A recent study comparing financial differences between men and women found that, overall, women are better at managing money and debt. Differences between the two populations include:

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.

By: Matt Sifferlen Ah, fraudulent behavior is currently enjoying a bright shiny moment in the sun in today's pop culture, particularly in the world of sports. Whether it's a college athlete being duped for months by telephone conversations with a non-existent girlfriend, or the world's best known cyclist coming clean on a lifetime of deceit, in both cases we're left shaking our heads and laughing, crying, or cringing while telling ourselves "I'm glad I'm too smart to fall for any of this." But are you just kidding yourself? In the case of the college football player, most of us have been scratching our heads wondering how any adult could possibly get strung along for such an extended period of time by such a scam.  But if you take a closer look at the interaction between the athlete and the fraudster, you'll see that the fraudster deployed some typical tactics that allowed him to keep the scam living and breathing.  In particular, he continuously kept communicating with the athlete via phone and social media, reinforcing the perception that he's aboveboard and genuinely interested in the athlete's life.  We see this in commercial fraud interactions too, where the commercial fraudster will perform expected, normal tasks and activities (e.g. making small payments on loans, placing phone calls to lender support staff) that will reinforce the lender's perception that the fraudster is just another normal client.  But unlike the athlete's scenario where the fraudster's story unraveled due to no logical conclusion being planned, commercial fraudsters will string lenders along until they get what they want — then they vanish.  Lenders can't get too complacent in their fraud prevention efforts, assuming that the mere presence of normal account activity equates to a validation of a client's authenticity.  To complicate things, while electronic communication methods like text messages, emails, and Twitter or Facebook messages offer many convenience advantages, they are ripe for manipulation by fraudsters who certainly find these methods preferable to any awkward face to face encounters with someone they're victimizing. The cyclist that admitted to a lifetime of lies also shines the light on some other tactics that commercial fraudsters might use — using perceived image and reputation to deceive. Fraudsters will often steal identities of licensed professionals (think physicians, dentists) with favorable credit profiles and use their information to apply for commercial credit or services, knowing that they will likely be viewed favorably due to their impressive profiles, at least on paper. In today's world where lightly staffed underwriting teams struggle to keep up with their workloads, it's easy to see why this tactic can help increase the odds that an application might escape closer scrutiny.  After all, it's a doctor's office so what could possibly go wrong?  A lot, if you're approving someone who really isn't the doctor! An objective evaluation and screening process where underwriting and analyst staff consistently verify all applicant data and not just cherry pick the ones that look suspicious on paper can go a long way towards avoiding this typical trap set by commercial fraudsters. And in the final scenario of art imitating life, there is the recent release of a major motion picture comedy about identify theft.  I'm sure anyone who has been a victim of identity theft won't find hilarity in the scenes of the victim's life getting turned upside down, suddenly unable to use his credit cards at the gas station and being asked about transactions that took place somewhere else in the country that he's never visited.  But undoubtedly many folks will find this humor hilarious because we probably know of some horror story that a friend or acquaintance has shared with us that is similar to one of the wacky scenarios covered in this movie.  So we'll laugh and take comfort in the fact that we're too smart to get scammed like this, but if the FTC is stating that identity theft will affect 1 in 6 people each year then we're fooling ourselves in thinking that our number won't be up at some point soon. So what can be learned from these high profile pop culture events?  I think a couple things.  First, know your customers (or athletes, heroes, girlfriends).  It sounds simple, but make sure they are who they say they are.  Whether you're lending to a business or a consumer, there are tools out there that can enable you to objectively screen your applicants and minimize any bias that might get exploited by fraudsters in a manual review heavy process.  If you're not cautious and get burnt, you might not have to go on Oprah or Dr. Phil to explain to your management team where things went horribly wrong, but  the level of financial and reputational damage inflicted could be a painful lesson for you and your institution.  Or if you're really (un)lucky, maybe they'll make a movie about your story — wouldn't that be hilarious? (sarcasm intended)