IoT devices are connected physical computing devices, which collect data that can be used for risk analysis, e.g. automobile trackers to predict safety and the potential for an accident based on speed, braking pattern, and GPS location.
• Mobile Applications: On smartphones, insurance apps can streamline the process of customers finding the right policy for their needs, getting questions answered promptly, filing claims, and checking claim statuses with more communication touchpoints.
• Virtual Claim Filing & Processing: Policyholders can submit claims online or through a mobile app, which can create a simpler, digital experience, e.g. taking a picture of the insured belongings or damage is more convenient than scheduling an in-person visit with an insurance representative to file a claim or receiving a third party appraisal.
• Artificial Intelligence (AI): AI automation tools can perform human functions with greater efficiency and accuracy, e.g. an AI-powered chatbot could help a user navigate a site in real-time and answer common product questions 24/7.
• Machine Learning (ML): ML enables insurance companies to extract insights from the vast amount of data collected to predict future losses and demand modeling to estimate customer premiums (e.g. predictive analytics tools such as smart sensors).
• Natural Language Processing (NLP): Chatbots and other uses of conversational AI could benefit insurers by reducing the costs of employing customer representatives and automating the customer service process.
• Big Data / Data Analytics: With data analytics, more insights can be obtained regarding the needs of their customers to offer more customized products/services.
• Know-Your-Customer (KYC): KYC is the process of customer identification and verifying identities to prevent fraud, which InsurTech can facilitate using software with stored customer identification records and customer record management databases.
• Facial Recognition Software: Consumer AI-powered facial recognition software can be embedded within the claims portal to verify the identity of the individual submitting a claim, reducing the time needed to process the claim and issue payment.
• Fraud Detection Risk: Fraudulent claims have long been a risk to insurance companies, but through InsurTech, companies can more accurately detect and avoid incurring losses related to fraud (e.g. authentication/verification process, duplicate transactions, public records).
• Geospatial Analytics: Satellite images and GPS analytics can support underwriting, evaluating claims, pricing insurance policies, and managing risk.
• Peer-to-Peer Insurance (P2P): P2P insurance is still a newer product segment, in which policyholders can pick an insurance pool to share premiums (and risks), with leftover premiums refunded to the policyholders.
• Drone Technology: Inspections performed using drones can be used by insurers to determine the extent of damage to an asset/property and assess the risk surrounding a particular area.

Personalized Insurance Policies (IoT, ML)

Customer-centricity has become a central point of InsurTech, and nowadays, consumers are well-versed in technology and expect insurance products to be on par with their other products, such as digital banking.

Since simplicity and transparency have become the norm, recent advancements have targeted these traditionally weak areas in the insurance industry.

Historically, premiums for insurance were set based on a limited number of data points, such as the type of policy sought, age of the policyholder, and criminal history records.

Using just a couple of pieces of information, an actuary or statistician attempts to determine the probability of an individual filing a certain claim.

But developments in machine learning and IoT devices have made gathering comprehensive data sets possible and more easily— so insurance companies can utilize the better, more robust data to personalize premiums.

1. IoT Devices: IoT devices such as telematics devices in automobiles and wearable consumer technology can collect personal data to build a more comprehensive customer profile.
2. Machine Learning Models (ML): Predictive models based on machine learning applications can digest large data sets to develop more accurate premiums based on the insights obtained.

By delivering personalized insurance policies, establishing customer cohorts based on shared data points, and increased customer engagement, there are more opportunities for upselling, cross-selling, and improving customer retention rates.

Claims Processing & Management

Claims processing and management is another segment with significant interest from startups, as the current method of handling receives constant criticism for the lack of transparency and slow communication.

Digital claims processing applications can fix these complaints, aided by AI-powered software applications that can automate certain portions of the process.

These applications often take the form of an online form and chatbot that offer support in real-time as policyholders submit a claim.

1. The internal software and chatbot verifies the policy details and gathers all the necessary information.
2. The chatbot ensures the claim passes the fraud detection algorithm.
3. If so, the bank is automatically contacted with instructions on sending across the correct reimbursement amount owed.

With a very minimal delay after filing, typically under a minute, the claim processing algorithms can sort through the claim and process it, all while scanning for signs of potentially fraudulent behavior.

InsurTech vs Incumbents – New Insurance Business Model

Still, despite the wide array of benefits and value-add products, there appears to be a disconnect between the growth in funding and the pace of adoption from incumbents.

In general, the legacy insurance industry has been dismissive of capitalizing on and leveraging new technologies.

Even though the insurance industry seems like a sector ripe for disruption, adoption has been rather disappointing as legacy insurance incumbents continue to be criticized for their reluctance to adopt new digital products/services.

But with regards to the value proposition, InsurTech has the potential to enable certain insurance providers to become more efficient in underwriting, processing claims with automated technology, and managing risk (e.g. fraud detection).

^{InsurTech vs Incumbents (Source: McKinsey)}

InsurTech Market Risks

The regulatory landscape has been (and to this date, continues to be) the major hurdle for insurance companies to embrace change.

On top of the compliance spending, insurance regulations often disincentivize upgrading to new technologies, i.e. regulations are in place to protect consumers from predatory pricing models that effectively make upgrading difficult.

For example, auto insurance is a heavily regulated industry in which providers must spend a significant amount on maintenance to ensure compliance with frequently changing standards.

Aside from the unfavorable regulatory structure, the reluctance of incumbents to integrate newer offerings is another headwind, much like in the healthcare industry.

Why? The insurance industry – again, with many parallels to healthcare – has gained a reputation for being risk-averse and cautious when it comes to spending, which is likely due to the low margins in the industry.

InsurTech startups are essentially starting from nothing and building bottoms-up using up-to-date technology, whereas existing incumbents must completely overhaul an outdated system developed internally for decades.

Incumbent’s Dilemma is Our Opportunity

“It is difficult for incumbents, with massive legacy businesses to protect, to wholeheartedly adopt new technologies that call for a 30% rate decrease for two-thirds of their customers

That may explain why 96% of incumbent policies use no telematics data, while the 4% that do, tend to turn it off after two weeks, and to underweight its signals.

Innovators, legacy-free and built from scratch in the 21st century, are uniquely positioned to lead the industry’s graduation from pricing based on proxies, to pricing based on continuous data streams.”

– Lemonade Shareholder Presentation (Source: Q3-2021 IR Deck)

InsurTech IPO, SPAC, and M&A Trends

Since going public via IPO or a SPAC merger, many leading InsurTech companies have seen their share prices plummet since the start of 2020.

With that said, the sharply declining valuations of public InsurTech companies have led many to predict M&A activity will soon pick up, given the plunge in share prices.

Latest Closing Date: 2/14/2022

In the coming years, the following patterns seem likely to emerge:

• Horizontal Integration: A wave of consolidation among InsurTech companies to improve their collective offerings, as well as benefit from cost synergies (e.g. eliminate duplicate functions)
• Vertical Integration: InsurTech companies focused on a specific industry niche could pursue acquiring (or merging) with adjacent solution providers to become more marketable and readily implemented by their target market.
• Technology-Driven M&A: Legacy insurance providers and carriers could soon begin acquiring InsurTech companies to improve their overall capabilities and plug gaps in their existing technical capabilities, especially given the collapsed valuations of InsurTech companies.
• Digitization: In the InsurTech industry, digitization should continue to be one of the main rationales for M&A, driven by the normalization of the remote workforce.
• Niche Providers: InsurTech providers specifically targeting underserved markets are expected to emerge – for example, small and medium-sized enterprises (SMEs) have historically been a neglected part of the market for insurance providers due to the lack of profit potential that had led to fewer policy offerings being available for small businesses, limiting their options in finding a suitable policy.

Lemonade & Metromile Example

Notably, Lemonade (NYSE: LMND) offers insurance to renters and homeowners by using artificial intelligence (AI) and chatbots.

Lemonade views itself as a disruptor leading the modern insurance business model because of two key factors:

• AI Premium Pricing: Lemonade utilizes AI to price premiums, where behavioral models and sophisticated algorithms ensure pricing is customized for customers with industry-leading precision and speed (and claims customers can get insured within 60 seconds).
• Simple Digital User Platform: The simplicity of Lemonade’s user interface and marketing attracts a market of consumers new to the insurance market, i.e. the CEO has stated that 90% of its customer base are first-time, younger purchasers of insurance products.

After a promising IPO in 2020, Lemonade’s shares soared approximately 139% on the first day of trading, closing at $69.41 per share.

Lemonade’s shares later went on to peak at an all-time high of around $188 per share.

Despite trading multiples times its IPO issuance price, Lemonade’s shares have since declined to their IPO level at $29.07 in early 2022.

^{Lemonade Historical Market Capitalization (Source: CapIQ)}

In November of 2021, Metromile, a pay-per-mile car insurance company, announced Lemonade would acquire it in an all-stock transaction, which is expected to close in Q2-2022.

Lemonade and Metromile are down by more than 80% and 90% from their all-time highs, respectively.

The acquisition of Metromile signals a steep write-down in the valuation, as the implied fully diluted equity value is approximately $500 million, or $200 million net of cash on the balance sheet.

Therefore, certain InsurTech companies startups might opt to sell their companies to a strategic rather than attempt to go public – or wait for the volatility to pass and share prices to recover to prior levels.