1/29: Have you ever had a concept explained to you that helps frame complex issues you’ve been wrestling with and opens your eyes to new possibilities? A concept that I share that seems to resonate well with Entrepreneurs and Investors is what I call “Truth Files”. Unpacked:
2/29: So what is a “Truth File?” Simple definition: “A truth file contains data that without need of additional confirmation can be considered factual.” Not all truth files are 100% accurate and not all are valuable, but the best ones can be transformational.
3/29: The operative question that defines how valuable a truth file is: “What does the truth file reveal that can be used as a substitute for investigative work or help make more accurate decisions?” The first reduces friction and the second improves outcomes.
4/29: Reduction in friction is valuable for all the obvious reasons. Investigative work costs money (almost certainly more than a truth file), creates delays in decision processes, and almost always results in reduced throughput in any funnel.
5/29: Improving outcomes is also valuable for all the obvious reasons. If spending X to buy a truth file results in a 3X improvement in the net economic value to a company, then buying the truth file can be easily justified on a ROI basis.
6/29: There are problems with truth files because none are perfect. A common problem is that most truth files contain some inaccuracies. While using a truth file might work well statistically, it could result in bad decisions at the individual level.
7/29: Another problem is coverage. Not all truth files have data on all people/businesses/etc. and therefore exception processes need to exist to handle the “unscoreables”.
8/29: A profound issue that makes many truth files less useful is that they can only describe the present and are unable to describe the past. For annuity-oriented products, without being able to score the past you can’t correlate the truth file to known outcomes. This matters!
9/29: Yet another issue is that the friction required to access some truth files can overwhelm the value the truth file creates. Without API access or access for "permissible use”, the utility of a truth file can be killed by the process used to access it.
10/29: A few examples of real truth files. Credit bureaus are truth files for the liability side of a consumer’s balance sheet. They contain treasure troves of mostly accurate information supplied by major financial institutions in a highly organized fashion on a regular basis.
11/29: Credit bureaus can be pulled in batch from organizations with “permissible use” (low friction to access) and the data exists going back decades (ability to look into the past).
12/29: While there are errors in the bureau data, it’s proven to be quite accurate and very valuable in statistical models where past behavior can be used to correlate with future outcomes. The value relative to cost is very easy to justify.
13/29: But the inaccuracies can cause poor decisions to be made at the individual level and the bureaus are missing obvious sources of data that would improve the understanding of the liabilities of consumers (i.e. – utility bills, rent, etc).
14/29: Does this mean bureau data should be thrown out? No. Life without bureaus would require long, manual application processes with lots of friction, verification work, and cost. Bureaus aren’t perfect but they’re pretty accurate truth files that do more good than harm.
15/29: Another example would be tax data. A tax filing is the truth file for declared income and deductions for an individual or a business. For individuals, data includes W2/1099 wages, investment gains/losses, real estate holdings, dependents, charitable deductions, etc.
16/29: Tax data should be the core of a valuable truth file, but it creates less value than one would think because the process required to access it isn’t easy to navigate. Instead, in many cases consumers supply the data and it has to be manually entered and verified.
17/29: Another example would be cash in and cash out transactions from a consumer’s primary checking account. This data is the core of an amazing truth file that can be analyzed to understand many important things about how a consumer is living his/her life.
18/29: It can be used to answer questions like: Is the consumer currently solvent (i.e. – monthly inflows exceed monthly outflows)? How regular is their income? Are they moving excess income to savings or investment accounts? Do they have insurance? Are they a homeowner?
19/29: Accessing checking account data has become easier (i.e. - Plaid), but a problem with cash flow data is that very little history is available. In order to use the data to predict how an annuity oriented product might perform, the data needs to be available in the past.
20/29: I’ve heard the narrative that credit bureau data represents the past and cash flow represents the now so cash flow decisions should be superior to credit decisions. I call BS a thousand times over and refuse to be trapped in a “tyranny of the or” narrative. Be gone!
21/29: Cash flow data helps predict “ability to pay based on current liabilities”. But credit data can be used to predict willingness to pay, determine the stability of how a consumer manages his/her financial life, and the stability of their current situation.
22/29: When credit data isn’t available or a consumer’s history with credit is very short, cash flow data can play an important role in lending decisions. Many people refuse to internalize the truth, but when available, credit data is very predictive of future performance.
23/29: Many businesses I’ve come across in the fintech ecosystem are tapping into or trying to create new truth files. They don’t always realize that this is what they’re doing! When I explain the truth file concept to them they quickly say: “That’s what we’re building!”
24/29: For instance, if a Landlord manages each property using a separate checking account the checking account becomes a truth file for how the building is performing. Occupancy rates, rental revenue, repair work, insurance payments, financing costs, etc.
25/29: Another example is the plethora of companies trying to create truth files for employment, income and direct deposit routing (APIs into Payroll data). They’re competing with the truth files available from Equifax through their market leading product (The Work Number).
26/29: While The Work Number has extremely accurate data, their truth files suffer from low coverage rates and a high friction process to access the data. Low coverage rates create the need for backup, manual processes which is frustrating for lenders and landlords.
27/29: The next gen companies in the space are trying to create lower friction, higher coverage truth files that will benefit businesses and consumers alike. Exciting times for everyone except The Work Number.
28/29: TLDR: It’s a useful exercise to think about using or creating low friction, high utility truth files. Businesses can be run more efficiently and effectively when they use truth files and very valuable companies can be created that generate and sell access to truth file.
29/29: “An assumption is the joke, truth the punchline.” Enjoy and RT liberally. Let’s get the conversation going! #fintech

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More from @fintechjunkie

26 Oct
1/15: Of all the questions I’m being asked on recent diligence calls about our companies, the most common is “What are the skills/gaps of the Founder(s)?” Given COVID, this has become an important topic so I thought it would be worth sharing how I think about the issue. Unpacked:
2/15: In every conversation I try to level-set the outsider and speak in “truisms” before diving into specifics. The first truism is that the skill set needed to run a high growth, disruptive start-up is multi-dimensional and that it’s about tradeoffs vs. insisting on completism.
3/15: We’d love if our Founders were world class on dimensions that include: Action orientation, ability to make decisions with limited/changing data, magnet for talent, ability to frame a business vision, and fundraising skills. These are just a few of the many important skills.
Read 15 tweets
16 Oct
1/42: What the heck is going on with the #fintech ecosystem’s obsession with Neo-Banks? Do they actually make sense in the US? Traditional Bankers say “absolutely not”. I say “they can”. Unpacked:
2/42: Because there’s so much confusion about the topic, it’s worth starting with a definitional statement about what a Neo-Bank is. One definition: A Neo-Bank is a COMPANY that offers a LIMITED SUITE OF BANKING PRODUCTS with NO OWNERSHIP OF BRANCH LOCATIONS.
3/42: COMPANY does not mean Bank. There are many forms and fashions of Neo-Banks but not many of them are actually Banks. It’s possible with today’s technological solutions for a non-Bank to offer Banking products.
Read 42 tweets
11 Oct
1/28: The most commonly debated and IMHO the least grounded topic in early stage VC is “how do you determine what a company is worth?” Recent early stage #fintech and #venturecapital valuations seem to defy gravity but are they justified?
2/28: Answering this question requires breaking down the problem into a framework that’s easier to analyze. One framework: A business is “worth” a combination of the intrinsic value of what it can produce and the option value of what it might be able to produce in the future.
3/28: When a company has cracked the code on turning a dollar of investment into a multiple of the dollar in the future it can be categorized as a money making machine.
Read 28 tweets
3 Oct
1/25: I’ve been told that some of the simple concepts I routinely share with Founders get adopted by their firms as “truths” (which is flattering). I was asked to outline a few of them in Tweet form. Unpacked:
2/25: One of my favorites is a concept called “0.8 to the 5th”. It’s an acknowledgment that contingent probabilities suck. If a business plan has many “ands” joining process steps to create outcomes then its stuck in the world of contingent probabilities.
3/25: Most businesses are complex with strings of three, four, and sometimes five or more dependencies linked together. The best Operator in the world only has in the ballpark of an 80% chance of hitting an aggressive goal if it’s one of many complex priorities on his/her plate.
Read 26 tweets
28 Sep
1/31: The biggest question coming out of my recent tweet thread about the evaluation of startups is: “How important is the startup’s distribution strategy in your diligence work?” The answer is: “Damn important because the business needs customers to exist!” Unpacked:
2/31: This may sound backwards to some investors but my diligence process around a company’s marketing strategy starts with the unit economics of their product/offering. The greater the contribution margin (in absolute dollars) the more options a company has to scale.
3/31: If a product can only throw off a few dollars of contribution margin a year then the channels the company should be testing will be very different than if the product can throw off a few hundred or a few thousand dollars of contribution margin.
Read 31 tweets
23 Sep
1/21: Every early stage startup pitch looks the same at a foundational level. This means that the analysis of every early stage startup also looks similar (especially true in #venturecapital and #fintech). Unpacked:
2/21: Every pitch has four main high-level asserted statements: A problem statement, a solution statement, a financial statement and a team statement.
3/21: The problem statement is the Founder’s way of helping his/her audience internalize a problem they’ve discovered in their target market and an articulation of why it’s a gigantic and profoundly painful problem to a defined group of customers.
Read 22 tweets

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