Times are definitely different in the banking world today

By: Joel Pruis

Times are definitely different in the banking world today.  Regulations, competition from other areas, specialized lenders, different lending methods resulting in the competitive landscape we have today.  One area that is significantly different today, and for the better, is the availability of data.  Data from our core accounting systems, data from our loan origination systems, data from the credit bureaus for consumer and for business.  You name it, there is likely a data source that at least touches on the area if not provides full coverage.

But what are we doing with all this data?  How are we using it to improve our business model in the banking environment?  Does it even factor into the equation when we are making tactical or strategic decisions affecting our business?

Unfortunately, I see too often where business decisions are being made based upon anecdotal evidence and not considering the actual data.  Let’s take, for example, Major League Baseball.   How much statistics have been gathered on baseball?  I remember as a boy keeping the stats while attending a Detroit Tigers game, writing down the line up, what happened when each player was up to bat, strikes, balls, hits, outs, etc.  A lot of stats but were they the right stats?  How did these stats correlate to whether the team won or lost, does the performance in one game translate into predictable performance of an entire season for a player or a team?  Obviously one game does not determine an entire season but how often do we reference a single event as the basis for a strategic decision?  How often do we make decisions based upon traditional methods without questioning why?  Do we even reference traditional stats when making strategic decisions?  Or do we make decisions based upon other factors as the scouts of the Oakland A’s were doing in the movie Moneyball?

In one scene of the Movie, Billy Beane, general manager of the A’s, is asking his team of scouts to define the problem they are trying to solve.  The responses are all very subjective in nature and only correlate to how to replace “talented” players that were lost due to contract negotiations, etc.  Nowhere in this scene do any of the scouts provide any true stats for who they want to pursue to replace the players they just lost.  Everything that the scouts are talking about relates to singular assessments of traits that have not been demonstrated to correlate to a team making the playoffs let alone win a single game.  The scouts with all of their experience focus on the player’s swing, ability to throw, running speed, etc.  At one point the scouts even talk about the appearance of the player’s girlfriends!

But what if we changed how we looked at the sport of baseball?  What if we modified the stats used to compile a team; determine how much to pay for an individual player? The movie Moneyball highlights this assessment of the conventional stats and their impact or correlation to a team actually winning games and more importantly the overall regular season.  Bill James is given the credit in the movie for developing the methodology ultimately used by the Oakland A’s in the movie.  This methodology is also referred to as Sabermetrics.

In another scene, Peter Brand, explains how baseball is stuck in the old style of thinking.  The traditional perspective is to buy ‘players’.  In viewing baseball as buying players, the traditional baseball industry has created a model/profile of what is a successful or valuable player.  Buy the right talent and then hopefully the team will win.  Instead, Brand changes the buy from players to buying wins.  Buying wins which require buying runs, in other words, buy enough average runs per game and you should outscore your opponent and win enough games to win your conference.  But why does that mean we would have to change the way that we look at the individual players?  Doesn’t a high batting average have some correlation to the number of runs scored?  Don’t RBI’s (runs batted in) have some level of correlation to runs?  I’m sure there is some correlation but as you start to look at the entire team or development of the line up for any give game, do these stats/metrics have the best correlation to lead to greater predictability of a win or more specifically the predictability of a winning season? Similarly, regardless of how we as bankers have made strategic decisions in the past, it is clear that we have to first figure out what it is exactly we are trying to solve, what we are trying to accomplish.  We have the buzz words, the traditional responses, the non-specific high level descriptions that ultimately leave us with no specific direction.  Ultimately it allows us to just continue the business as usual approach and hope for the best.

In the next few upcoming blogs, we will continue to use the movie Moneyball as the back drop for how we need to stir things up, identify exactly what it is we are trying to solve and figure out how to best approach the solution.