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SimpliFed serves up $500,000 pre-seed toward infant nutrition support

By Christine Hall

Feeding babies can take many different forms, and is also an area where parents can feel less supported as they navigate this new milestone in their lives.

Enter SimpliFed, an Ithaca, New York-based company providing virtual lactation and a baby feeding support platform. The startup announced Friday that it raised $500,000 in pre-seed funding led by Third Culture Capital.

Andrea Ippolito, founder and CEO of SimpliFed. Image Credits: SimpliFed

CEO Andrea Ippolito, a biomedical engineer and mother of two young children, had the idea for SimpliFed three years ago. She struggled with breastfeeding after having her first child and, realizing that she was not alone in this area, set out to figure out a way to get anyone access to information and support for infant feeding.

“Post discharge is when the rubber meets the goal for us,” she told TechCrunch. “This is a huge pain point for Medicaid, and it is not just about increasing access, but providing ongoing support for feeding and the quagmire that is health insurance. We want to help moms reach their infant feeding goals, no matter how they choose to feed, and to figure out what feeding looks like for them.”

The American Academy of Pediatrics recommends that mothers nurse for up to six months. However, the Centers for Disease Control and Prevention estimates that 60% of mothers don’t breastfeed for as long as they intend due to reasons like difficulty lactating or the baby latching, sickness or an unsupportive work environment.

SimpliFed’s platform is a judgement-free zone providing evidence-based information on nutritional health for babies. It isn’t meant to replace typical care that mother and baby will receive before and after delivery, but to provide support when issues arise, Ippolito said. Parents can book a free, initial 15-minute virtual consultation with a lactation expert and then subsequent 60-minute sessions for $100 each. There is also a future membership option for those seeking continuing care.

The new funds will be used to hire additional employees to further develop the telelactation platform and grow the company’s footprint, Ippolito said. The platform is gearing up to go through a clinical study to co-design the program with 1,000 mothers. She also wants to build out relationships with payers and providers toward a longer-term goal of becoming in-network and paid through reimbursement from health plans.

Julien Pham, managing partner at Third Culture Capital, said he met Ippolito at MIT Hacking Medicine a decade ago. A physician by training, he saw first-hand how big of an opportunity it is to demystify providing the best nutrition for babies.

“The U.S. culture has evolved over the years, and millennials are the next-generation moms who have a different ask, and SimpliFed is here at the right time,” Pham said. “Andrea is just a dynamo. We love her energy and how she is at the front line of this as a mother herself — she is most qualified to do this, and we support her.

 

Bangkok-based insurtech Sunday banks $45M Series B from investors like Tencent

By Catherine Shu

Sunday, an insurtech startup based in Bangkok, announced it has raised a $45 million Series B. Investors include Tencent, SCB 10X, Vertex Growth, Vertex Ventures Southeast Asia & India, Quona Capital, Aflac Ventures and Z Venture Capital. The company says the round was oversubscribed, and that it doubled its revenue growth in 2020.

Founded in 2017, Sunday describes itself as a “full-stack” insurtech, which means it handles everything from underwriting to distribution of its policies. Its products currently include motor and travel insurance policies that can be purchased online, and Sunday Health for Business, a healthcare coverage program for employers. Sunday also offers subscription-based smartphone plans through partners.

The company uses AI and machine learning-based technology underwrite its motor insurance and employee health benefits products, and says its data models also allow it to automate pricing and scale its underwriting process for complex risks. Sunday says it currently serves 1.6 million customers.

The new funding will be used to expand in Indonesia and develop new distribution channels, including insurance agents and SMEs.

Insurance penetration is still relatively low in many Southeast Asian markets, including Indonesia, but the industry is gaining traction thanks to increasing consumer awareness. The COVID-19 pandemic also drove interest in financial planning, including investment and insurance, especially health coverage.

Other insurtech startups in Indonesia that have recently raised funding include Lifepal, PasarPolis, Qoala and Fuse.

In a statement, Sunday co-founder and chief executive officer Cindy Kuo said, “Awareness for health insurance will continue to increase and we believe more consumers would be open to shop for insurance online. We plan to expand our platform architecture to offer retail insurance to our health members and partners while we continue to grow our portfolio in Thailand and Indonesia.”

Shepherd raises $6.2M seed round to tackle the construction insurance market

By Alex Wilhelm

Shepherd, an insurtech startup focused on the construction market, has closed a $6.15 million seed round led by Spark Capital. The funding event comes after the startup raised a pre-seed round in February led by Susa Ventures, which also participated in Shepherd’s latest fundraising event.

Thinking broadly, Shepherd fits into a theme of neoinsurance providers selling more to other companies than to consumers. Insurtech startups serving consumers enjoyed years of venture capital backing only to find their public debuts met with early optimism followed quickly by eroding share prices.

But companies like Shepherd — and Blueprint Title earlier this week — are wagering on there being margin elsewhere in the insurance world to attack. For Shepherd, the construction market is its target, an industry that it intends to carve into starting with excess liability coverage.

The company’s co-founder and CEO, Justin Levine, told TechCrunch that contractors in the construction space have a number of insurance requirements, including general liability, commercial auto and so forth. But construction projects often also require more liability coverage, which is sold as excess or umbrella policies.

Targeting the middle-market of the construction space — companies doing $25 million to $250 million in projects per year, in its view — Shepherd wants to lean on technology as a way to help underwrite customers.

Levine said that his company’s offering will have two core parts. The first is what you expected, namely a complete digital experience for customers. The CEO likened its digital offering to table stakes for the insurtech world. We agree. But the company gets more interesting when we consider its second half, namely its work to partner with construction tech providers to help it make underwriting decisions.

The startup has partnered with Procore, for example, a company that invested in its business.

The concept of leaning on third-party software companies to help make underwriting decisions makes some sense — companies that are more technology-forward in terms of adopting new techniques and methods won’t have the same underwriting profile as companies that don’t. Generally, more data makes for better underwriting decisions; linking to the software that helps construction companies function makes good sense from that perspective.

The CEO of Procore agrees, telling TechCrunch that an early customer of his business said that its product is “a risk management solution disguised as construction management software.” The more risk that is managed, the lower Shepherd’s loss ratios may prove over time, allowing it to better compete on price.

On the subject of price, Levine thinks that the construction insurance market is suffering at the moment. Rising settlement costs have led to some legacy insurance books in the space with larger-than-anticipated losses, pushing some providers to raise prices. Levine’s view is that that Shepherd’s ability to enter its market without a legacy book of business will help it offer competitive rates.

Excess liability coverage is the “wedge” that Shepherd intends to use to get into the construction insurance market, it said, with intention of launching other products in time. The startup is attacking excess liability coverage first, its CEO said, because it’s the place of maximum pain in the larger construction insurance market.

Frankly, TechCrunch finds the B2B neoinsurance startup market fascinating. Selling policies to consumers has a particular set of cost of goods sold (COGS) — varying based on the type of coverage, of course — and often stark go-to-market costs. Furthermore, customer acquisition costs (CACs) can prove irksome when going up against national brands with huge budgets. Perhaps the business insurance market will prove more lucrative for upstart tech companies. Venture investors are certainly willing to place that particular wager.

Natalie Sandman led the deal for Spark, telling TechCrunch that when she first encountered Shepherd it was working on a different project, but that when it shifted its focus, it struck a chord with her firm. The investor said that the idea of bringing new data to the construction insurance underwriting process may help the company make smarter decisions. In the insurance world, better underwriting choices mean more profitable coverage. Which means greater future cash flows. And we all know that that means for value creation.

Blueprint raises $16M Series B to grow its title-focused insurtech business

By Alex Wilhelm

Blueprint Title, an insurtech startup working in the title insurance space, announced this morning that it closed a $16 million Series B. The new round was led by Forté Ventures. The startup previously raised an $8.5 million Series A in the final weeks of 2019.

While Blueprint is an insurtech startup and therefore fits into the neoinsurance cohort that we’ve tracked in recent quarters as a number of companies from the group have gone public, it’s somewhat distinct. Blueprint is different from the Roots and MetroMiles and Hippos that debuted via traditional IPOs or SPACs; it largely sells to business customers and has a very different product on offer.

The neoinsurance companies that went public in the last year and a half sell to consumers. Blueprint, in contrast, sells to professional groups looking for a better title insurance experience. That means its customer base is not made up of consumers hoping to cover their main residence, Blueprint CEO Steve Berneman told TechCrunch in an interview.

That means that the company’s go-to-market activities are distinct from its mates in the consumer-focused cohort and that its loss profile is very different.

Title insurance, Berneman said, has around a 1% to 4% claims rate, far lower than auto insurance, to pick an example. That means its risk profile is different, and its pricing less flexible; there’s less loss ratio to wring out of title insurance underwriting, so cost and delivery of service are even more important than in other insurance varietals.

According to the CEO, the title insurance market in the United States today is made up of four companies with around 90% market share. And thanks to rules requiring public pricing in many states, there’s alignment on pricing from some leading players. The result of market concentration and effective price harmonization is that Berneman thinks that the $18 billion title insurance business should really be a $10 billion market.

Our call with Blueprint was the first in which a startup discussed shrinking its market.

But the point is reasonable; if title insurance is mispriced, and Blueprint sells to corporate customers, it can likely offer profitable coverage at a lower-than-market price point — and grow quickly in the process. That appears to be the case, with the startup stating in a release that it anticipates 400% revenue growth in 2021 when compared to 2020.

That growth rate explains the Nashville-based company’s most recent round and what we presume was a stiff upsizing in its valuation.

As part of its funding round announcement, Blueprint also disclosed that it has purchased Southwest Land Title Insurance Company, an underwriting company. Berneman said that to shrink the title insurance market through more reasonable pricing, his company needs to be full-stack, i.e., both writing its own coverage and selling it. Otherwise, margins would leak on either side of its operations.

Blueprint, akin to Next Insurance, is a startup bet that selling insurance to business customers will prove to be a lucrative effort. Given that consumer-focused neoinsurance providers have seen Wall Street change its tune on their value, it will be interesting to watch this more B2B cohort grow and eventually debut.

Insurify, a ‘virtual insurance agent,’ raises $100M Series B

By Mary Ann Azevedo

How many of us have not switched insurance carriers because we don’t want to deal with the hassle of comparison shopping?

A lot, I’d bet.

Today, Insurify, a startup that wants to help people make it easier to get better rates on home, auto and life insurance, announced that it has closed $100 million in an “oversubscribed” Series B funding round led by Motive Partners.

Existing backers Viola FinTech, MassMutual Ventures, Nationwide, Hearst Ventures and Moneta VC also put money in the round, as well as new investors Viola Growth and Fort Ross Ventures. With the new financing, Cambridge, Massachusetts-based Insurify has now raised a total of $128 million since its 2013 inception. The company declined to disclose the valuation at which the money was raised.

Since we last covered Insurify, the startup has seen some impressive growth. For example, it has seen its new and recurring revenue increase by “6x” since it closed its Series A funding in the 2019 fourth quarter. Over the last three years, Insurify has achieved a CAGR (compound annual growth rate) of 151%, according to co-founder and CEO Snejina Zacharia. It has also seen consistent “2.5x” year-over-year revenue growth, she said.

Insurify has built a machine learning-based virtual insurance agent that integrates with more than 100 carriers to digitize — and personalize — the insurance shopping experience. There are others in the insurtech space, but none that we know of currently tackling home, auto and life insurance. For example, Jerry, which has raised capital twice this year, is focused mostly on auto insurance, although it does have a home product. The Zebra, which became a unicorn this year, started out as a site for people looking for auto insurance via its real-time quote comparison tool. Over time, it has also evolved to offer homeowners insurance with the goal of eventually branching out into renters and life insurance. But it too is mostly focused on auto.

Zacharia said that since Insurify’s Series A funding, it has expanded its home insurance marketplace, deepened its carrier integrations to provide users an “instant” purchase experience and launched its first two embedded insurance products through partnerships with Toyota Insurance Management Solutions and Nationwide (the latter of which also participated in the Series B funding round).

Image Credits: Insurify

Last year, when ShyScanner had to lay off staff, Insurify scooped up much of its engineering team and established an office in Sofia, Bulgaria.

Zacharia, a former Gartner executive, was inspired to start the company after she was involved in a minor car accident while getting her MBA at MIT. The accident led to a spike in her insurance premium and Zacharia was frustrated by the “complex and cumbersome” experience of car insurance shopping. She teamed up with CTO Tod Kiryazov to build Insurify, which the pair describe as a virtual insurance agent that offers real-time quotes.

“We decided to build the most trusted virtual insurance agent in the industry that allows for customers to easily search, compare and buy fully digitally — directly from their mobile phone, or desktop, and really get a very smart, personalized experience based on their unique preferences,” Zacharia told TechCrunch. “We leverage artificial intelligence to be able to make recommendations on both coverage as well as carrier selection.”

Notably, Insurify is also a fully licensed agent that takes over the fulfillment and servicing of the policies. Since the company is mostly working as an insurance agent, it gets paid new and renewed commission. So while it’s not a SaaS business, its embedded insurance offerings have SaaS-like monetization.

“Our goal is to provide an experience for the end consumer that allows them to service and manage all of their policies in one place, digitally,” Zacharia said. “We think that the data recommendations that the platform provides can really remove most of the friction that currently exists in the shopping experience.”

Insurify plans to use its fresh capital to continue to expand its operations and accelerate its growth plans. It also, naturally, wants to add to its 125-person team.

“We want to build into our API integrations so customers can receive real-time direct quotes with better personalization and a more tailored experience,” Kiryazov said. “We also want to identify more embedded insurance opportunities and expand the product functionality.”

The company also down the line wants to expand into other verticals such as pet insurance, for example.

Insurify intends to use the money in part to build brand awareness, potentially through TV advertising.

“Almost half of our revenue comes from self-directed traffic,” Zacharia said. “So we want to explore more inorganic growth.”

James “Jim” O’Neill, founding partner at Motive Partners and partner Andy Rear point out that online purchasing now accounts for almost all of the growth in U.S. auto insurance. 

“The lesson from other markets which have been through this transition is that customers prefer choice, presented as a simple menu of products and prices from different insurers, and a straightforward online purchasing process,” they wrote via email. “The U.S. auto market is huge: even a slow transition to online means a massive opportunity for Insurify.”

In conducting their due diligence, the pair said they were impressed with how the startup is building a business model “that works for customers, insurers and white-label partners.”

Harel Beit-On, founder and general partner at Viola Growth, believes that the quantum leap in e-commerce due to COVID-19 will completely transform the buying experience in almost every sector, including insurance.

“It is time to bring the frictionless purchasing experience that customers expect to the insurance space as well,” she said. “Following our fintech fund’s recent investment in the company, we watched Insurify’s immense growth, excellent execution with customer acquisition and building a brand consumers trust.”

Oviva grabs $80M for app-delivered healthy eating programs

By Natasha Lomas

UK startup Oviva, which sells a digital support offering, including for Type 2 diabetes treatment, dispensing personalized diet and lifestyle advice via apps to allow more people to be able to access support, has closed $80 million in Series C funding — bringing its total raised to date to $115M.

The raise, which Oviva says will be used to scale up after a “fantastic year” of growth for the health tech business, is co-led by Sofina and Temasek, alongside existing investors AlbionVC, Earlybird, Eight Roads Ventures, F-Prime Capital, MTIP, plus several angels.

Underpinning that growth is the fact wealthy Western nations continue to see rising rates of obesity and other health conditions like Type 2 diabetes (which can be linked to poor diet and lack of exercise). While more attention is generally being paid to the notion of preventative — rather than reactive — healthcare, to manage the rising costs of service delivery.

Lifestyle management to help control weight and linked health conditions (like diabetes) is where Oviva comes in: It’s built a blended support offering that combines personalized care (provided by healthcare professionals) with digital tools for patients that help them do things like track what they’re eating, access support and chart their progress towards individual health goals.

It can point to 23 peer-reviewed publications to back up its approach — saying key results show an average of 6.8% weight loss at 6 months for those living with obesity; while, in its specialist programs, it says 53% of patients achieve remission of their type 2 diabetes at 12 months.

Oviva typically sells its digitally delivered support programs direct to health insurance companies (or publicly funded health services) — who then provide (or refer) the service to their customers/patients. Its programs are currently available in the UK, Germany, Switzerland and France — but expanding access is one of the goals for the Series C.

“We will expand to European markets where the health system reimburses the diet and lifestyle change we offer, especially those with specific pathways for digital reimbursement,” Oviva tells TechCrunch. “Encouragingly, more healthcare systems have been opening up specific routes for such digital reimbursement, e.g., Germany for DiGAs or Belgium just in the last months.”

So far, the startup has treated 200,000 people but the addressable market is clearly huge — not least as European populations age — with Oviva suggesting more than 300 million people live with “health challenges” that are either triggered by poor diet or can be optimised through personalised dietary changes. Moreover, it suggests, only “a small fraction” is currently being offered digital care.

To date, Oviva has built up 5,000+ partnerships with health systems, insurers and doctors as it looks to push for further scale by making its technology more accessible to a wider range of people. In the past year it says it’s “more than doubled” both people treated and revenue earned.

Its goal is for the Series C funding is to reach “millions” of people across Europe who need support because they’re suffering from poor health linked to diet and lifestyle.

As part of the scale up plan it will also be growing its team to 800 by the end of 2022, it adds.

On digital vs face-to-face care — setting aside the potential cost savings associated with digital delivery — it says studies show the “most striking outcome benefits” are around uptake and completion rates, noting: “We have consistently shown uptake rates above 70% and high completion rates of around 80%, even in groups considered harder to reach such as working age populations or minority ethnic groups. This compares to uptake and completion rates of less than 50% for most face-to-face services.”

Asked about competition, Oviva names Liva Healthcare and Second Nature as its closest competitors in the region.

“WW (formally Weight Watchers) also competes with a digital solution in some markets where they can access reimbursement,” it adds. “There are many others that try to access this group with new methods, but are not reimbursed or are wellness solutions. Noom competes as a solution for self-paying consumers in Europe, as many other apps. But, in our view, that is a separate market from the reimbursed medical one.”

As well as using the Series C funding to bolster its presence in existing markets and target and scale into new ones, Oviva says it may look to further grow the business via M&A opportunities.

“In expanding to new countries, we are open to both building new organisations from the ground up or acquiring existing businesses with a strong medical network where we see that our technology can be leveraged for better patient care and value creation,” it told us on that.

 

Why have the markets spurned public neoinsurance startups?

By Alex Wilhelm

We’ve spent quite a lot of time of late wondering just what the heck is up with the valuations of insurtech startups that went public in the last year. Keep in mind that we’re discussing neoinsurance providers like MetroMile and Hippo, not insurtech marketplaces like Insurify or Zebra.

There was a stream of insurtech exits in 2020 and early 2021. After Lemonade’s firecracker IPO, MetroMile and Hippo and Root also went public. Since those debuts, we’ve seen their valuations erode significantly.


The Exchange explores startups, markets and money.

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But Oscar Health got somewhat lost in our larger analysis of the space. An investor pointed out to The Exchange this weekend that we were a bit early in wondering just what investors were thinking when Oscar was going public — its IPO price range felt incredibly high, and we said so. Then, Oscar Health priced above that $32 to $34 per share interval, kicking off its life worth $39 per share.

Today’s it’s worth $13.58 per share.

We could call it another data point in our larger analysis, but it’s a bit more than that as Oscar Health expands the list of insurance types that startups tackled, scaled, took public and then saw fall out of investor favor. The companies that we are examining cover a number of industries, from auto insurance (Root, MetroMile), to home and rental insurance (Hippo, Lemonade), and, thanks to Oscar Health, health insurance as well. All are taking a whacking by the market.

Why? Happily, I think I’ve figured it out. More precisely, a CEO of a neoinsurance company in a different niche talked The Exchange through one particular hypothesis that makes rather good sense.

Show me the money metrics

Last week, I chatted with Pie Insurance co-founder CEO John Swigart. Pie sells SMB-focused insurance, with a focus on workers’ comp coverage. In Swigart’s view, small businesses have historically been overcharged and underserved for insurance. With a bit of tech, his company can offer coverage to smaller companies than many traditional insurance providers found attractive, and at better price points to boot.

Pie raised a $118 million Series C in March, with Crunchbase tallying $306 million in external capital for the company thus far. We’ll talk more about Pie at a later date.

What matters for our needs this morning is what Swigart said when I asked him what in the flying fuck was going on with public insurtech share prices. Given that he is building a related company, I was hoping that he would be both up to speed and have a take. He did.

Indonesian D2C insurance marketplace Lifepal raises $9M Series A

By Catherine Shu

Choosing an insurance policy is one of the most complicated financial decisions a person can make. Jakarta-based Lifepal wants to simplify the process for Indonesians with a marketplace that lets users compare policies from more than 50 providers, get help from licensed agents and file claims. The startup, which says it is the country’s largest direct-to-consumer insurance marketplace, announced today it has raised a $9 million Series A. The round was led by ProBatus Capital, a venture firm backed by Prudential Financial, with participation from Cathay Innovation and returning investors Insignia Venture Partners, ATM Capital and Hustle Fund.

Lifepal was founded in 2019 by former Lazada executives Giacomo Ficari and Nicolo Robba, along with Benny Fajarai and Reza Muhammed. The new funding brings its total raised to $12 million.

The marketplace’s partners currently offer about 300 policies for life, health, automotive, property and travel coverage. Ficari, who also co-founded neobank Aspire, told TechCrunch that Lifepal was created to make comparing, buying and claiming insurance as simple as shopping online.

“The same kind of experience a customer has today on a marketplace like Lazada—the convenience, all digital, fast delivery—we saw was lacking in insurance, which is still operating with offline, face-to-face agents like 20 to 30 years ago,” he said.

Indonesia’s insurance penetration rate is only about 3%, but the market is growing along with the country’s gross domestic product thanks to a larger middle-class. “We are really at a tipping point for GDP per capita and a lot of insurance carriers are focusing more on Indonesia,” said Ficari.

Other venture-backed insurtech startups tapping into this demand include Fuse, PasarPolis and Qoala. Both Qoala and PasarPolis focus on “micro-policies,” or inexpensive coverage for things like damaged devices. PasarPolis also partners with Gojek to offer health and accident insurance to drivers. Fuse, meanwhile, insurance specialists an online platform to run their businesses.

Lifepal takes a different approach because it doesn’t sell micro-policies, and its marketplace is for customers to purchase directly from providers, not through agents.
Based on Lifepal’s data, about 60% of its health and life insurance customers are buying coverage for the first time. On the other hand, many automotive insurance shoppers had policies before, but their coverage expired and they decided to shop online instead of going to an agent to get a new one.

Ficari said Lifepal’s target customers overlap with the investment apps that are gaining traction among Indonesia’s growing middle class (like Ajaib, Pluang and Pintu). Many of these apps provide educational content, since their customers are usually millennials investing for the first time, and Lifepal takes a similar approach. Its content side, called Lifepal Media, focuses on articles for people who are researching insurance policies and related topics like personal financial planning. The company says its site, including its blog, now has about 4 million monthly visitors, creating a funnel for its marketplace.

While one of Lifepal’s benefits is enabling people to compare policies on their own, many also rely on its customer support line, which is staffed by licensed insurance agents. In fact, Ficari said about 90% of its customers use it.

“What we realize is that insurance is complicated and it’s expensive,” said Ficari. “People want to take their time to think and they have a lot of questions, so we introduced good customer support.” He added Lifepal’s combination of enabling self-research while providing support is similar to the approach taken by PolicyBazaar in India, one of the country’s largest insurance aggregators.

To keep its business model scalable, Lifepal uses a recommendation engine that matches potential customers with policies and customer support representatives. It considers data points like budget (based on Lifepal’s research, its customers usually spend about 3% to 5% of their yearly income on insurance), age, gender, family composition and if they have purchased insurance before.

Lifepal’s investment from ProBatus will allow it to work with Assurance IQ, the insurance sales automation platform acquired by Prudential Financial two years ago.

In a statement, ProBatus Capital founder and managing partner Ramneek Gupta said Lifepal’s “three-pronged approach” (its educational content, online marketplace and live agents for customer support) has the “potential to change the way the Indonesian consumer buys insurance.”

Part of Lifepal’s funding will be used to build products to make it easier to claim policies. Upcoming products include Insurance Wallet, which will include an application process with support on how to claim a policy—for example, what car repair shop or hospital a customer should go to—and escalation if a claim is rejected. Another product, called Easy Claim, will automate the claim process.

“The goal is to stay end-to-end with the customer, from reading content, comparing policies, buying and then renewing and using them, so you really see people sticking around,” said Ficari.

Lifepal is Cathay Innovation’s third insurtech investment in the past 12 months. Investment director Rajive Keshup told TechCrunch in an email that it backed Lifepal because “the company grew phenomenally last year (12X) and is poised to beat its aggressive 2021 plan despite the proliferation of the COVID delta variant, accentuating the fact that Lifepal is very much on track to replicate the success of similar global models such as Assurance IQ (US) and PolicyBazaar (India).”

DigiSure, the mobility insurance platform with high-tech screening, comes out of stealth with $13.1M raise

By Rebecca Bellan

DigiSure, a digital insurance company that caters to modern mobility form factors like peer-to-peer marketplaces, is officially coming out of stealth to announce a $13.1 million pre-Series A funding round. The startup will use the funds to hire more than 50 engineers, data scientists, business development, insurance and compliance specialists, as well as scale into new industry verticals and across into Europe.

Since its founding in 2018, DigiSure has built a business around using AI and machine learning to manage big data in real time in order to provide a nuanced risk assessment and more fairly priced liability insurance for individuals renting vehicles. DigiSure has a total of 12 clients, including motorcycle rental company EagleRider, EV rental company Envoy and truck rental company Fetch. DigiSure says it goes beyond credit and driving history to give users a more personalized quote, and in the process helps operators lower their own insurance costs.

“With our DigiSure Protection Suite, we screen all the people who are looking to rent and operate vehicles, we prevent bad actors from getting on these vehicles that might harm other people and then we provide insurance to the operator, as well as to the individual renters,” Mike Shim, DigiSure’s co-founder and CEO, told TechCrunch.

Property and casualty insurance, which is usually one of an operator’s top operational costs, is nearly a $700 billion industry in the U.S., and Shim thinks that’s in large part because of outdated screening methods that result in bad actors slipping through the cracks and causing damage. Traditional auto insurance carriers typically provide a quote by comparing statistical averages to information like a user’s age, gender, education level, location, driving record, credit history, vehicle details and location, but in the vehicle rental space, Shim says underwriting is limited or non-existent.

“There is therefore a huge opportunity to improve the quality of the risk management by using more sophisticated pricing models that lead to better conversions and lower losses overall,” he said.

DigiSure’s Protection Suite uses traditional underwriting factors, as well, but also utilizes the renter’s transaction history alongside external data sources that a normal insurance company wouldn’t have access to. According to a statement from the company, the Protection Suite includes “AI-powered identity verification utilizing biometrics technology, advanced fraud detection, credit checks, driving history and telematics data integration.”

It then plugs the data into its proprietary machine learning algorithms to get better at providing real-time insurance quotes over time, says Shim. For example, DigiSure’s data science team might find that the ratio of rider height to seat height of a motorcycle is an important risk factor in predicting low-speed tip overs and then recommend improvements to the model.

“We’re basically constructing a composite risk profile on that user and building a profile on that user over time,” said Shim. “Our technology is creating a next generation underwriting model for next generation mobility.”

DigiSure is able to perform screenings and come up with a quote in seven seconds or less, according to Shim. On the user side of things, by the time they’ve begun the checkout process and are ready to finalize a booking of, say, an RV rental, DigiSure is able to offer up a dynamically priced bundled insurance product at the point of sale, making it feel like a real-time process.

DigiSure is still new, so there’s room to grow, says Shim. The traditional world of vehicle insurance is not built for newer mobility models, like peer-to-peer, which is currently DigiSure’s bread and butter, or shared micromobility, which the company sees a lot of potential in.

“The main problem was that insurance companies were just not serving our mobility customers and not able to keep pace with not only all the new business cases but also the fact that consumers are just looking to move and get around in different ways,” said Shim. “We’re basically creating a mobility insurance platform and a risk platform that is trying to get ahead and support these innovators.”

In the case of shared micromobility, where there’s no bundled insurance product offered at checkout, DigiSure would primarily offer its fast screening services to filter out potential loose cannons from hopping on shared scooters or bikes. The operator could then point to this service in order to lower its overall insurance costs, which typically make up a fairly large portion of the operating costs pie in an industry that’s barely been able to make a profit yet.

Presently, DigiSure doesn’t provide any insurance that covers the rider in the event of personal injury, but Shim says that’s standard for the industry. The platform provides property insurance for the operator or the owner of a vehicle on a peer-to-peer marketplace that protects the vehicle itself. It also provides casualty insurance for both the marketplace or operator and the rider or driver, which includes liability coverage to protect those parties if the driver is responsible for an accident that causes injury to another person or damage to another person’s property.

While insurance is certainly on offer here, it’s the screening tech that makes DigiSure’s product unique.

“Our view is it’s better to focus on the screening tech to weed out bad actors and keep the platform safe,” said Shim. “Those 1% to 2% of the customer base are likely the ones who are going to cause 30% to 40% of the worst-case claims costs. If you can control for those outcomes, you can really impact your bottom-line insurance costs.”

This funding round was led by Morado Ventures, with participation from Xplorer Capital, Valor Equity Partners, Clocktower Technology Ventures, True North Cos and ex-Upwork CEO Fabio Rosati.

How the law got it wrong with Apple Card

By Ram Iyer
Liz O'Sullivan Contributor
Liz O’Sullivan is CEO of Parity, a platform that automates model risk and algorithmic governance for the enterprise. She also advises the Surveillance Technology Oversight Project and the Campaign to Stop Killer Robots on all things artificial intelligence.
More posts by this contributor

Advocates of algorithmic justice have begun to see their proverbial “days in court” with legal investigations of enterprises like UHG and Apple Card. The Apple Card case is a strong example of how current anti-discrimination laws fall short of the fast pace of scientific research in the emerging field of quantifiable fairness.

While it may be true that Apple and their underwriters were found innocent of fair lending violations, the ruling came with clear caveats that should be a warning sign to enterprises using machine learning within any regulated space. Unless executives begin to take algorithmic fairness more seriously, their days ahead will be full of legal challenges and reputational damage.

What happened with Apple Card?

In late 2019, startup leader and social media celebrity David Heinemeier Hansson raised an important issue on Twitter, to much fanfare and applause. With almost 50,000 likes and retweets, he asked Apple and their underwriting partner, Goldman Sachs, to explain why he and his wife, who share the same financial ability, would be granted different credit limits. To many in the field of algorithmic fairness, it was a watershed moment to see the issues we advocate go mainstream, culminating in an inquiry from the NY Department of Financial Services (DFS).

At first glance, it may seem heartening to credit underwriters that the DFS concluded in March that Goldman’s underwriting algorithm did not violate the strict rules of financial access created in 1974 to protect women and minorities from lending discrimination. While disappointing to activists, this result was not surprising to those of us working closely with data teams in finance.

There are some algorithmic applications for financial institutions where the risks of experimentation far outweigh any benefit, and credit underwriting is one of them. We could have predicted that Goldman would be found innocent, because the laws for fairness in lending (if outdated) are clear and strictly enforced.

And yet, there is no doubt in my mind that the Goldman/Apple algorithm discriminates, along with every other credit scoring and underwriting algorithm on the market today. Nor do I doubt that these algorithms would fall apart if researchers were ever granted access to the models and data we would need to validate this claim. I know this because the NY DFS partially released its methodology for vetting the Goldman algorithm, and as you might expect, their audit fell far short of the standards held by modern algorithm auditors today.

How did DFS (under current law) assess the fairness of Apple Card?

In order to prove the Apple algorithm was “fair,” DFS considered first whether Goldman had used “prohibited characteristics” of potential applicants like gender or marital status. This one was easy for Goldman to pass — they don’t include race, gender or marital status as an input to the model. However, we’ve known for years now that some model features can act as “proxies” for protected classes.

If you’re Black, a woman and pregnant, for instance, your likelihood of obtaining credit may be lower than the average of the outcomes among each overarching protected category.

The DFS methodology, based on 50 years of legal precedent, failed to mention whether they considered this question, but we can guess that they did not. Because if they had, they’d have quickly found that credit score is so tightly correlated to race that some states are considering banning its use for casualty insurance. Proxy features have only stepped into the research spotlight recently, giving us our first example of how science has outpaced regulation.

In the absence of protected features, DFS then looked for credit profiles that were similar in content but belonged to people of different protected classes. In a certain imprecise sense, they sought to find out what would happen to the credit decision were we to “flip” the gender on the application. Would a female version of the male applicant receive the same treatment?

Intuitively, this seems like one way to define “fair.” And it is — in the field of machine learning fairness, there is a concept called a “flip test” and it is one of many measures of a concept called “individual fairness,” which is exactly what it sounds like. I asked Patrick Hall, principal scientist at bnh.ai, a leading boutique AI law firm, about the analysis most common in investigating fair lending cases. Referring to the methods DFS used to audit Apple Card, he called it basic regression, or “a 1970s version of the flip test,” bringing us example number two of our insufficient laws.

A new vocabulary for algorithmic fairness

Ever since Solon Barocas’ seminal paper “Big Data’s Disparate Impact” in 2016, researchers have been hard at work to define core philosophical concepts into mathematical terms. Several conferences have sprung into existence, with new fairness tracks emerging at the most notable AI events. The field is in a period of hypergrowth, where the law has as of yet failed to keep pace. But just like what happened to the cybersecurity industry, this legal reprieve won’t last forever.

Perhaps we can forgive DFS for its softball audit given that the laws governing fair lending are born of the civil rights movement and have not evolved much in the 50-plus years since inception. The legal precedents were set long before machine learning fairness research really took off. If DFS had been appropriately equipped to deal with the challenge of evaluating the fairness of the Apple Card, they would have used the robust vocabulary for algorithmic assessment that’s blossomed over the last five years.

The DFS report, for instance, makes no mention of measuring “equalized odds,” a notorious line of inquiry first made famous in 2018 by Joy Buolamwini, Timnit Gebru and Deb Raji. Their “Gender Shades” paper proved that facial recognition algorithms guess wrong on dark female faces more often than they do on subjects with lighter skin, and this reasoning holds true for many applications of prediction beyond computer vision alone.

Equalized odds would ask of Apple’s algorithm: Just how often does it predict creditworthiness correctly? How often does it guess wrong? Are there disparities in these error rates among people of different genders, races or disability status? According to Hall, these measurements are important, but simply too new to have been fully codified into the legal system.

If it turns out that Goldman regularly underestimates female applicants in the real world, or assigns interest rates that are higher than Black applicants truly deserve, it’s easy to see how this would harm these underserved populations at national scale.

Financial services’ Catch-22

Modern auditors know that the methods dictated by legal precedent fail to catch nuances in fairness for intersectional combinations within minority categories — a problem that’s exacerbated by the complexity of machine learning models. If you’re Black, a woman and pregnant, for instance, your likelihood of obtaining credit may be lower than the average of the outcomes among each overarching protected category.

These underrepresented groups may never benefit from a holistic audit of the system without special attention paid to their uniqueness, given that the sample size of minorities is by definition a smaller number in the set. This is why modern auditors prefer “fairness through awareness” approaches that allow us to measure results with explicit knowledge of the demographics of the individuals in each group.

But there’s a Catch-22. In financial services and other highly regulated fields, auditors often can’t use “fairness through awareness,” because they may be prevented from collecting sensitive information from the start. The goal of this legal constraint was to prevent lenders from discrimination. In a cruel twist of fate, this gives cover to algorithmic discrimination, giving us our third example of legal insufficiency.

The fact that we can’t collect this information hamstrings our ability to find out how models treat underserved groups. Without it, we might never prove what we know to be true in practice — full-time moms, for instance, will reliably have thinner credit files, because they don’t execute every credit-based purchase under both spousal names. Minority groups may be far more likely to be gig workers, tipped employees or participate in cash-based industries, leading to commonalities among their income profiles that prove less common for the majority.

Importantly, these differences on the applicants’ credit files do not necessarily translate to true financial responsibility or creditworthiness. If it’s your goal to predict creditworthiness accurately, you’d want to know where the method (e.g., a credit score) breaks down.

What this means for businesses using AI

In Apple’s example, it’s worth mentioning a hopeful epilogue to the story where Apple made a consequential update to their credit policy to combat the discrimination that is protected by our antiquated laws. In Apple CEO Tim Cook’s announcement, he was quick to highlight a “lack of fairness in the way the industry [calculates] credit scores.”

Their new policy allows spouses or parents to combine credit files such that the weaker credit file can benefit from the stronger. It’s a great example of a company thinking ahead to steps that may actually reduce the discrimination that exists structurally in our world. In updating their policies, Apple got ahead of the regulation that may come as a result of this inquiry.

This is a strategic advantage for Apple, because NY DFS made exhaustive mention of the insufficiency of current laws governing this space, meaning updates to regulation may be nearer than many think. To quote Superintendent of Financial Services Linda A. Lacewell: “The use of credit scoring in its current form and laws and regulations barring discrimination in lending are in need of strengthening and modernization.” In my own experience working with regulators, this is something today’s authorities are very keen to explore.

I have no doubt that American regulators are working to improve the laws that govern AI, taking advantage of this robust vocabulary for equality in automation and math. The Federal Reserve, OCC, CFPB, FTC and Congress are all eager to address algorithmic discrimination, even if their pace is slow.

In the meantime, we have every reason to believe that algorithmic discrimination is rampant, largely because the industry has also been slow to adopt the language of academia that the last few years have brought. Little excuse remains for enterprises failing to take advantage of this new field of fairness, and to root out the predictive discrimination that is in some ways guaranteed. And the EU agrees, with draft laws that apply specifically to AI that are set to be adopted some time in the next two years.

The field of machine learning fairness has matured quickly, with new techniques discovered every year and myriad tools to help. The field is only now reaching a point where this can be prescribed with some degree of automation. Standards bodies have stepped in to provide guidance to lower the frequency and severity of these issues, even if American law is slow to adopt.

Because whether discrimination by algorithm is intentional, it is illegal. So, anyone using advanced analytics for applications relating to healthcare, housing, hiring, financial services, education or government are likely breaking these laws without knowing it.

Until clearer regulatory guidance becomes available for the myriad applications of AI in sensitive situations, the industry is on its own to figure out which definitions of fairness are best.

What happens when Wall Street falls out of love with your sector?

By Alex Wilhelm

It’s been an awful week for public neoinsurance companies. A subsector of the larger insurtech world, neoinsurance providers tackled a number of insurance categories using a blend of modern app design and machine learning in hopes of creating more user-friendly and profitable insurance products.

The idea proved attractive to venture capitalists, who invested in a host of companies working on the problem space. And it went so well that in the last year or so we saw a number of U.S. neoinsurance companies go public.


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That’s the extent of the good news. Since the IPOs and SPAC combinations that took MetroMile, Hippo, Lemonade and Root public, the group has seen their values either decline sharply below their initial trading prices or far under their recent highs.

We’ve covered some of these declines in recent weeks and wondered if we should be worried about neoinsurance valuations and how they may impact startups. This morning, we’re examining what happened to neoinsurance companies this week, why, and which startups could be impacted.

Grounding our work is an interview that The Exchange held with Root CEO Alex Timm in the wake of his company’s earnings report. It’s a pretty illustrative example of where the sector finds itself today: Flush, busy and somewhat unloved.

Recent declines

Measuring from last Friday’s closing price to yesterday’s, here’s a digest of where the market is for public neoinsurance companies:

  • Hippo: -20%
  • MetroMile: -30%
  • Root: -23%
  • Lemonade: -6%

Declines from recent highs are more extreme for several of the now-public neoinsurance companies, something that we discussed last Friday. The point we made then has only become more acute. We could add names to this list, like Oscar Health, but health insurance feels sufficiently distinct from the above companies that I don’t want to muddy the waters.

What’s new in all of this is that the value of some of these companies is getting close to their cash balance. Or more simply, they are trending toward basement-level enterprise values. Here’s the data:

Catch takes hold of $12M to provide benefits that aren’t tied to employers

By Christine Hall

Catch is working to make sure that every gig worker has the health and retirement benefits they need.

The company, which is in the midst of moving its headquarters to New York, sells health insurance, retirement savings plans and tax withholding directly to freelancers, contractors or anyone uncovered.

It is now armed with a fresh round of $12 million in Series A funding, led by Crosslink, with participation from earlier investors Khosla Ventures, NYCA Partners, Kindred Ventures and Urban Innovation Fund, to support more distribution partnerships and its relocation from Boston.

Co-founders Kristen Anderson and Andrew Ambrosino started Catch in 2019 and raised $6.1 million previously, giving it a total of $18.1 million in funding.

It took the Catch team of 15 nearly two years to get approvals to sell its platform in 38 states on the federal marketplace. Anderson boasts that only eight companies have been able to do this, and three of them — Catch included — are approved to sell benefits to consumers. The other side of the business is payroll, and the company has gathered thousands of sources based on biller.

“More companies are not offering healthcare, while more people are joining the creator and gig economies, which means more people are not following an employer-led model,” Anderson told TechCrunch.

The age of an average Catch customer is 32 years old, and in addition to current offerings, were asking the company to help them set up income sources, like setting aside money for taxes, retirement, as well as medical leave without having to actively save.

When the global pandemic hit, many of Catch’s customers saw their income collapse, 40% overall across industries, as workers like hairstylists and cooks had income go down to zero in some cases.

It was then that Anderson and Ambrosino began looking at partnership distribution and developed a network of platforms, business facilitation tools, gig marketplaces and payroll companies that were interested in offering Catch. The company intends to use some of the funding to increase its headcount to service those partnerships and go after more, Anderson said.

Catch is one startup providing insurance products, and many of the competitors either do a single offering and do it well, like Starship does with health savings accounts, Anderson said. Catch is taking a different approach by offering a platform experience, but going deep on the process, she added. She likens it to Gusto, which provides cloud-based payroll, benefits and human resource management for businesses, in that Catch is an end-to-end experience, but with a focus on an individual person.

Over the past year, the company’s user base tripled, driven by people taking on second jobs and through a partnership with DoorDash. Platform users are also holding onto 5 times their usual balances, a result of setting more goals and needing to save more, Anderson said. Retirement investments and health insurance have grown similarly.

Going forward, Anderson is already thinking about a Series B, but that won’t come for another couple of years, she said. The company is looking into its own HSA product as well as disability insurance and other products to further differentiate itself from other startups, for example, Spot, Super.mx and Even that all raised venture capital this month to provide benefits.

Catch would also like to serve a broader audience than just those on the federal marketplace. The co-founders are working on how to do this — Anderson mentioned there are some “nefarious companies out there” offering medical benefits at rates that can seem too good to be true, but when the customer reads the fine print, finds out that certain medical conditions are not covered.

“We are looking at how to put the right thing in there because it does get confusing,” Anderson added. “Young people have cheaper options, which means they need to make sure they know what they are getting.”

 

Connected car insurance startup Flock raises $17M Series A led by Chamath Palihapitiya

By Mike Butcher

Cast your mind back to that scene in Minority Report where all those autonomous cars are whizzing through the city. The more practically-minded of you may well have gone: “Yeah, but what about the insurance…?”.

Among the startups building the on-demand, connected insurance world for the vehicles of tomorrow right now are UK-based Zego which has raised $201.7 million. Another is Flock.

Emerging from an academic project to look at drones, Flock shifted into providing drones insurance then commercial vehicle insurance. The twist is that it hooks into the telematics of cars so that the vehicle only triggers insurance cover when it’s actually moving, not when it’s sitting on the lot, incapable of causing any accidents.

Flock has now raised $17 million in a Series A funding led by Social Capital, the investment vehicle run by Chamath Palihapitiya, best known as a SPAC investor and Chairman of Virgin Galactic. Flock’s existing investors Anthemis and Dig Ventures also participated. This round brings Flock’s total funding to $22 million. Justin Saslaw (Social Capital’s Fintech Partner) joins Flock’s Board of Directors as does Ross Mason (Founder of Dig Ventures & MuleSoft).

Ed Leon Klinger, CEO of Flock said: “Transportation is changing faster than ever, but the traditional insurance industry can’t keep up! The proliferation of electric cars, new business models such as ridesharing, and the emergence of autonomous vehicles pose huge challenges that traditional insurers just aren’t equipped for.”

He added: “Modern fleets need an equally modern insurance company that moves as fast as they do. Commercial motor insurance is a $160Bn market, crying out for disruption. The opportunity ahead of us is enormous.”

In a statement Chamath Palihapitiya, CEO of Social Capital said: “Flock is bridging the gap between today’s insurance industry and tomorrow’s transportation realities. By using real-time data to truly understand vehicle risk, Flock is meeting the demands of our rapidly evolving, hyper-connected world. Flock has the potential to help unlock and enable a truly autonomous world, and even save lives. We’re excited to be a part of their journey.”

Speaking to me over a call, Klinger outlined how the company had hit a sweet spot by hooking into Telematics APIs for cars, or by doing special integrations with existing providers and OEMs: “We’ve built our own integrated approach whereby we partner with some and we build bespoke integrations with them. Often they are not as advanced as others. So we’ll either use our integration platform or or we’ll use their approach. We’re highly flexible. The core value proposition at Flock is its flexibility, so we don’t force our own integration approach.”

Khosla Ventures leads Even’s $5M seed to give India the kind of healthcare their insurance doesn’t

By Christine Hall

The global pandemic highlighted inefficiencies and inconsistencies in healthcare systems around the world. Even co-founders Mayank Banerjee, Matilde Giglio and Alessandro Ialongo say nowhere is this more evident than in India, especially after the COVID death toll reached 4 million this week.

The Bangalore-based company received a fresh cash infusion of $5 million in seed funding in a round led by Khosla Ventures, with participation from Founders Fund, Lachy Groom and a group of individuals including Palo Alto Networks CEO Nikesh Arora, CRED CEO Kunal Shah, Zerodha founder Nithin Kamath and DST Global partner Tom Stafford.

Even, a healthcare membership company, aims to cover what most insurance companies in the country don’t, including making going to a primary care doctor as easy and accessible as it is in other countries.

Banerjee grew up in India and said the country is similar to the United States in that it has government-run and private hospitals. Where the two differ is that private health insurance is a relatively new concept for India, he told TechCrunch. He estimates that less than 5% of people have it, and even though people are paying for the insurance, it mainly covers accidents and emergencies.

This means that routine primary care consultations, testings and scans outside of that are not covered. And, the policies are so confusing that many people don’t realize they are not covered until it is too late. That has led to people asking doctors to admit them into the hospital so their bills will be covered, Ialongo added.

Banerjee and Giglio were running another startup together when they began to see how complicated health insurance policies were. About 50 million Indians fall below the poverty line each year, and many become unable to pay their healthcare bills, Banerjee said.

They began researching the insurance industry and talking with hospital executives about claims. They found that one of the biggest issues was incentive misalignment — hospitals overcharged and overtreated patients. Instead, Even is taking a similar approach to Kaiser Permanente in that the company will act as a service provider, and therefore, can drive down the cost of care.

Even became operational in February and launched in June. It is gearing up to launch in the fourth quarter of this year with more than 5,000 people on the waitlist so far. Its health membership product will cost around $200 per year for a person aged 18 to 35 and covers everything: unlimited consultations with primary care doctors, diagnostics and scans. The membership will also follow as the person ages, Ialongo said.

The founders intend to use the new funding to build out their operational team, product and integration with hospitals. They are already working with 100 hospitals and secured a partnership with Narayana Hospital to deliver more than 2,000 COVID vaccinations so far, and more in a second round.

“It is going to take a while to scale,” Banerjee said. “For us, in theory, as we get better pricing, we will end up being cheaper than others. We have goals to cover the people the government cannot and find ways to reduce the statistics.”

 

Ethos picks up $100M at a $2.7B+ valuation for a big data platform to improve life insurance accessibility

By Ingrid Lunden

More than half of the U.S. population has stayed away from considering life insurance because they believe it’s probably too expensive, and the most common way to buy it today is in person. A startup that’s built a platform that aims to break down those conventions and democratize the process by making life insurance (and the benefits of it) more accessible is today announcing significant funding to fuel its rapidly growing business.

Ethos, which uses more than 300,000 data points online to determine a person’s eligibility for life insurance policies, which are offered as either term or whole life packages starting at $8/month, has picked up $100 million from a single investor, SoftBank Vision Fund 2. Peter Colis, Ethos’s CEO and co-founder, said that the funding brings the startup’s valuation to over $2.7 billion.

This is a quick jump for the the company: it was only two months ago that Ethos picked up a $200 million equity round at a valuation of just over $2 billion.

It’s now raised $400 million to date and has amassed a very illustrious group of backers. In addition to SoftBank they include General Catalyst, Sequoia Capital; Accel; GV; Jay-Z’s Roc Nation; Glade Brook Capital Partners; Will Smith and Robert Downey Jr.

This latest injection of funding — which will be used to hire more people and continue to expand its product set into adjacent areas of insurance life critical illness coverage — was unsolicited, Colis said, but comes on the heels of very rapid growth.

Ethos — which is sold currently only in the U.S. across 49 states — has seen both revenues and user numbers grow by over 500% compared to a year ago, and it’s on track to issue some $20 billion in life insurance coverage this year. And it is approaching $100 million in annualized growth profit. Ethos itself is not yet profitable, Colis said.

There are a couple of trends going on that speak to a wide opportunity for Ethos at the moment.

The first of these is the current market climate: globally we are still battling the Covid-19 global health pandemic, and one impact of that — in particular given how Covid-19 has not spared any age group or demographic — has been more awareness of our mortality. That inevitably leads at least some part of the population to considering something like life insurance coverage that might not have thought about it previously.

However, Colis is a little skeptical on the lasting impact of that particular trend. “We saw an initial surge of demand in the Covid period, but then it regressed back to normal,” he said in an interview. Those who were more inclined to think about life insurance around Covid-19 might have come around to considering it regardless: it was being driven, he said, by those with pre-existing health conditions going into the pandemic.

That, interestingly, brings up the second trend, which goes beyond our present circumstances and Colis believes will have the more lasting impact.

While there have been a number of startups, and even incumbent providers, looking to rethink other areas of insurance such as car, health and property coverage, life insurance has been relatively untouched, especially in some markets like the U.S. Traditionally, someone taking out life insurance goes through a long vetting process, which is not all carried out online and can involve medical examinations and more, and yes, it can be expensive: the stereotype you might best know is that only wealthier people take out life insurance policies.

Much like companies in fintech who have rethought how loan applications (and payback terms) can be rethought and evaluated afresh using big data — pulling in a new range of information to form a picture of the applicant and the likelihood of default or not — Ethos is among the companies that is applying that same concept to a different problem. The end result is a much faster turnaround for applications, a considerably cheaper and more flexible offer (term life insurance lasts for only as long as a person pays for it to), and generally a lot more accessibility for everyone potentially interested. That pool of data is growing all the time.

“Every month, we get more intelligent,” said Colis.

There is also the matter of what Ethos is actually selling. The company itself is not an insurance provider but an “insuretech” — similar to how neobanks use APIs to integrate banking services that have been built by others, which they then wrap with their own customer service, personalization and more — Ethos integrates with third-party insurance underwriters, providing customer service, more efficient onboarding (no in-person medical exams for example) and personalization (both in packages and pricing) around them. Given how staid and hard it is to get more traditional policies, it’s essentially meant completely open water for Ethos in terms of finding and securing new customers.

Ethos’s rise comes at a time when we are seeing other startups approaching and rethinking life insurance also in the U.S. and further afield. Last week, YuLife in the UK raised a big round to further build out its own take on life insurance, which is to sell policies that are linked to an individual’s own health and wellness practices — the idea being that this will make you happier and give more reason to pay for a policy that otherwise feels like some dormant investment; but also that it could help you live longer (Sproutt is another also looking at how to emphasize the “life” aspect of life insurance). Others like  DeadHappy and BIMA are, like Ethos, rethinking accessibility of life insurance for a wider set of demographics.

There are some signs that Ethos is catching on with its mission to expand that pool, not just grow business among the kind of users who might have already been considering and would have taken out life insurance policies. The startup said that more than 40% of its new policy holders in the first half of 2021 had incomes of $60,000 or less, and nearly 40% of new policy holders were under the age of 40. The professions of those customers also speak to that democratization: the top five occupations, it said were homemaker, insurance agent, business owner, teacher, and registered nurse.

That traction is likely one reason why SoftBank came knocking.

“Ethos is leveraging data and its vertically integrated tech stack to fundamentally transform life insurance in the U.S.,” said Munish Varma, managing partner at SoftBank Investment Advisers, in a statement. “Through a fast and user-friendly online application process, the company can accurately underwrite and insure a broad segment of customers quickly. We are excited to partner with Peter Colis and the exceptional team at Ethos.”

How we built an AI unicorn in 6 years

By Ram Iyer
Alex Dalyac Contributor
Alex Dalyac is the CEO and co-founder of Tractable, which develops artificial intelligence for accident and disaster recovery.

Today, Tractable is worth $1 billion. Our AI is used by millions of people across the world to recover faster from road accidents, and it also helps recycle as many cars as Tesla puts on the road.

And yet six years ago, Tractable was just me and Raz (Razvan Ranca, CTO), two college grads coding in a basement. Here’s how we did it, and what we learned along the way.

Build upon a fresh technological breakthrough

In 2013, I was fortunate to get into artificial intelligence (more specifically, deep learning) six months before it blew up internationally. It started when I took a course on Coursera called “Machine learning with neural networks” by Geoffrey Hinton. It was like being love struck. Back then, to me AI was science fiction, like “The Terminator.”

Narrowly focusing on a branch of applied science that was undergoing a paradigm shift which hadn’t yet reached the business world changed everything.

But an article in the tech press said the academic field was amid a resurgence. As a result of 100x larger training data sets and 100x higher compute power becoming available by reprogramming GPUs (graphics cards), a huge leap in predictive performance had been attained in image classification a year earlier. This meant computers were starting to be able to understand what’s in an image — like humans do.

The next step was getting this technology into the real world. While at university — Imperial College London — teaming up with much more skilled people, we built a plant recognition app with deep learning. We walked our professor through Hyde Park, watching him take photos of flowers with the app and laughing from joy as the AI recognized the right plant species. This had previously been impossible.

I started spending every spare moment on image classification with deep learning. Still, no one was talking about it in the news — even Imperial’s computer vision lab wasn’t yet on it! I felt like I was in on a revolutionary secret.

Looking back, narrowly focusing on a branch of applied science undergoing a breakthrough paradigm shift that hadn’t yet reached the business world changed everything.

Search for complementary co-founders who will become your best friends

I’d previously been rejected from Entrepreneur First (EF), one of the world’s best incubators, for not knowing anything about tech. Having changed that, I applied again.

The last interview was a hackathon, where I met Raz. He was doing machine learning research at Cambridge, had topped EF’s technical test, and published papers on reconstructing shredded documents and on poker bots that could detect bluffs. His bare-bones webpage read: “I seek data-driven solutions to currently intractable problems.” Now that had a ring to it (and where we’d get the name for Tractable).

That hackathon, we coded all night. The morning after, he and I knew something special was happening between us. We moved in together and would spend years side by side, 24/7, from waking up to Pantera in the morning to coding marathons at night.

But we also wouldn’t have got where we are without Adrien (Cohen, president), who joined as our third co-founder right after our seed round. Adrien had previously co-founded Lazada, an online supermarket in South East Asia like Amazon and Alibaba, which sold to Alibaba for $1.5 billion. Adrien would teach us how to build a business, inspire trust and hire world-class talent.

Find potential customers early so you can work out market fit

Tractable started at EF with a head start — a paying customer. Our first use case was … plastic pipe welds.

It was as glamorous as it sounds. Pipes that carry water and natural gas to your home are made of plastic. They’re connected by welds (melt the two plastic ends, connect them, let them cool down and solidify again as one). Image classification AI could visually check people’s weld setups to ensure good quality. Most of all, it was real-world value for breakthrough AI.

And yet in the end, they — our only paying customer — stopped working with us, just as we were raising our first round of funding. That was rough. Luckily, the number of pipe weld inspections was too small a market to interest investors, so we explored other use cases — utilities, geology, dermatology and medical imaging.

An insurtech startup exposed thousands of sensitive insurance applications

By Zack Whittaker

A security lapse at insurance technology startup BackNine exposed hundreds of thousands of insurance applications after one of its cloud servers was left unprotected on the internet.

BackNine might be a company you’re not familiar with, but it might have processed your personal information if you applied for insurance in the past few years. The California-based company builds back-office software to help bigger insurance carriers sell and maintain life and disability insurance policies. It also offers a white-labeled quote web form for smaller or independent financial planners who sell insurance plans through their own websites.

But one of the company’s storage servers, hosted on Amazon’s cloud, was misconfigured to allow anyone access to the 711,000 files inside, including completed insurance applications that contain highly sensitive personal and medical information on the applicant and their family. It also contained images of individuals’ signatures as well as other internal BackNine files.

Of the documents reviewed, TechCrunch found contact information, like full names, addresses and phone numbers, but also Social Security numbers, medical diagnoses, medications taken and detailed completed questionnaires about an applicant’s health, past and present. Other files included lab and test results, such as blood work and electrocardiograms. Some applications also contained driver’s license numbers.

The exposed documents date back to 2015, and as recently as this month.

Because Amazon storage servers, known as buckets, are private by default, someone with control of the buckets must have changed its permissions to public. None of the data was encrypted.

Security researcher Bob Diachenko found the exposed storage bucket and emailed details of the lapse to the company in early June, but after receiving an initial response, he didn’t hear back and the bucket remained open.

We reached out to BackNine vice president Reid Tattersall, with whom Diachenko was in contact and ignored. TechCrunch, too, was ignored. But within minutes of providing Tattersall — and him only — with the name of the exposed bucket, the data was locked down. TechCrunch has yet to receive a response from Tattersall, or his father Mark, the company’s chief executive, who was copied on a later email.

TechCrunch asked Tattersall if the company has alerted local authorities per state data breach notification laws, or if the company has any plans to notify the affected individuals whose data was exposed. We did not receive an answer. Companies can face stiff financial and civil penalties for failing to disclose a cybersecurity incident.

BackNine works with some of America’s largest insurance carriers. Many of the insurance applications found in the exposed bucket were for AIG, TransAmerica, John Hancock, Lincoln Financial Group and Prudential. When reached prior to publication, spokespeople for the insurance giants did not comment.

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Miami twins raise $18M for Lula, an insurance infrastructure upstart

By Mary Ann Azevedo

Lula, a Miami-based insurance infrastructure startup, announced today it has raised $18 million in a Series A round of funding.

Founders Fund and Khosla Ventures co-led the round, which also included participation from SoftBank, hedge fund manager Bill Ackman, Shrug Capital, Steve Pagliuca (Bain Capital co-chairman and Boston Celtics owner), Tiny Capital’s Andrew Wilkinson. Existing backers such as Nextview Ventures and Florida Funders also put money in the round, in addition to a number of insurance and logistics groups such as Flexport.

The startup’s self-proclaimed mission is to provide companies of all sizes — from startups to multinational corporations — with insurance infrastructure. Think of it as a “Stripe for insurance,” its founders say.

Founded by 25-year-old twin brothers and Miami natives Michael and Matthew Vega-Sanz, Lula actually emerged from another business the pair had started while in college.

“We couldn’t afford to have a car on campus and wanted pizza one night,” Michael recalls. “So I thought it would be cool if there was an app that let me rent a car from another student, and then I thought ‘Why don’t we build it?’ We then built the ugliest app you’ve ever seen but it allowed us to rent cars from other people on the campus.” It was the first company to allow 18-year-olds to rent cars without restrictions, the brother say.

By September 2018, they formally launched the app beyond the campus of Babson College, which they were attending on scholarships. Within eight days of launching, the brothers say, the app became one of the top apps on Apple’s App Store. The pair dropped out of college, and within 12 months, they had cars available on more than 500 college campuses in the United States.

“As you can imagine we needed to make sure there was insurance coverage on each rental. We pitched it to 47 insurance companies and they all rejected us,” Michael said. “So we developed our own underwriting methodologies or underwriting tools into the operations and had the lowest incident rate in the industry.”

As the company grew, it began partnering with car rental providers (think smaller players, not Enterprise, et al.) to supplement its supply of vehicles. In doing so, the brothers soon realized that the most compelling aspect of their offering was the insurance infrastructure they’d built into it.

“Our rental companies begin to put a significant portion of their business through our platform, and one day one called us and asked if they could start using the software in the insurance infrastructure we’d built out in the rest of our business.”

That was in early 2020, right before the COVID-19 pandemic hit.

“At that moment, we began to realize, ‘Hey maybe the big opportunity here is not a car-sharing app for college students, but maybe the big opportunity here is something with insurance,’” Michael said.

A few weeks later, the duo shut down their core business and by April 2020, they pivoted to building out Lula as it exists today.

“In the same way that Stripe has built a payment API that eliminates the need for companies to build their own payment infrastructure, we decided we could build an insurance API that eliminates the need for companies to build their own insurance infrastructure,” Matthew said. “Companies would no longer need to build out internal insurance systems or tools. No longer would they need to deal with insurance brokers to procure them coverage. No longer would they need to deal with insurance teams. We can integrate on to a platform and handle all things insurance for companies and their customers via our API.”

By August of 2020, the company launched an MVP (minimum viable product) and since then has been growing about 30% month over month after reaching profitability in its first four months.

Image Credits: Lula

Today, Lula offers a “fully integrated suite” of technology-enabled tools such as customer vetting, fraud detection, driver history checks, and policy management and claims handling through its insurance partners. It has a waiting list of nearly 2,000 companies and raised its funding to fulfill that demand.

“The main purpose for raising capital was so we can build out the team necessary to fulfill demand and sustain growth moving forward,” Matthew said. “And apart from that, we also just want to further develop the technology — whether it be in the ways that we’re collecting data so we can get more granular and make smarter decisions or just optimizing our vetting system. We’re also just working toward developing a much more robust API.”

Existing clients include ReadyDrive, a car-sharing program for the U.S. military and a “ton of SMBs,” the brothers say. Investor Flexport will be conducting a pilot with the company.

“Every time a trucker picks up a load or delivery, instead of paying monthly policies, they will be able to pay for insurance for the two to three days they are on the road only,” Michael says. “Also, if someone is shipping a container via Flexport, they can add cargo coverage at the point of sale and get an additional layer of protection.”

Ultimately, Lula’s goal is to act as a carrier in some capacity.

Founders Fund’s Delian Asparouhov believes that the way millenials and Gen Zers utilize physical assets is “wildly different” than prior generations.

“We grew up in a shared economy world, where apps like Uber, GetAround, Airbnb have allowed us to episodically utilize assets rather than purchase them outright,” he said.

In his view, though, the insurance industry has not picked up on the massive shift.
“Typical insurance agents both don’t know how to underwrite episodic usage of assets, and they don’t know how to integrate into these typical of digital rental platforms and allow for instantaneous underwriting,” Asparouhov told TechCrunch. “Lulu is combining both of these technologies into an incredibly unique approach that digitizes insurance and gives us flashbacks to how Stripe disrupted the digitization of payments.”

Despite their recent success, the brothers emphasize that the journey to get to this point was not always a glamorous one. Born to Puerto Rican and Cuban parents, they grew up on a small south Florida farm.

“We started our company out of our dorm room and initially emailed 532 investors only to get one response,” Michael said. “Founders just see the headlines but I just want to advise them to stay persistent and really keep at it. I’m not afraid to share that the company started off slow.”

Insurtech AI startup Akur8 closes $30M Series B

By Mike Butcher

Automating insurance claims is a big business, and the world of AI is coming at it ‘full pelt’. The latest is Akur8, an insurtech automating insurance platform whose ‘Transparent AI’ product is trying to eat into the incumbent large business of Willis Towers watson, among others.

It’s now closed a Series B funding round of $30m led by an undisclosed investor. This brings its total funding to $42m. The round is to support international expansion.

Akur8 is used by actuaries and pricing teams to make faster decisions about insurance claims.

Customers include AXA, Generali, and Munich Re, specialty insurers Canopius and Tokio Marine Kiln, insurtechs Wakam and wefox, as well as mutualistic player Matmut.

Samuel Falmagne, co-founder and CEO of Akur8 commented: “We are happy to announce the closing of our Series B funding round and are grateful for the support we have seen from our investors. This latest milestone will enable us to accelerate the transformation of insurance pricing even further, fuel our international expansion in the US and APAC, and equip P&C and health carriers with a state-of-the-art, integrated pricing solution that we have been building and refining tirelessly.”

Julien Creuzé, Partner at BlackFin Capital Partners Said: “The BlackFin team is thrilled to see Akur8 continue to spread its wings and deploy its next-generation pricing platform across insurance carriers worldwide. We have built a great relationship with the Akur8 management team and it’s a pleasure to welcome new investors and continue this journey with them.”

Tractable raises $60M at a $1B valuation to make damage appraisals using AI

By Ingrid Lunden

As the insurance industry adjusts to life in the 21st century (heh), an AI startup that has built computer vision tools to enable remote damage appraisals is announcing a significant round of growth funding.

Tractable, which works with automotive insurance companies to let users take and submit photos of damaged cars that are then “read” to make appraisals, has raised $60 million, a series D that values Tractable at $1 billion, the company said.

Tractable says it works with more than 20 of the top 100 auto insurers in the world, and it has seen sales grow 600% in the last 24 months, which CEO Alex Dalyac told me translates as “well into 8 figures of annual revenue.” He also told me that “we would have grown even faster if it weren’t for Covid.” People staying at home meant far less people on the roads, and less accidents.

Its business today is based mostly around car accident recovery — where users can take pictures using ordinary smartphone cameras, uploading pictures via a mobile web site (not typically an app).

But Tractable’s plan is to use some of the funding to expand deeper into areas adjacent to that: natural disaster recovery (specifically for appraising property damage), and used car appraisals. It will also use the investment to continue building out its technology, specifically to help build out better, AI-based techniques of processing and parsing pictures that are taken on smartphones — by their nature small in size.

Insight Partners and Georgian co-led the round and it brings the total raised by the company to $115 million.

Dalyac, a deep learning researcher by training who co-founded the company with Razvan Ranca and Adrien Cohen, said that the “opportunity” (if you could call an accident that) Tractable has identified and built to fix is that it’s generally time-consuming and stressful to deal with an insurance company when you are also coping with a problem with your car.

And while a new generation of “insuretech” startups have emerged in recent years that are bringing more modern processes into the equation, typically the incumbent major insurance companies — the ones that Tractable targets — have lacked the technology to improve that process.

It’s not unlike the tension between fintech-fuelled neobanks and the incumbent banks, which are now scrambling to invest in more technology to catch up with the times.

“Getting into an accident can be anything from a hassle to trauma,” Dalyac said. “It can be devastating, and then the process for recovery is pretty damn slow. You’re dealing with so many touch points with your insurance, so many people that need to come and check things out again. It’s hard to keep track and know when things will truly be back to normal. Our belief is that that whole process can be 10 times faster, thanks to the breakthroughs in image classification.”

That process currently also extends not just to taking pictures for claims, but also to help figure out when a car is beyond repair, in which case which parts can be recycled and reused elsewhere, also using Tractable’s computer vision technology. Dalyac noted that this was a popular enough service in the last year that the company helped recycle as many cars “as Tesla sold in 2019.”

Customers that have integrated with Tractable to date include Geico in the U.S., as well as a large swathe of insurers in Japan, specifically Tokio Marine Nichido, Mitsui Sumitomo, Aioi Nissay Dowa and Sompo. Covéa, the largest auto insurer in France, is also a customer, as is Admiral Seguros, the Spanish entity of UK’s Admiral Group, as well as Ageas, a top UK insurer.

Japan is the company’s biggest market today Dalyac said — the reason being that it has an ageing population, but one that is also very strong on mobile usage: combining those two, “automation is more than a value add; it’s a must have,” Dalyac said. He also added that he thinks the U.S. will overtake Japan as Tractable’s biggest market soon.

The new directions into property and other car applications will also open the door to a wider set of use cases beyond working with insurance providers over time. It will also bring Tractable potentially into new competitive environments. There are other companies that have also identified this opportunity.

For example, Hover, which has built a way to create 3d imagery of homes using ordinary smartphone cameras, is also eyeing ways of selling its tech (originally developed to help make estimates on home repairs) to insurance companies.

For now, however, it sounds like the opportunity is a big enough one that the race is more to meet demand than it is to beat competitors to do so.

“Tractable’s accelerating growth at scale is a testament to the power and differentiation of their applied machine learning system, which continues to improve as more businesses adopt it,” said Lonne Jaffe, MD at Insight Partners and Tractable Board member, in a statement. “We’re excited to double down on our partnership with Tractable as they work to help the world recover faster from accidents and disasters that affect hundreds of millions of lives.”

Emily Walsh, Partner at Georgian Partners added: “Tractable’s industry-leading computer vision capabilities are continuing to fuel incredible customer ROI and growth for the firm. We’re excited to continue to partner with Tractable as they apply their artificial intelligence capabilities to new, multi-billion dollar market opportunities in the used vehicle and natural disaster recovery industries.”

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