Crypto Market Commentary 

14 October 2019

Doc's Daily Commentary

The 10/9 ReadySetLive Update with Doc & Mav is listed below.

Mind Of Mav

Big FinTech Is On Fire – Part Two – Insurance

Last week we talked about how global FinTech is on fire. A big piece of that thesis was how countries were adopting and accepting fintech at an increasingly rapid pace.

Today, I want to talk about as aspect of future FinTech.

Specifically, I want to talk about how Fintech is changing the face of the most boring industry on the planet – insurance.

Data management/data integrity

While not unique to the insurance industry, data integrity is, nevertheless, an important enabler; a base layer so to speak, on which to build innovative insurance solutions. With all the talk about exabytes of data from which to extract insights, insurance companies, as well as other financial services firms, are still struggling to keep up with their current flow of data. It is especially true in larger organizations that have gone through multiple acquisitions. A parent company and each acquired company runs its own data management software. The result is siloed pools of data, integration patches that do not work well, manual data cleanups, etc. This causes poor visibility into business operations, operational inefficiencies and lost market opportunities. Thus, solving the data integrity issues, unifying and cleaning data is an important first step. Tamr is one example of the companies that address this in the financial services sector.

Up-to-date digitized and DLT-enabled title registry is another opportunity where material operational efficiencies can be realized. Goldman Sachs estimated that digital ledger technologies could lead to annual savings of $2–4 billion in the real estate title insurance market by streamlining transactions. In addition to powering immutable record of property ownership, DLTs could power smart workflows and smart escrow to record transaction progress and make payments. Palo Alto-based Propy, for instance, developed a real estate transacting platform with DLT-enabled title registry. The platform allows buyers, sellers, and agents to sign offers, make payments, and record ownership data.

Sales/Customer engagement

Mobile internet is changing the way insurance carriers interact with their customers. A customer can compare personal insurance quotes, file a claim, monitor claim status, and make payments on mobile devices. Both large insurance carriers and startups are working on new ways to engage with customers. MIT-spun Insurify, uses predictive analytics to help customers shop for auto insurance, compare quotes, etc. The company raised in total $6.6M in VC funding and secured partnerships with major insurance carriers.

Commercial insurance lines, however, are lagging in this area with the still prevalent old-fashioned time-consuming process of calling agents, comparing quotes, and making a policy selection. Commercial insurance is a much more complicated product, and the complexity of information could be overwhelming for some business owners. Coverwallet is streamlining this process with a user-friendly interface and an A.I.-powered agent that helps business owners make commercial insurance buying decisions. The New York based company raised over $30M from USV, Index, Aon, Zurich Insurance, and others.

Improving the underwriting process

In underwriting, A.I. can be used to extract insights from various data sources, collected via IoT and mobile devices, and update analysis instantaneously to improve risk evaluation and pricing based on a specific risk profile. This reduces the underwriting time and enables insurance companies to offer more customized and more accurately priced policies.

Boston-based Corvus Insurance has developed a commercial insurance platform to improve risk selection and risk management. The platform uses IoT and customer specific data to develop scoring, inform underwriting decisions, and pricing. The offering benefits the company and the insurance buyers, allowing commercial insurance brokers and their clients to better predict and prevent losses. The company raised a total of $14M across several rounds from Bain Capital Ventures, .406 Ventures, and others.

Cape Analytics, based in SF Bay, provides an on-demand data stream of property features via a cloud-based real-time property data feed platform. The company’s platform leverages geospatial imagery, computer vision and machine learning to extract proprietary data, enabling insurance carriers to access property data for more accurate initial underwriting decisions. In 2018, the company raised $17M series B led by XL Innovate with participation from Lux Capital, Khosla Ventures, and others.

A Colorado-based company, Flyreel, developed an AI-enabled underwriting system to help users make smarter, data-based underwriting decisions. The company’s platform uses an AI-assistant, computer vision, and detailed property reports, enabling property owners to improve underwriting efficiency. Using the company’s app, a user scans a property using a mobile device, and the company’s computer vision algorithms automatically identify items relevant to the policy. In effect, this technology eliminates the need for professional inspections. The company recently raised $3.85M led by Gradient Ventures.

Image result for fintech insurance

Reducing fraudulent claims losses and improving claim settlement efficiency

Insurance Information Institute estimates that annual losses attributable to insurance fraud amount to $100-$300B. Property and casualty insurance fraud accounts for $30–40 Billion of the total while healthcare related fraud accounts for the rest.

Insurance companies’ Special Investigation Unit (SIU) professionals review potentially fraudulent claims. Tens of thousands of claims are filed annually and thousands are flagged as being potentially fraudulent. However, due to resource and time constraints, only a small fraction of those claims is reviewed by SIU professionals. Thus, thousands of fraudulent claims still end up being processed and paid, resulting in increased liabilities and expenses for insurance companies and increased premiums for policyholders.

Streamlining the SIU review process using A.I. will improve the accuracy of fraud detection as well as reduce the time to settle a claim. San Francisco based (YC2018), developed a fraud documents detection platform that automates the process of identifying fraudulent claims. The platform uses a combination of NLP and computer vision to scan documents sent. In December 2018, The company raised $3M seed round from Crosslink Capital, SV Angels, and others.

DLTs could help reduce fraud by providing an immutable record of transactional data. Going back to property title example, losses due to title fraud in 2015 in the U.S. totaled more than $5 billion with average losses per incident exceeding $100,000. DLT-enabled title registry could reduce such fraud dramatically, in addition to providing transaction efficiency discussed earlier.

For claims that go through subrogation process, DLTs can help speed up claim settlement and reduce costs. Subrogation is a process by which insurance companies settle claim losses among each other. This is a relatively complex process which requires information exchange among the insurance companies and causes delays in claims settlements and payments. A permissioned DLT platform powered by smart contracts could significantly speed up the process with automatic payment disbursement as soon as liability determination is completed


Reducing frequency, severity, and LAE

For those unfamiliar with the terminology, claim frequency is simply a number of accidents while severity refers to losses per claim. LAE, or loss adjustment expense, is the cost an insurance company incurs when processing a claim. LAE could include such expenses as claim adjuster and car rental expenses, among others.

Severity per auto accident involving only property damage averages about $3,000 per claim, but it has been on the rise due to increasing auto values. 15% of accidents involve bodily injury (BI). Bodily injury increases the severity per claim by $25,000 — $50,000 (and more) and increases the loss adjustment expenses from the average per claim of $300 for accidents involving property damage only to $3,000-$5,000 for those accidents involving BI. With thousands of accidents happening every year, these add up to hundreds of millions of dollars in losses and expenses.

Image result for fintech insurance

Proliferation of low cost IoT devices and sensors and ubiquitous wireless connectivity enable V2V (vehicle-to-vehicle) and V2I (vehicle-to-infrastructure) communications systems. A.I. powered real-time collision avoidance systems, such as those offered by Mobileye, are expected to materially reduce frequency and severity of accidents. In commercial fleets the effect could be even more dramatic, because severity in commercial truck accidents can easily reach $300,000-$500,000 per claim.

Boston-based TrueMotion provides a telematics platform that utilizes mobile technology, machine learning, and data science, to accurately determine when a person is driving and reveal behaviors behind the wheel, including distracted driving. The company raised $10M in total from General Catalyst and Bain Capital Ventures.


In commercial and residential real estate sector, California-based Hippo Insurance developed pricing strategy that relies on data from multiple sources as well as on IoT and smart home sensors to prevent losses in the first place and to price policies more fairly. For example, because undetected water leaks can result in tens of thousands of dollars in losses per claim, the company’s first time policy buyers receive a free water leak sensor that sends alerts to property owners as soon as water is detected. As of July 2019, Hippo raised in total over $200M, including the recent $100M Series D round led by Bond.

New business models, emerging product lines, autonomous vehicles policies

With advanced driver-assisted systems (ADAS) or other telematics systems installed on vehicles, insurance companies can now receive real-time data on driver behavior and driving patterns. DLTs enable insurers to collect data from multiple sources and update smart contracts in real time. The result is more accurate re

al-time dynamic risk assessments and pricing models, such as pay-as-you-drive (PAYD), pay-how-you-drive (PHYD), and on-demand just-in-time insurance offering. Companies, such as Metromile, Root Insurance, and Cuvva, are pioneering these models with tremendous success. Other companies, such as New York-based and SoftBank backed Lemonade which recently launched in Boston, are disrupting insurance with their peer-to-peer insurance business models.

In an increasingly interconnected world of IoT devices and sensors, cybersecurity becomes critical. Until recently, companies have been dealing with cyber risks by adopting technology solutions, but in addition to tech solutions, businesses are now increasingly hedging financial risks by purchasing cyber insurance. While the concept of cyber insurance is not new (the first cyber insurance policy was written by Lloyd’s of London in 2000), it is now the fastest growing segment in the insurance sector and is projected to reach $20 Billion within the next 5 years. Despite a number of large carriers offering cyber insurance, underwriting cyber risk is still a difficult task because of the lack of historical information. This is the problem California-based At-Bay is tackling. In May 2018, the company raised $13M in Series A from Khosla Ventures, Lightspeed Venture Partners, and others.

And then, of course, self-driving cars. With Level 4 and Level 5 autonomous cars on the road (estimated by 2035), drivers will not be in control of their vehicles. Many believe that it will dramatically reduce the need for insurance resulting in 85–90% reduction in insurance premiums. While it is true that driver risk will be largely reduced or eliminated, new risks, such as software and hardware failure, as well as risk of cyber attack, will be introduced. Thus, we should expect a shift of risk from individuals to manufacturers and software license providers, or from personal insurance lines to commercial insurance. Large insurance carriers are reacting to this threat by boosting their data and A.I. capabilities, while startups, such as Avinew, are already beginning to offer policies that cover semi-autonomous vehicles. Traditional insurers typically increase their premiums on cars with new technology, because they do not have enough data. Avinew, however, has developed a way to collect operational data on smart vehicles and incorporate that data into risk assessment and policy pricing by leveraging telematics, A.I., and ML. The California-based company raised $5M in Seed funding from American Family Ventures, Frontier Venture Capital, and RPM Ventures.

Innovation is happening along the entire insurance value chain. New technologies streamline existing processes, creating new revenue models and product offerings, and new companies are rethinking the insurance business ground-up. Not that far in the future, I see the emergence of decentralized autonomous insurance organizations that will leverage IoT, A.I., and DLTs to enable P2P insurance and eliminate the need for middle men. I see a future state of the industry in which customer engagement, policy underwriting, claim filing, inspection, claim settlement, payments are customized and fully automated.





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