Insurtech has its first rollup as Knip and Komparu merge to create Digital Insurance Group.


An exit is when the rubber meets the road. All valuations up to that point are only on paper. So we like to track exits to see what they tell us about broader trends. Knip, one of the well known Swiss Fintech ventures, was recently acquired by Digital Insurance Group. It is also an “exit” for a Dutch venture called Komparu.

If you have never heard about Digital Insurance Group, don’t feel bad. It was formed from the merger of Knip and Komparu. The terms of the deal were not disclosed (which can mean it was not very much). It reads like the creation of a platform for a rollup and we believe it is the first rollup in Insurtech.

This does not seem like a cash out exit (which is why I call it an “exit” in quotes). It is probably more like a postponed exit. To get scale, a couple of ventures are brought together and the bigger entity can raise more money to do more acquisitions. In short this is the initial platform for a rollup. It makes sense as a strategy. One can find equivalents of both Knip and Komparu in virtually every market. Most of the things they need to do are the same in  each market. Local nuance matters of course but it is likely to be 90% standard and 10% local (like changing the menu slightly in a fast food chain to accommodate local tastes).

It also makes sense because Knip is what we call a Robo Broker and Komparu  is more of a comparison engine. A Robo Broker needs a comparison engine so the two are complementary. Comparison engines are easy to build and market but have low barriers to entry. So the combination makes sense.

Mergers always create a problem of who will be boss. It looks like they resolved that.   Dennis Just will step down as CEO of Knip, while Komparu’s Roeland Werring will become Group CTO and Ruben Troostwijk remains CEO of Komparu, focusing on B2B business opportunities as well as launching Knip in the Netherlands. Ingo Weber becomes the new Group CEO of the Digital Insurance Group. Ingo Weber comes from the VC world. He seems like the architect of the rollup strategy. He comes with a strong background in insurance, technology and business building.

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Bernard Lunn is a Fintech deal-maker, investor and thought-leader.

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XBRL creates a new equities research value chain & democratizes the stock market

Democratized data

This is Part 2 of a 2 Part Research Note, where we look at how XBRL could enable a new equities research value chainIn Part 1, on Wednesday, our Research Note described how regulation and market pressure is forcing transparency on equities research and how that is leading to cost pressures. Our thesis is that the only way out is to reduce cost via automation and that is where XBRL is ready for its prime time. A new breed of micro research firms will enter the market opened up by regulation and empowered by XBRL. They will start by solving a huge pain point in small cap equities research. There are investing bargains to be had in small cap. On the other side of the table the small cap companies have been neglected; they are in “small cap hell” and want to be discovered by investors. New value chains will form around new business models that use XBRL automation.  

XBRL 101

  • XBRL stands for eXtensible Business Reporting Language.
  • It is an open standard based on XML, created by an accountant named Charlie Hoffman.
  • If you tag content consistently, software applications can more easily analyze the data and present more useful information. Think of XBRL like a barcode on financial statements. Yes, that sounds like the Semantic Web, and we all know that the Semantic Web has faced the chicken-and-egg problem (i.e. not enough content is semantically tagged yet and so developers don’t create tools to parse the semantic data). Imagine a Semantic Web standard where governments around the world tell companies that they must tag data that way. That is XBRL
  • XBRL gained attention when the SEC in the US in 2009, mandated that public companies report their financial results in XBRL.
  • The SEC XBRL mandate motivated almost every major market around the world to follow suite.
  • XBRL is being used across almost every asset class. In this post we focus only on equities.

For more research, go to

It was this superb article in Wired called Radical Transparency in the dark days of February 2009 in the thick of the Global Financial Crisis that first got me excited about XBRL.

Two reasons why XBRL will move to prime time in 2016

  • # 1: High Frequency Trading killed the Day-Trading business. When speed of trading is the game, computers will always beat humans. When you work on small gains every day, the front running done by HFT hurts. So there are a lot of retail investors looking to find the next game, looking for new ways to make money in the stock market. The winners in the market will now be stock-pickers who can go either Long or Short based on fundamental analysis.
  • #2: The XBRL rollout on US stocks is now complete. So we can move from the production phase of XBRL (all costs, no benefits) to the analysis phase (lots of benefits, very low costs).

XBRL is one of our Top 10 Fintech Predictions for 2016:

“XBRL will start climbing out of the slough of despond but won’t be recognized yet as moving into the plateau of productivity.”

Three reasons why XBRL fell into the slough of despond

It has been 7 years since XBRL was mandated by the SEC and got public attention. That is too long in our attention deficit world. We see the same thing with something as complex as Bitcoin, which is also about 7 years old, that is losing attention because the results have not been quick enough. There are also three reasons specific to XBRL why it fell into the slough of despond:

  • # 1: XBRL is a complex story that requires an understanding of both technology and accounting.
  • #2: The XBRL production process took longer, cost more and was more error-prone than forecast. In the production phase it was all costs and no benefits. It was all problems and no results.
  • # 3. There have not yet been any success stories for journalists to write about.

Why you will continue to hear these negative stories

XBRL democratizes fundamental stock analysis in the same way that PCs democratized computing and social media democratized HTML.

When you can compare the cash holdings across 100 companies with a single entry in your spreadsheet, or see quarter to quarter revenue growth for every company in the index that you devised, do you need mega brokers and their me too reports churned out by freshly minted MBA graduates in an outsourcing center somewhere?

That is disruptive to the current financial value chain (aka Wall Street). Those threatened by disruption always point out the flaws in a new technology and in all new technology there are plenty of flaws (XBRL is no exception).

When you see issues with XBRL, remember how klunky the Internet was in 1994 (cue sound of a screeching dial up modem). When something promises to automate a task 100% and only achieves 90%, it is easy to point out the flaws that meant 100% was not achievable. The reality shouting at us is that 90% automation is game-changing. The firms who mastered the business of deploying people to do things manually (i.e the threatened incumbents) have the money and the motivation to convince you that this will never work.

Those who have been toiling in the XBRL world know that the data is still not totally reliable. So, they will be defensive when they hear the incumbents talk about all the problems with XBRL. True there are problems with XBRL, but it is still way better than what we have now (manually cutting and pasting from HTML into Excel). To the retail investor, finding a data problem is an opportunity. They have the time. If you use XBRL to screen 100 stocks based on some parameter and end up with 3 that “look interesting”, you can easily eyeball those 3 on your short-list to spot where the data errors might be or why a stock that looks crazily undervalued based only on the numbers is still a lousy stock to buy. Automated analysis of the basics enables more time for value added analysis (“Alpha” in Hedge Fund speak).

New technology usually gets a foothold in a market that has major pain where the market participants will over look something klunky because the need is so great. This leads us to looking at the “small cap hell” pain point.

Getting out of “small cap hell”

When I was writing the SAAS Insights Report in 2010, only Big Caps (over $2bn market cap) had to report in XBRL. Now I can get all the SAAS stocks in XBRL; or I can get all Biotech, Oil, Property, e-commerce stocks, financials/Fintech. I can get whatever is the focus of my research. Then I can set up automated screening programs to find the investing gems (long or short) in those markets. Those screens are not just based on something as crude as price or PE or PSR. They are based on data that you can only get by digging deep into the 10Q statements and – this is what matters – knowing what you are looking for because you understand the market that you are researching.

Today, Small Cap stocks are in “Small Cap Hell”. When your market cap is below $2bn, mutual funds cannot buy your stock unless the Fund is specifically mandated to buy Small Cap Stocks.

It is worse than that. Since ETF trading took off, fundamental research declined even more. About 75% of stocks now have no research. Only Mega Caps or recent IPOs get research on a consistent basis.

When everything changes, one law that remains constant is the law of supply and demand. When all of the research supply goes to a few stocks and the demand is screaming from the rest, there is a window of opportunity.

So, XBRL needs stories of a couple of geeky young Warren Buffet like investors to make a fortune this way. That will get them onto CNBC or Fortune or Business Insider. A good reporter with a nose for the story will ask “how did you do it?” and that is where the newly-rich investors will go against their best interests and reveal that XBRL analysis tool they used.

Investors win in inefficient markets. Small Cap is an inefficient market. Here is a report from Credit Suisse that shows why trading in Big Caps is a mugs game for retail investors, but how there are big opportunities if you can quickly find the undervalued/overvalued small cap stocks. The TL:DR summary of that Credit Suisse report:

“Shares in small and mid cap companies are, for the most part, less liquid than those of large caps. The typically higher risk of an investment in small and mid cap stocks is reflected in higher risk and liquidity premiums and has a positive effect on performance over time.”

In other words, Small Cap stocks are ideal hunting ground for individual retail investors. With the small sums they trade, the lack of liquidity is less of an issue.

Qui Bono and Qui Amisit?

Translation – who wins and who loses?

  • Private Equity Funds – lose a bit. They are currently doing very well in an inefficient market. They can afford to have analysts screen for small cap value and take them private. As other investors figure out how to do this – meaning the market becomes efficient and small cap bargains go up in price – the easy arbitrage will be gone. PE funds have many other ways to make money; this is just some easy-pickings that will go.
  • Small Cap Funds – gain a bit. Their existing holdings will go up in value. They will have to work harder to find bargains in future but as that is what they do for a living, they will figure out how to add value in the new value chain.
  • Me Too Research – lose a lot. Investors will demand a lot more value and insight. Me Too Research, even with lots of fancy charts and visualization, will be downgraded.
  • New investing services – gain a lot. There is room for a new Yahoo Finance (free, ad supported) or a new Bloomberg or a new (pick your favorite subscription based service). This is a high risk/high reward game that is subject of a future Daily Fintech Research Note. The winners will be distribution channels for the Micro Research Firms (see below).
  • Micro Research Firms – gain a lot. This will be like the blogging revolution. Suddenly everybody can be a research firm. All you need is insight; the mechanical part is automated. I call this latent alpha. For example, somebody with decades of experience as an oil geologist might have valuable insight into the oil business, but lacked any way to monetize that insight.
  • Jane Q Public – gain the most. Some will become Micro Research Firms. Most will simply invest smarter. This is how the stock market was supposed to work. This will be a democratized stock market that will help to reduce inequality. Wall Street is based on information asymmetry. It is like a car dealer with all the data. That era is coming to an end.

Daily Fintech Advisers provide strategic consulting to organizations with business and investment interests in Fintech. Bernard Lunn is a Fintech thought-leader.

Landscape Report: XBRL Analytics

This is the first Landscape Report from the Fintech 1,000 database.

This Landscape Report demonstrates the difficulty of using a rigid taxonomy. The names given to a “space” (VC speak) or a “segment” (analyst speak) evolve over time and the most successful ventures emerge before anybody has given a name to what they do. Entrepreneurs don’t enter a space/segment; they solve problems and, if those are big problems that a lot of people have, somebody then gives a name to what they are doing and that then becomes a space/segment. Ideally you control the messaging enough that you invent the name that defines what you created. I describe this approach – define the market that you want to lead – in my book Mindshare to Marketshare.

The first phase of XBRL was all about the people, process and technology needed to produce XBRL content.

The next phase of XBRL is where it gets interesting. This is when we see the tools to consume XBRL content. This is what I call XBRL Analytics.

Where does XBRL Analytics fit within a Fintech taxonomy? Here are some choices:

  • Low Cost Active Alpha (within a broader Personal Financial Management space). This is where I tend to put XBRL Analytics, but I understand that it is a quirky name and probably won’t catch on.
  • Analytics or Big Data. This has become so broad and hype-driven as to be meaningless today. It begs the question “what do you want to analyze and why?”
  • Equities (within a broader Capital Markets space). This fits because most XBRL is used to analyze publicly listed stocks. However XBRL has applicability to Fixed Income and Private Equity as well.

That is why I use tagging as well as taxonomy to do custom searches on the Fintech 1,000 database. I never assume I will have a perfect taxonomy or a complete database. Fintech 1,000 is always a pragmatic work in progress tool.

XBRL Analytics could be used by any of the following:

  • Individual Investor.
  • Hedge Fund analyst.
  • Private Equity analyst who is looking for take private opportunities.
  • Investment Banker who is looking for M&A or IPO comparables.

Where it gets interesting and disruptive is when that humble Individual Investor creates something like a Syndicate or a Motif or a Wikifolio. Now the lines between Individual/Retail and Institutional/Professional start to get blurred. Entrepreneurs love blurry intersections because they signal opportunities that are overlooked by established players.

XBRL is disruptive because:

“XBRL democratizes fundamental stock analysis in the same way that PCs democratized computing and social media democratized HTML. When you can compare the cash holdings across 100 companies with a single entry in your spreadsheet, or see quarter to quarter revenue growth for every company in the index that you devised, do you need mega brokers and their me too reports churned out by freshly minted MBA graduates in an outsourcing center somewhere?”


That is why XBRL Analytics is important.

I am seeing 5 different types of companies moving into XBRL Analytics:

  1. Use XBRL Analytics “under the hood” to deliver actionable information to investors such as:

Capital Cube The interview with Larry King is a good intro.

Market Realist. Their tag line is “democratizing investment management”.

StocksForTheWeek. They focus on discovery of undervalued small cap stocks, which is the natural use case for XBRL, because it is a way for the Small Cap to escape the valuation trap known as “small cap hell” and for the small investor to find the bargains overlooked by the big guys.

  1. BI (Business Intelligence) vendors using XBRL. An interesting one to watch is Tableau because they are innovators with scale in BI (successful IPO in 2013, market cap over $6 billion). IBM is the behemoth in BI and they seem to be adding XBRL capability to a lot of their products. SAS are doing the same. When companies like Oracle and IBM embed XBRL into accounting systems, analysts can merge internal and external data.
  1. XBRL vendors that started in XBRL creation:

Rivet There are a lot of “devil in the details” of XBRL creation, so understanding these should make one better at the analytics part.

  1. Pure Play XBRL Analytics tools.



Thinknum. They have an interesting “model sharing” model (share your model in the free version, keep it private in the paid version; a bit like GitHub).


Silqe. They are still in Beta, so I am not sure if positioning is correct.

  1. New data technology. Data that is semantic and real time may need something other than RDBMS. I only found one company in this category, which is 28IO. You can see an example of their output at

The best expert in the XBRL Analytics space is Jim Truscott who writes a good blog here.

It is interesting to see a stack emerging. At a low level we have companies like 28io with new database technology to power real time analysis of large semantic data sets. At a user level we have companies like Market Realist and StocksForTheWeek that abstact away the XBRL layer to deliver actionable information. For financial analysts who want more control there are services such as Thinknum, Contexxia and FinDynamics.

I hope this is useful. Please tell me in comments if I have missed any innovators.

Yodlee IPO $YDLE the picks and shovels of Fintech

Yodlee is one of four in my Fintech IPO watch list and the first to actually go public.

The much bigger Lending Club IPO is next on the calendar.

However Yodlee is first out of the gate.

Here are the key facts:

  • Consumers don’t know Yodlee, but developers and entrepreneurs and Bankers are quite familiar with Yodlee. This is a white label service. That is why I describe this as the “picks and shovels” of Fintech. You use Yodlee to retrieve data from multiple banks for applications such as Personal Financial Management (PFM).
  • Founded in 1999, by mostly ex Microsoft guys, so the white label platform play makes sense. This was before social media and mobile made it so cheap to reach consumers directly and the biggest tech successes such as Microsoft had been platforms, so a white label strategy made sense.
  • Revenue in 2013 was $70.2m, 21% increase on 2012, small loss of $1.2m. Not massive growth but enough to hit profitability pretty soon.
  • Built into their customer’s processes so revenue visibility is good. Partnered with Y Combinator so they get a mix of high potential startups with steady bank customers.
  • 14% of revenue from Bank of America which is also a shareholder.
  • Headline says, “soared 45%”, but then it dropped back to 12% up on the day. Sounds like a reasonable start. This week will show what Mr. Market’s verdict is on Yodlee.

I have no idea yet whether Yodlee is a good or bad valuation, I will be digging into that later.