What’s My Favorite Color?

Apologies for being so delinquent in posting but it’s been a busy start to the year.  In addition to working on time intensive projects with a few of our portfolio companies, QED formalized a very exciting relationship with Fifth Third Bancorp.  You might or might not have read about it (Fifth Third Bancorp Partners With QED Investors), but hopefully it explains why I’ve been a little light on the content recently.  More on this relationship soon.  With this said, back to the Blog!

Every once in a while I feel like I’m experiencing what I think of as “Advisory Deja Vu” (ADV).  It’s when I’ve had the same thematic conversation on multiple occasions over a short window of time but with different founders.  And more than once it’s become a theme for a Blog post, this being the case in point.  So, to convey my current ADV theme, I thought it would be best to start with a simple story.

A behavioral psychologist, a data scientist and a five year old walk into a bar.  All three belly up to the bar and order drinks (and in case you’re wondering, the five year old ordered milk – don’t hate on me!).  After a few minutes of complete silence, the bartender addresses the group.

“I can tell that you’re bored and can use a bit of a challenge to liven things up.  Do you see that woman sitting over there?  Free drinks for a year to whoever tells me her favorite color.”

The challenge is accepted and they agree to meet back in an hour to reveal their guesses.

When they return, the behavioral psychologist answers first.  “I spent the last hour analyzing her every movement and studying her attire and jewelry.  She must like muted colors because of her subtle shade of lipstick and very minimal use of makeup.  Her outfit is comprised of shades of black and gray as is her handbag.  But, lots of women like to wear black and gray so this doesn’t tell me much. What’s interesting is that she went to the bathroom half-an-hour ago and came out wearing a maroon colored hair clip.  It stands out nicely against her black outfit and her confidence increased once it was in place so it must be a color she really adores.  As a result, I can definitively say that her favorite color is Maroon.”

The bartender responds: “Sorry.  That’s not right.  The woman is my wife and I bought her that hair clip a year ago.  She wears it to humor me but I don’t think she likes it very much.”

The data scientist goes next.  “I spent the last hour hacking into her computer and analyzing all her purchase behavior on Amazon and her searches on the web.  I loaded all the data into a machine learning optimizer and using a random forest algorithm determined that her favorite color is Green.”

The bartender responds: “Sorry.  We share a computer so the data you analyzed isn’t quite right.  What’s funny is that you figured out MY favorite color, not hers!”

The five year old goes next: “Blue.”

The bartender responds: “That’s right.  How did you know?”

The five year old: “You’re a silly goose.  All I did was go up to her and ask.”

Why tell this simple story?  A recent string of conversations I’ve had with Founders have all centered around something not being quite right with a business with the Founder trying to diagnose the cause using complex data analysis or econometric trends or “theories”.  I’ve found myself giving the same advice over and over and a few Founders have actually taken my advice and started to report back pretty interesting findings.  The advice?

“Your customers know more about themselves than you do and are more than happy to share their thoughts if you ask.”

This is a simple but profound truth that I find is often neglected by Founders and Executives at small and big companies alike.  If you want to know why customers aren’t buying the high end version of your product — ask them.  If you want to know why customers aren’t making payments on your loans — ask them.   If you want to know why customers use your product once and never come back — ask them.

Don’t get me wrong — data is fantastic and very helpful in many contexts.  I love data and what it can teach a business.  I’m a big fan of tools like Full Story and Google Analytics.  But, when trying to understand why your prospects and customers are behaving a certain way I’d suggest you find a way to talk to them and listen to their answers.  Trust me: Ask and they will tell — Listen and you’ll learn.

A long time ago in a galaxy far, far away….

While I don’t like to admit it, I think I’ve finally become “that grumpy old guy”.  To be clear, I’m still in my 40s and am generally upbeat.  But, I’ve finally ticked all the major boxes that would qualify me as a curmudgeon.

Box #1: In my early 30s I called the cops on a neighbor who was racing his motorcycle at 3am.  I felt like a heel but wanted some sleep.

Box #2: About 5 years ago, sitting on a plane I (nicely) told a mother that she needed to control her child.  In my defense, the child was about 3 years old and kept putting his drooly hands on my laptop, my shoulders, my hair, etc.  Not cool.

Box #3: I’ve started to play the “remember when” game all the time.  Remember when everyone wasn’t pausing for selfies every 3 seconds?  Remember when new college grads understood that they had to work their way up the ladder and didn’t complain about it?  Remember when you drank some orange juice when you felt sick instead of “researching your symptoms” on WebMD and convincing yourself that you’re about to die?

And I’m definitely becoming an elder statesman in the FinTech world because in my work interactions I’ve also started to play the “remember when” game.  In fact, just this week I had multiple conversations with various 20+ year veterans in the space about how the most recent generation of FinTech companies should review the lessons learned from last generation’s innovators.   And of these lessons, we all agreed that they should pay special attention to what’s been learned on the topic of “growth”.

To put it simply, there are usually three phases of growth and each has its own characteristics and cadence.

Phase 1: Test, learn and burn

Phase 2: Revenue your way into scale

Phase 3: Cost your way into a healthy business

In Phase 1, every iota of energy needs to be focused on figuring out product/market fit and if the unit economics of the product make sense.   It’s about maniacally executing a well designed learning agenda.  It’s about interpreting early market/test results.  It’s about figuring out which levers matter and how hard they should be pulled.

Phase 1 is also about burning cash.  There’s no way around it because at this stage the business by definition has very few customers and revenue can’t overcome the fixed costs of supporting the product.  While every dollar matters for all the obvious reasons, the goal should be to maximize learnings rather than minimize burn.  Test, learn and burn.

In Phase 2, a business should be focused on scaling aggressively in order to build market relevance and overcome fixed costs.  But, aggressive scaling doesn’t mean mashing the accelerator with no constraints.  Decisions should be made based on the learnings from Phase 1 and not be speculative in nature.  In the formative days of Capital One we used to talk about how this phase of a product’s life felt like cheating because we learned enough in Phase I to know what the results were going to be in advance of spending our marketing dollars.  Cha-Ching!

Phase 2 is also about building out the capabilities of the team and operational processes such that the results are repeatable.  Selling widgets starts with making consistent widgets to sell, and this is a piece of the growth story that’s routinely being overlooked by today’s hyper-growth lending start-ups.  My observation is that many lending companies that I’ve diligenced over the years grew or are growing faster than they should.  It’s not uncommon for a hyper-growth lending company to tap pockets of customer demand at the margin, and it’s precisely these marginal customers that have less robust economics and more volatility associated with them.  Increasing repeatability and reducing volatility is almost always more important than an extra 10%, 20% or even 50% growth rate.  Volatile revenue is garbage and nine times out of ten will destroy value and create distracting fire drills.  Do the work and analyze what the last 10% or 20% is actually contributing.  It’s not an easy exercise but ALWAYS worth the time and effort.  Revenue your way into scale is a good mantra to espouse but only if the revenue is stable and robust revenue.

The transition from Phase 2 to Phase 3 is really interesting because it usually comes as a wake-up call vs. a choice.  Most successful businesses grow at a 45 degree angle straight up until the day that they suddenly hit a wall.  WHAM!  Sometimes the wall is a direct result of a shock to the industry or the economy.  Sometimes the wall shows itself after an internal breakdown in processes or controls.  And sometimes the wall is erected by the competitive landscape and appears as a slowdown in the growth rate of the business.  Welcome to Phase 3!

The Phase 3 playbook is usually quite obvious but not without pain and suffering.  The leverage in this Phase typically comes from refinement vs. trail blazing.  It comes from ripping costs out of the system and jettisoning marginal projects, tests and even people.  It comes from renegotiating contracts and focusing on cost-efficient-widget-making.  Costing your way into a healthy business is almost always an inevitable stop on the way to greatness, so when it arrives embrace it, deal with it, and move past it.  I wrote a few posts  earlier in the year that lightly touch on this topic: Thriving, Surviving or Dying and What Happens When The Cash Runs Out.

So how does this tie to my earlier comments about being in “remember when” mode?  It ties to my comments because I find myself sharing stories (both good and bad) and lessons from the past with less experienced operators.  I find myself remembering back to a time when being great meant growing top line at 20-40% a year with 20%+ ROEs for 10 years in a row.  I remember when hyper-growth was punished and consistency was rewarded.  I remember when the resilience and quality of revenue mattered more than raw growth numbers.

Don’t get me wrong, early stage lending companies need to grow at 2-3X a year in order to achieve relevance and overcome fixed costs.  But, my lamentation is around the growth advice that some investors are giving to entrepreneurs and that many entrepreneurs are glomming onto.  Grow, grow, grow!  More is better!

In many cases there doesn’t seem to be an appetite for discussing the right growth rate if it’s sub-100%.  In many cases unit economics take a back seat to top line revenue growth and vertical economics.  And in many cases the hard work around attacking costs is replaced with a view that “we can grow our way into our cost structure”.

My advice is simple.  Learn first.  Grow when you’re ready.  Become operationally excellent.  Embrace the inevitability of a wall.  Build a great business by attacking costs and improving the bottom line.  Rinse and repeat.  And don’t let anyone fool you into thinking that building a lending business is a land grab (as justification for their growth advice).  We live in a consumerist society so the opportunity to lend money will be there tomorrow if it’s there today.  Patience you must have my young Padawan.  Patience.

Belts and Suspenders

First, apologies to those dedicated fellow fintech junkies that actually read what I write.  I’ve been very delinquent in posting recently but it’s been a busy time personally and professionally.  Hopefully I’ll be able to post more regularly going forward….hopefully!

With this said, having just returned from the Money2020 conference I thought I’d share a few thoughts on a topic that I was asked about by quite a few people.  It’s not lost on the broad community of investors and entrepreneurs in the lending space that most of the “next-gen” lenders have built and optimized their risk models during a fairly benign economic period.  Everyone seems to be worried about how well these models will perform in a downturn and how one can protect against a sudden and massive deterioration in a portfolio’s performance.

The key to answering these questions lies in the meta-question: “How resilient is your borrower base”.  And measuring resilience is really about understanding “How many things have to go wrong” at the customer level.

At its core, a lender’s job is relatively straightforward.  A loan officer makes loans to “healthy” customers who they believe are willing and able to pay back the loans.  But the unfortunate truth is that some borrowers in every portfolio default on their loans.  The “why” is pretty clear:   Borrower’s circumstances change over time and these changes matter.

Foundationally, a healthy borrower has the following traits:

  • A relatively stable source of income that supports one’s obligations/lifestyle
  • Enough savings to weather a temporary disruption to one’s income
  • Enough savings or free cash flow that can handle the introduction of additional unforeseen expenses
  • A willingness to pay one’s debtors when the money is available
  • The ability to quickly find a new source of income after a disruption
  • The ability and willingness to turn collateral into cash to pay one’s obligations

So the breakdown of a healthy customer can be traced to a fundamental change in their circumstances.

  • Temporary reduction of income (job loss, reduced commission, etc)
  • Permanent reduction of income (major change in health, retirement, etc)
  • Increased cost of living (increased borrowing, new child, etc)
  • Unforeseen major expenses (car repair, medical bill, etc)
  • Reduction in financial safety net (increased spending, reduction in new savings, etc)
  • Reduction in willingness to pay (strategic default, attitudinal change, etc)

Statistically based underwriting models perform better than loan officers because models are able to predict the natural change in circumstances at the customer and portfolio levels.  A model doesn’t classify a customer as “good” or “bad” but rather that they have a certain probability of paying back a loan.

But both human and statistical based underwriting models/policies suffer from the same phenomenon – tomorrow isn’t guaranteed to look like today.  And while models are able to project an ambient deterioration in a portfolio’s performance, they aren’t fundamentally able to project what will happen in a future they’ve never seen before.

The natural reaction from investors and entrepreneurs who haven’t managed loan portfolios through cycles is to be terrified of what’s to come.  Investors want to naturally stop investing in companies that originate loans.  Less experienced entrepreneurs don’t know how to build resilient underwriting models and convince investors that all is “OK”.

My advice is simple:

Make sure your models give significant weight to the major drivers of risk (ability to pay, willingness to pay, stability of income, etc).  Just exposing a model to hundreds of potential variables isn’t good enough.  Credit officers have to make sure their models appropriately weights each and every potential driver.  And if a model doesn’t want to use an important variable or driver, a credit officers’ job is to force it into the models or policies.

Why?  If an important goal is to create a resilient portfolio, to do this a lender needs to make sure many things can go wrong within the customer base before defaults exceed expectations.  For example, it’s critical to avoid lending to customers who are on edge from a capacity standpoint because minor changes in their circumstances will push them from solvency to insolvency.  DTI might not show up in the underwriting models because our economy has been great for the past handful of years, but I can definitively say from experience that DTI won’t matter until it’s the only thing that matters.  Models don’t understand this.  Good credit officers do.

Another example: Make sure customers with less stable professions have enough savings to weather a temporary disruption to their incomes.  There’s great data at the Bureau of Labor Statistics regarding unemployment rates by profession.  If you’re not studying these statistics and internalizing their impact on the stability of your customer base’s income stream you’re missing out on a great source of information.  Reg B (fair lending) has to be considered when designing approve/decline/pricing policies but once again I can definitively say that this data can be used to improve the resilience of a portfolio of loans.

Belts and Suspenders.  Just make sure you’re not building a business where a single shock to the system causes issues at the customer level.  Trust me — you’ll sleep better as will your investors and customers!

You Can’t Accelerate Time

There are certain “truths” in life that are immutable whether one wants to believe them or not.  In order for the motion of an object to change, a force must act upon it (Newton’s First Law of Motion).  The total energy in a closed or isolated system is constant (Conservation of Mass-Energy).  If you need n items of anything, you will have n – 1 in stock (Seuker’s Note).

In the domain of building businesses there are immutable laws as well and trying to violate them rarely ends well.  If you don’t have money you can’t pay for anything (The Law of Empty Pockets).  Owning 5% of a successful company is better than owning 100% of a bankrupt entity (The Law of Greed).  A good idea that never sees the light of day makes for a bad business (The Law of Pot Sitting).

What surprises me is that recently I’ve had what I feel is the same discussion over and over again with various Entrepreneurs and Investors regarding what I think of as an “immutable truth”.  I think of it as “The Unfortunate Law of Building Complex Businesses” and it can be described simply by the following “truth”:

You Can’t Accelerate Time

Most complex businesses can’t be cracked overnight and this is especially true if they require today’s investments to result in a stream of cash that trickles in over time.  The return profile of money invested in originating customers today might take months, quarters, or even years to understand and for some reason I’ve found myself in the middle of some pretty confusing conversations about this concept recently.  As much as everyone involved with a business always likes to show quick progress and results that resemble a 45-degree angle straight up, the cadence of growing certain types of businesses doesn’t fit this profile.

Patience seems to have been lost in the investment community and the drivers are obvious.   Every day cash is being burned which puts pressure on start-ups to produce results now.  The cash-out date of a business is always known and burned into a Founder’s brain.  Founders typically work backwards from cash-out minus 3-5 months (to give time for a fundraise) and put plans together that make their company look attractive to the next round investor.  Unfortunately, this typically manifests itself as priority being given to growth of the top line at all costs.  They want to show that the dogs are eating the dogfood and that the business can originate customers at a reasonable cost.

But, for many businesses on-boarding the customer is only the beginning.  Complex, annuity oriented products require time to understand.  If a business’s financial model suggests that the economics at month 12 or month 24 post-acquisition matters, then the business needs to gather data over 12 or 24 months to gain confidence in its projections.  For some products there are ways of analyzing early performance results as a method for gaining comfort with out-month/out-year performance estimates, but there are times that these estimates are inherently flawed.  And for the most complex products, there is no substitute for real data.

You Can’t Accelerate Time.  Full Stop.

Backing these businesses requires patient Investors, a well-established learning agenda that outlines what will be learned by when, and enough capital to prove out the critical assumptions that make or break the business.   The alternative is to move quickly and recognize that levers might need to be pulled at a later date if the results aren’t coming in as anticipated.  Moving quickly can work if the business is fungible and customers are understanding, but it’s important to internalize that most decisions aren’t reversible.  Growing a business that requires making irreversible decisions before performance is well understood is equivalent to gambling with investor money.

My recommendations are simple:

To Founders

Before you become a steward of Investors’ money make sure you and your investors understand what the business will have learned before the money runs out.  If it isn’t enough, adjust the model accordingly or raise enough money to prove out the next series of critical assumptions.  If you can’t do either then you’re likely dead before you start so don’t start.

To Investors

Before you invest in a complex, annuity oriented business, identify the critical assumptions that need to be proved out before the business is at “the next stage” and ask the Founder to pull together a plan that proves out these assumptions.  If you’re willing to fund this plan, great.  If not, ask for the plan to be refined accordingly.  But, at all costs you should avoid funding a business to get part-way to the next stage without expecting to write the next check yourself.  It’s better to just move on because a half-funded business will typically be managed very poorly for all the obvious reasons.

Tick Tock, respect the clock!

When Good Turns To Bad

Contrary to popular belief, there is no such thing as an absolute “good trait” or an absolute “bad trait”.  There are times that being aggressive is a good thing and there are times that being selfless is a bad thing.  Context matters.

To this end, I wanted to share my thinking about a particular Entrepreneurial trait that most people believe is universally good and is without exception considered one of the must-haves of a backable Entrepreneur.  As an Investor you look for signs of its presence through references and direct conversations.  As an Entrepreneur you tell tales from your past with pride in an attempt to prove its existence.  Many times it’s the reason an Entrepreneur succeeds.  And unfortunately, I also believe that it’s frequently the reason behind many failures.  The trait I’m referring to is “tenacity”.

Many of you may be thinking “how can being tenacious be a bad thing?”  The answer is simple.  Tenacity can easily transform into stubbornness and stubbornness can blind an Entrepreneur to the truth.  And since the truth is almost always dictated by a business’s customers, an Entrepreneur should know how to launch, listen, evaluate, adjust and re-launch without compromising his or her greater mission.  But many can’t do this and it can lead to downfall.

My advice to Entrepreneurs is:

Quote

While this sounds simple, it’s actually difficult for many strong personalities to accept.  They obsess about a problem and can articulate facts about the competition, the market need, and why conditions are finally right for a solution to emerge (good tenacity).  But sometimes they believe that their initial solution is the one and only answer to the problem (bad tenacity = stubbornness).  They can easily get over-invested in the work it took to get from Point A to Point B and want to move on to Point C to show continued progress.  But sometimes when a business arrives at Point B the current plans need to be scrapped because the market isn’t reacting the way you want it to. While this can hurt quite badly, many times it’s better to re-trench than march forward.

And the same blindness exists within the Investor community.  Just last week I was talking to an Investor about an early stage company and in our conversation they confidently told me the following: “I don’t trust the Founder because he’s changed the business’s model already.  If he doesn’t have the tenacity to stick with his model and get it to work then I can’t back him.”  For context, the company we were discussing is precisely 9 months old and its mission hasn’t changed one iota since inception.  What has changed is how they plan on attacking the problem they’ve identified and these changes were made based on conversations with prospective customers and experts in the field (including me!).

The tricky balance that’s grossly mismanaged more frequently than not is to make sure that a business is capitalized enough to allow for changes.  If an Entrepreneur only gets one “at bat” then luck is playing a pretty big role in the success or failure of the business.  If you get multiple “at bats” then an Entrepreneur has a realistic chance to pull together a winning business model.

While I’ve met many brilliant Entrepreneurs in my journeys, I’ve never met one that’s 100% right 100% of the time.  The best you can hope for is that they’re 100% right about the problem they’re attacking and that they’ll figure out the solution on the way.  And as an Investor you have to realize that change is precisely what you want because counting on the Entrepreneur to be perfect is a fantasy standard.

So, don’t forget that it’s essential to stay true to your mission but NOT to the solution.

Dear Past…

Dear Past Quote

Those of you who know me well know that I have a history of challenging the norm.  I’ve found myself called into many a boss’s office over things I did that didn’t conform to the rules.  When faced with a problem or a challenge, I’ve always started by finding a solution that assumed away all constraints first.   Only after I had an “ideal solution” in mind would I layer the constraints back on to see where compromise was necessary.  And if I’m honest, more often than not I would ignore or challenge the constraints in pursuit of the best answer possible.

I bring this up because the current debate about the operating models that the non-Bank Specialty Originators have put in place is giving me flashbacks.  Traditional Bankers are saying “you should have known better” and claim that the models were bankrupt from the start.  If you ask the simple question “why”, the answers all feel like a variation on the theme of “you can’t run a lending business without access to deposits”.  And when you ask the follow-up question “how else can you fund a lending business”, these same pundits don’t want to engage in a meaningful conversation.  Instead they say “you should have known better” and claim that the operating model was destined to fail from the start.

But what’s interesting is that if you ask even the most conservative Banker “did this modern breed of Originator do anything right” you’ll get positive responses from almost all of them (at least those that know what the heck these models are all about).  Even though to them the model doesn’t hold together they’re willing to admit that pieces and parts actually worked.

So what have they done right?  What can be learned from the Innovators?

  1. Consumers are interested in an amortizing personal loan product. The Banks shut down their personal loan businesses during the last financial crisis and haven’t brought it back in any meaningful way (i.e. – The Hourglass Effect).  In essence, the Banks haven’t been listening to and serving their customers’ needs because consumers like the product structure and when it went away it created a void.  Over $20B of amortizing unsecured personal loans have been made by a small handful of players and if not for the current funding situation this number most likely would have doubled in the next year alone.
  2. Customers are willing to accept lending products from non-Banks. The brand and stability of a Bank doesn’t matter much when you’re giving out money to consumers (i.e. – loans).  The situation might be very different if a consumer needs to give you their money (i.e. – deposits), but even this may be changing.  The general insight is that if a consumer can easily understand the terms and conditions of a loan then they don’t care much about who they’re getting the loan from.
  3. Companies can create nearly frictionless loan application processes. With the right focus on tech and user experience, a loan application process can be created that allows applicants to use the device of their choice to quickly and easily navigate what has historically been a complex and drawn out process. The current leaders in the space have reduced the entire process to a single point of friction (verification) and even this point of friction is being attacked and improved upon.  Instant approval is the new norm and loan proceeds can be made available as quickly as the verification process can be completed.
  4. Platforms can be created that allow lenders to invest in fractional loans/buy whole loans at the individual customer level. This might not sound like a big deal, but it is.  “Assemble your own loan book” has never been done before and it’s a powerful idea.  Approved loan applicants are systematically matched with buyers based on established criteria or put on a platform for “first come first serve” sale.  This process is serving deep pools of existing capital but also has opened up new investor pools to the asset class, some of which never had the ability or desire to participate in large securitization programs (i.e. – retail investors).
  5. Scale matters. The infrastructure investment needed to put all the pieces in place is many multiples of what Entrepreneurs and Investors originally anticipated.  Now that the model has matured it’s very clear that the capabilities needed to run a non-Bank loan originations platform aren’t trivial to assemble and manage.  But, this can be seen as a double-edged sword because once the capabilities have been built, a real barrier to entry exists that won’t be easy for new start-ups to replicate.
  6. Funding is fickle. Without direct control of the inflow and outflow of funding capital, a loan originations platform has to find some way to attract capital that’s interested in its production.  Many sources of capital look at the cash flows being thrown off by loans as a “trade” which means it has to stand up on both an absolute and a relative basis.  If better risk/return options emerge in the market, the platforms have to figure out how to quickly improve the investor return profile or risk losing access to capital.  This means that if a platform isn’t nimble and over-capacitized in terms of capital sources, its ability to originate new loans is always at risk.

If we tally the scorecard, it would look something like this:

  • The first four learnings squarely favor the non-Bank Originators
  • The fifth learning helps the at-scale players and hurts the smaller players
  • The final learning is a big black-eye for the non-Bank Originators

Is the final learning a sign that an extinction event is on the horizon?  You can judge for yourself but I can say with confidence that I’m not in that camp.  There’s no need to believe in historical constraints and traditional viewpoints when by disbelieving them real solutions might (will) arise.  Whether the solution ends up as an alignment with more permanent sources of capital or some other creative solution that has yet to show itself, I personally believe solutions exist.  The next few weeks/months will likely write the story but to me it just feels like another trip to the boss’s office.

Shedding Tears

“Tears come from the Heart, not the Brain”

Leonardo da Vinci

I’m sad.  Not angry, not frustrated, not panicked.  Just sad.  The industry that I love so much is in the midst of a minor meltdown, and everyone wants to know if it’s a storm in a teacup or an extinction event.  I’ve carefully articulated my perspective in recent posts, so if you want to know whether or not I think the next-gen lending companies are real or Memorex, feel free to read these pieces:

Thriving, Surviving or Dying

What Happens When The Cash Runs Out

Welcome Back My Friends

I Once Was Lost…

But what’s really going through my head after the events of the past few days is a bit more philosophical.  I think about life as a series of small moments that when strung together tell a story.  Most of these moments are unremarkable and forgotten as soon as the next moment arrives.  But occasionally a moment is memorable because it’s at the heart of a bigger story.  And when this moment involves “choice” it’s typically catalytic in defining who a person is and down what path they’re destined to walk.

Recent events remind me of my “moment” which took place on a rainy day almost two decades ago back when I was a young analyst at Capital One.  At the time, I was busy managing one of the company’s rapidly growing credit card P&Ls, but like most of my career at Capital One this wasn’t my only job.  Off the side of my desk I managed a team of analysts who were in charge of the loan loss forecasts for the company, and let me tell you, it wasn’t a job that I liked one bit.  When the books closed for the month, data came pouring in and my team had 48 hours to digest the new information, talk to the business lines about what we were seeing, and publish an integrated forecast that fed into the financial infrastructure of the company.  Needless to say, it was a thankless but important job that didn’t win me any friends.

For each of the previous four months our published forecast had under predicted losses by a small margin.  Every month we made a small tweak to the forecast to reflect the increases we were seeing, but the adjustments didn’t seem to be working.  I was taking a little heat from the finance organization because the misses were causing minor problems with the company’s ability to deliver against our broader financial obligations (to the street) but I was also being pressured by the business units to stay the course because the forecast influenced their profitability models and how much they could grow.  It’s an understatement to say that I was nervous leading up to the next forecasting cycle because I was pretty certain we were about to miss again.  The data came in and I was right.  Another small miss.

I sat down with my forecasting team (2 analysts plus me) and we decided to re-examine every assumption in each of the individual forecast models and rebuild the forecast from the ground up.  Something just wasn’t being picked up in the models and we had to fix it.  Being wrong month after month was frustrating to the entire organization, but more importantly we needed to get to the bottom of the problem and figure out what was going on so Management could steer the mother ship accordingly.  And it had to be done in 48 hours including a complete “State of the Union” memo that I personally wrote each and every month.

When I replay the moment in my mind it feels like something out of a movie.  The three of us were huddled around a computer that had our new models loaded and compiled.  I nervously looked at them and asked “are you ready?”  Slow nods all around.  I hit the enter key and we waited while the models ran.  The result — A flashing NEGATIVE $100MM.  Our jaws hit the floor.  NEGATIVE $100MM.

The implications hit me like a brick wall.  Adding another $100MM to our reserves would mean missing earnings for the first time in Capital One’s short life.  Sharing this information would obviously kick-off an internal review of our work that I would have to defend.  It would mean more late nights running and re-running our models and trying to find ways to explain what was happening to a suite of angry Executives.  It would make me the center of attention with the various P&L owners and likely the most hated person in the company.  And, if the forecast held up to everyone’s scrutiny, the downstream impact would taint the perfect record of the company that I so carefully helped build.

It wouldn’t have been difficult to tweak the numbers and punt the problem downstream.  It wouldn’t have been difficult to show another small miss and take my now-routine slap on the wrist by the finance department like I had the past few months.  It wouldn’t have been difficult to cover the whole thing up and find a way to hand the job off to someone else before the wheels came off the proverbial bus.  But this was my “moment” and I published the forecast.

Capital One survived but it wasn’t a fun time.  Management informed the Street that we were likely to miss earnings, the stock took a major hit, smaller product lines were shuttered completely, and lots of people across the entire company were fired or let-go as the business units were forced to reduce their costs.  And to this day, I might be the only one that remembers the choice that I had and the decision that I made.  It was my “moment”.

So, my response to what I think about the recent news and the implications on the industry is that I’m sad.  Not angry, not frustrated, not panicked.  Just sad.

Peace out.