Author Reuben

Overview of P2P Automation and Analytics Sites

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I wrote earlier this week about some of the institutional analytic and connectivity tools that are emerging as the crowdfunding and P2P markets mature. There is a nice summary of similar tools aimed at individuals and small funds investing in P2P lending platforms: \Overview of P2P Automation and Analytics Sites.

“…Analytics and data has been a large part of p2p lending from the start and investors are always looking for an advantage to potentially maximize returns. Investment automation allows for a much better investment experience versus selecting notes on the platform by hand”…

The next generation of financial services data and analytics

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I was recently discussing with someone what data and analytics in capital markets might look like in the future. Will it be about bigger and bigger data sets, or something fundamentally different? I am referring to data and analytics that help client-facing financial services professionals make decisions, whether they are engaged in investing, lending, M&A or other capital markets transactions.

As an example, I used to pitch acquisition ideas to large enterprise software companies. The process to decide “what targets to pitch” works something like this:

Understand context: Meeting with CEO and head of M&A. We think they may be interested in the marketing automation space. Based on their prior deals and current position, they would consider sub $100M targets.

Data gather and compile: This set of tasks is usually handed to a lucky junior associate or analyst. It requires going to “trusted sources” of information such as private company databases, third party research providers, colleagues, and maybe doing some Google searches. Dump every company name into a spreadsheet.

Filter, prioritize and apply intelligence: This is the hardest and most time consuming part. Usually it involves taking the long list of targets, gathering even more information (e.g. company description, revenue, investors, growth) and drawing inferences about the suitability of the target based on the patchwork of data that has been collected. The career ending move at this stage is missing something big — such as including a company that was already acquired or filtering out the “obvious” idea.

Today there are now fintech startups applying technology to make this sort of decision making (whether the question is “what targets to pitch”, “what stock to buy” or “who to extend credit to”) much more intelligent. Many people discuss these firms as part of “Big Data”, but I think it is more interesting to consider how the applications are different rather than the underlying technology.

With my example, a company like Data Fox has developed a very cool way of helping someone identify the leading companies in the tech sector. At its heart, it is about mashing up a lot of different data sources, applying algorithms to identify relationships between companies (clusters), and visualizing the results in a way that delivers insight.

There are a number of dimensions which make this “new” data and analytic approach different than traditional ones:

  • Contextual. The traditional approach focuses on a defined data set and emphasizes breadth (e.g. number of companies or fields). The new approach focuses on the context of the business problem. I probably don’t need to include 100 acquisition targets in a 45 minute pitch, but it would be great to pick the 10 that fit best based on relevance. Presenting me funding rounds, investors, company description and success metrics (growth in employees, website traffic) is way more insightful than a laundry list of names.
  • Structured and unstructured. The traditional approach focuses on building large, proprietary structured data sets. New approaches will combine both structured and unstructured data, from a lot of different sources. Individuals leave their digital footprint all over the internet, particularly through social media. Businesses have a similar digital footprint that can be pieced together through their online presence and interactions, as well as individuals talking about those businesses online. Combining this data with intelligent natural language processing delivers tremendous insight.
  • Inferential. The traditional approach leaves the consumer of the data to apply intelligence. The new approach will draw inferences for you. Understanding the relationships between companies (e.g. comparable sector, shared board members, competitive products) is hugely valuable. This is not based on a rigid hierarchy or classification system, but rather machine learning.
  • Evolving. When the approach to delivering analytics shifts to context and inference, it also moves from being static to evolving. It is not about incrementally expanding and updating a single data set, but rather continually refining and combining multiple data sets. The analysis evolves organically in the same way that a manual search for the leading fin tech companies today leads to a different same of names, relationship and data than the same search did 6 months ago.

While I’ve focused on the problem of “what targets to pitch” in the context of private company analytics, there are many decisions in financial services that are complex and will benefit from these new approaches. To name a few, credit scoring and evaluation, stock selection, trading signals, sourcing liquidity in fragmented markets, marketing of financial services. Many of these areas have already seen one or more fintech startups (e.g. Zest, Dataminr, Kensho). I think we are still early in the development cycle.

(Kareem Hamady contributed to this post)

The Next Big Thing in Crowdfunding

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Next Big Think In CrowdfundingThe rapid growth in crowdfunding platforms is remarkable. As the market matures, some key trends are emerging which I think have important implications for future areas of growth.

Fragmentation.  With over 600 platforms (Crowdsourcing.org) today, the market has become highly fragmented. Pick any segment — equity, loans, real estate — and you’ll find multiple platforms.

Institutionalization.  Crowdfunding and P2P platforms are increasingly turning to institutional capital sources to provide liquidity and scale. This includes investor groups, “institutional” investors that have raised funds, and banks (recent articles here and here).

Electronification. As participants have become more sophisticated, so too have the platforms. Electronic access through APIs, both for data analysis and investment, have become prevalent.

When you combine Fragmentation, Institutionalization and Electronification the future looks quite interesting.

  • Investment analysis becomes much more complex. Relying on the tools of one platform are not longer sufficient. Institutional investors need to bridge marketplaces. They want an aggregated view of where the “market” is for a particular security, loan or asset. They want to dissect relative value across platforms. They want to consume and analyze large sets of data using standardized or proprietary analytics.
  • Connecting to multiple platforms, to either consume data or submit investment requests, becomes a pain point. Connections are costly to setup and maintain. By themselves they provide little competitive advantage (for now at least). Participants would much prefer to deal with a single connection point.
  • Electronic trading becomes a capability for potential differentiation. Given the small investment amounts and desire to redeploy capital, institutional investors need tools to enable them to invest in an efficient, rule-based approach that leverages APIs that are now available. As these electronic capabilities mature, I would expect more advanced algorithmic trading to increase. While some participants may prefer to build these capabilities from scratch, I expect others will prefer to license a platform and invest in developing trading strategies on top of it.

Helping accelerate these developments is the desire for crowdfunding sites to grow their marketplaces as quickly as possible. In general, they appear to have adopted an approach which is open to the broader ecosystem, and does not discriminate based on type of investor or trading style.

As a result, I see a tremendous opportunity for “second order” crowdfunding players that are focused on providing data, analytics, connectivity services and electronic trading platforms. There are already some notably companies addressing these needs (e.g. Crowdnetic, LendingRobot). I expect we’ll see many more in the future.

More effective deal pipeline management for angels and investors

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Deal Flow ToolsAngelList and the multitude of other crowdfunding platforms have democratized early stage investing. They’ve also created a real pain point to efficiently identify, manage and track deals across all platforms, as well as directly sourced opportunities. I recently did a quick survey of deal pipeline tools for myself and thought I’d share what I found. Keep in mind there are other tools focused on traditional CRM — what’s below are the ones that put dealflow at their core.

1. Gust (www.gust.com): Seems to be the most widely referenced tool for sourcing and managing deals. Includes deal database, deal tracking workflow, as well as collaboration with other team members and investors. Pricing requires a demo and depends on size of the enterprise. Better suited for angel groups or VCs, but there is Gust for individual investors coming soon (yay!) (signup: https://gust.com/signup/investors)

2. Proseeder (http://proseeder.com/): Good if you are involved in syndication (multiple SPVs) and need investor communication and document sharing. Pricing requires a demo and depends upon size of enterprise.

3. Streak (https://www.streak.com/): If you use gmail as your primary communication and task management tool, then Streak has a great deal flow management workflow centered around Gmail. Allows you to aggregate emails into deals, track stages and queue follow-up emails. Also lets you share deals with others via Google’s API.

4. Xenapto (http://xenapto.com/): This company is still early in development, but what I like is that it quickly pulls in information about a company in your pipeline. So while it is built around contacts, it automates a lot of the workflow that would go into a spreadsheet as you gather information on a company initiating a raise…. And it’s free (at least for now).

5. DealFlow (https://mydealflow.com/): Aimed more at established VC firms that want a customized solution.

6. Venture360 (https://venture360.co): This does a lot more than just deal pipeline management, including fund management (capital calls), portfolio management (cap table tracking, investor portal) and investor management. A lot of horsepower, though the basic package starts at only $40/mo for 20 users.

7. Hubble (http://hubble.disruption.vc/): Aimed at angel groups, incubators and small funds.

Here are some CRM tools that could be adapted to use for dealflow management, particularly for individual angels or small groups:
RelateIQ (https://www.relateiq.com/)
Pipedrive (https://www.pipedrive.com)
Base (https://getbase.com/)
Nimble (http://www.nimble.com/)

Why there’s not much “capital markets” in fintech

Reuben 1 Comment

NYSE

The folks at FinTech Collective posted an interesting comment about the lack of “invest tech” within the fintech landscape. By “invest tech” they mean technology that serves investment managers. Having spent quite a few hours mapping the fintech landscape, I’d go further by saying there are not many new “capital markets” startup companies out there serving financial institutions generally, at least relative to the large numbers of payment, wealth management, bitcoin, P2P marketplaces, etc that seem to be attracting the bulk of venture funding.

A few observations about why the institutional space is different:

  • Sales cycles to banks and buyside firms are often long, involve larger budgets and multiple decision makers (IT and business). The POC process alone could take a startup months or years.
  • Banks have been cutting costs, and consolidating systems, not looking for taking a risk with new vendors (in NY at least this has benefited the startup community in that many founders and tech teams have spun out of these institutions). Buyside firms have fared better, though last year was one of the worst for hedge fund launches.
  • Many markets are quite “mature” from a technology perspective — e.g. equity exchanges had their renaissance in response to regulatory changes (Reg NMS) around 2005. There was a flurry of innovation — new electronic market places, connectivity providers, low latency providers (hardware, software, service) — that is now history. Similarly, trading platforms have generally been moving cross-asset and consolidating (OMS with EMS, front-office to back-office, etc).

It’s not all doom and gloom. Read any of the institutional-focused research and you’ll hear of a number of trends that could drive startup opportunities: regulatory change and new compliance requirements, changes in market structure (e.g SEF ecosystem), outsourcing of bank functions (e.g. KYC).

It may take some time to see more startups take root — the “core banking” and payments landscape was pretty slow for a period of time too. Given the proximity to so many financial institutions, NY-based startups have an incredible opportunity as some of these trends start to take hold.

And hopefully capital will start to move more broadly.

Disclaimer: The views expressed on this blog are mine alone.

An open API standard for banks

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The UK government has issued a call for comment on the establishment of an open API standard across UK banks. This follows a report last year, Data Sharing and Open Data for Banks, which discussed the benefits of such a policy.

The UK ambition is quite remarkable:

“..the government wants to go beyond how APIs are currently used in other countries, to deliver an open API standard in UK banking. An open API standard would entail UK banks developing a single and common API, which is publicly available and can be used by any fintech firm or app developer to design products or apps which work for all UK banks.”

— Open Consultation (01/28/15)

Assuming all the privacy, security and industry cost concerns could be addressed, this would remove a huge barrier that firms face when initially starting up — how to get access to data without bespoke integration and lengthy negotiations.

There are many personal finance, expense management and comparative banking products in existence today (see FinTech Market Map) that would stand to benefit.

Of course, common standards sound good on paper but will need to resolve several issues:

  • who sets the standards?
  • how are the standards going to evolve over time?
  • who bears the initial cost and maintenance cost?
  • who gets to monetize the data, and its derivations?

It will be interesting to see if similar initiatives gain traction in other markets, and if the momentum continues or ends up falling short.

M&A marketplaces: what it takes to be successful

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DealmakingI’ve recently been looking at companies that are building marketplaces to help match buyers and sellers of private companies (e.g. IntraLinks DealNexus, Axial Market, ExitRound, Mergerdeals, Dealgate, BizBuySell). I’ve spent most of my professional career doing M&A, and I find it fascinating to consider what will separate the winners from the losers and how they could change the deal process for practitioners.

There is no doubt the M&A market is inefficient. There is a large number of private companies (5 million in the US alone with fewer than 1,000 employees according to the Census Bureau). There is no easy way for business owners to identify which companies might be looking to acquire (or more importantly, looking for someone like them). And it is extremely difficult for strategic buyers and private equity firms to actively monitor more than a few hundred firms effectively.

So why shouldn’t there be a Match.com that makes it easier to transact?

There should. But there are important differences between private company M&A transactions and other types of transactions that have moved online.

Heterogeneous market. Unlike other financial transactions involving standardized products, there is a massive range in the characteristics of targets – industry focus, size, business model, geography. So there is not really “one” market, but multiple markets. And the opportunity for an online marketplace will vary – markets with large numbers of targets, “discovery” challenges (e.g. targets with limited online presence) or diverse buyer profiles, should be more attractive.

Implications for success 

Trying to be a marketplace for everyone would be challenging – significant acquisition cost to bring on members, long time to scale to the point of seeing transactions.

As a result, success (or at least early success) becomes a problem of where to build first. BizBuySell has succeeded by focusing on the really small end of the market – sole proprietorships, franchises. ExitRound is focused more on the technology sector. So I believe it will be possible for multiple “micro” platforms to coexit and not overlap, at least in the medium term.

Role of intermediaries. In contrast to many “broker” relationships, the role of the advisor in M&A is quite complex. Making buyer introductions may be “high value”, but that is a small part of the job. Ask any analyst, associate or junior VP what they do, and you’ll probably here about tasks such as preparing and distributing marketing materials, handling buyer NDAs, managing the exchange of due diligence information, maintaining process logs or preparing client updates.

Implications for success:

There appear to be three ways for online marketplaces to “play” with advisors:

  • Replace them by providing with technology what they do themselves.
  • Open the marketplace up to them, treating them much like business owners who can create listings on behalf of clients.
  • Build additional workflow tools for them as a platform within the platform (with additional subscription revenue).

These are by no means mutually exclusive. A marketplace could both disrupt the advisor model, replacing part of their role, while enabling them to focus on other aspects on a deal. This is already the way many marketplaces work today – they are a compliment rather than a replacement for an advisor.

And I do believe it will be challenging to disintermediate advisors entirely without significant investment in workflow tools, or integrating existing workflow tools into the platform. Otherwise, when a business owner faces a decision whether to “self-serve” on a platform or hire an advisor route, the platform will not deliver a sufficient solution.

Building workflow may be challenging. IntraLinks is perhaps the best positioned to combine workflow with a marketplace, since they already have the largest online dataroom product and also acquired DealNexus. However, they appear focused on selling their Via product to corporates (staying ahead of Box.com et al), as opposed to building an M&A desktop application. Many workflow components (e.g. CRM, diligence logging, client reporting) will require expertise and resources to build, and time to adopt. While some components already exist, such as CRM tools, adapting them to the M&A use case is tricky (have a look at Navatar).

Noise. My inbox is overloaded. I’m sure yours is too. Having worked in corp dev, I can tell you what M&A practitioners don’t need are more poorly targeted opportunities being lobbed at them. Very few deals are actually consummated, and one of the biggest determinants is in the initial filter of what companies to look at. This is important for two reasons – first, I believe many marketplace business models will depend upon a success fee (at least in part) so you need transactions to take place to earn revenue; second, I think practitioners will be slow to adopt (or quick to dismiss) a marketplace that does not deliver relevant recommendations. Continue reading “M&A marketplaces: what it takes to be successful” →

Weekend reading: Collection of key articles and reports on payment tech

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I like to save my more dense reading to tackle on the weekend with a morning cup of coffee. Below is a collection of key payment tech pieces on my list for this weekend. My interests range from macro questions to the very specific: what are the structural changes in payments? what does the value chain look like today and where is it being disrupted? how does ApplePay actually work?

Happy reading…

1. Broad view of all payments globally

Cap Gemini World Payments Report:  Does a great job summarizing the overall payment landscape, regulatory initiatives, as well as changes in payment processing.

2. Payment Processing 101

If you need a primer on how a payment actually goes from Point A to Point B, this is a very simple guide: PayFirma

This Quora post does a nice job of explaining the economics along the payment system value chain: What is the payment system value chain?

There’s a specific example for a credit card transaction:

Credit-card-economics

Nice summary of the impact of the Durbin Amendment: Economics of Durbin.

3. Opportunity for disruption

This is a well informed summary of what’s changing structurally in the US payment landscape, and is dense with ideas, themes and opinions. I’ve read it once already, but need to read it again: Structural Changes In Payments.

This is a great report on the changes specifically impacting the card processing industry: Transforming the Credit Card Processing Industry.

What does it take to be a startup providing online payment services: How to Become a Payment Processor.

4. How Apple Pay works

For the un-initiated, here is an Apple Pay primer: The Essential Apple Pay.

This post does a great job explaining tokenization, how it works and why it matters: Clovers Developers Blog.

Detail on EMV and how it works: EMV Guide.

How EMV works

 

 

Fintech: in 14 great charts

Reuben 1 Comment

Below are 14 charts about fintech I found quite interesting. They each pickup on different themes — from mobile banking to payment technology. These are great for pitchbooks to investors, customers and employees. If you want a complete collection (200+), check them out here (courtesy of The Financial Brand).

1. Consumer confidence in banks

consumer_confidence_in_banks_1979_through_2013

2. How Consumers Use Of Branches Has Changed Over Time

how_consumers_use_of_branches_has_changed_over_time

3. Projections For Retail Banking Interactions By Channel

projections_for_retail_banking_interactions_by_channel

4. Banking Status Of US Households Continue reading “Fintech: in 14 great charts” →