Defining the issue – what is it bankers are trying to solve for

by Guest Contributor 4 min read June 10, 2013

By: Joel Pruis

What is it we as bankers are trying to accomplish?  If you have been in the industry for 20+ years, this question may sound ridiculous! We do what we do!  We are bankers!  What do you mean define what are we trying to do?  But that is the question, what is it we are trying to do?  I am going to propose we boil it down to the basic/fundamental element – Banks aggregate money from various sources and redeploy these funds to earn a return for the shareholders.  Ultimately, our objective is to generate an appropriate return for the shareholders

Getting back to the movie Moneyball, Billy Beane and Peter Brand define the objective of the Oakland A’s for the season in terms of projecting the number of wins that are needed to assure, with all probability, that the team makes the playoffs (this would be similar to the objective of banking to generate an appropriate return for the shareholders).  But Peter Brand quickly moves into very specific targets that are required for the A’s to make it to the playoffs, namely win 99 regular season games.  In order to win 99 regular season games, the A’s offense will need to score 814 runs in the season and defensively only allow 645 runs.  Plain and simple.  Very objective, very measurable and it is all based upon data, data, data.

Let’s break this down.  Based upon their conference, the teams in their conference along with the overall schedule, Peter Brand projects that 99 wins are necessary to land a spot in the playoffs.  No gut check, no darts or crystal ball but rather historical data that when analyzed provides the benchmark of 99 wins to statistically assure the Oakland A’s that they will make the playoffs.

So let’s apply this to banking.  Our objective is to generate the appropriate return for our shareholders or the old Return on Equity.  So, for example, if our targeted return on equity is 20% (making the playoffs) we need to make sure we generate enough net income (99 wins) through producing the necessary gross yield on assets (814 runs generated by the Oakland A’s offense) less the expected charge offs (645 runs allowed by the Oakland A’s defense).

For a quick dive into details, our data would provide for a margin of error on the variable to provide for statistical assurance of achieving the objective (Return on Equity).  In the movie there is no guaranty that the 814 runs will win the conference but at the same time there is no guaranty that the Oakland A’s opponents will score 645 runs.  Never in the movie does the coach, Billy Beane or Peter Brand tell the team, “You only have to score X number of runs this game, don’t score anymore.”  Or even crazier, “You are not letting the other team score enough runs, they need to score 645!”  No, the strategy is still to generate as many runs as possible while minimizing the number of runs scored by the opposition.

Rather it is the review of the total amount of earning assets of the financial institution and the overall credit quality that we must understand and control to determine our ability to generate the net yield on assets required to generate the return on equity that is required.  If we assume too much risk in the portfolio in order to generate the required yield it would be similar to having a poor pitching staff projected to allow 10 runs a game requiring the team to produce 11 runs a game in order to win.  It just is not realistic.  So basically we need to assess at the high level, are we appropriately structured to allow for the generation of enough profit to provide the appropriate return on equity.  At this point, we do not need to complicate it any further than that.

Now let’s take a look at the constraints.  We know we have them in banking, let’s take a look at probably the single biggest constraint imposed on Billy Beane and the Oakland A’s.  In the movie, before Billy Beane is even aware of the Moneyball concept, his is given his constraint by the owner.  Beane asks for more money to ‘buy players’ and is flat out rejected by the owner.  The owner, in fact, cuts Beane off by asking, “is there anything else I can do for you?”.  Net result is that the Oakland A’s have $38 million dollars for payroll vs. the New York Yankees at $120 million.  Seriously it does not seem fair.  How can you attract the needed talent when you cannot pay the type of salary needed to get the necessary players to win a championship?  Let’s rephrases this for banking…  How can a bank be expected to deploy its assets when such a high rate of return is required?  Boiling it down to a specific example, “How can I originate a commercial loan at this rate of interest when the competition is ½ to 1% lower than our rates?”

Up next – Why will 99 games get us to the playoffs?  How do we assess the environment?

Related Posts

Why Distribution Matters in Income and Employment Verification 

Verification has become an increasingly important area of focus in mortgage lending, but success is about more than just coverage. In the latest episode of the Chrisman Commentary Podcast, Experian's Jamie Norris, Senior Manager of Strategic Alliances, shares why distribution and integration are increasingly the keys to driving adoption, automation, and better borrower experiences.  Why Distribution Matters in Verification  As lenders continue to pursue faster, more efficient mortgage processes, verification solutions must fit seamlessly into the systems they already use. Norris explains how Experian's strategy is focused on helping lenders access trusted income and employment data while minimizing workflow disruption by making Experian Verify accessible across loan origination systems (LOS), point-of-sale platforms, underwriting technologies, and reseller networks.  Building a Smarter Verification Strategy  The conversation explores why lenders benefit from having access to multiple verification providers, how they can optimize verification strategies to maximize automation while minimizing costs and borrower friction, and why an "instant-first" approach is gaining momentum across the industry.  Looking Ahead: AI, Automation, and the Future of Mortgage Lending  Norris also discusses how AI-driven underwriting and decisioning are reshaping mortgage technology. As lending platforms become increasingly automated, real-time verification data is expected to support faster decisioning and more streamlined borrower experiences.  She shares Experian's vision for expanding its verification ecosystem and delivering a broader suite of solutions that meet lenders wherever they work.  Listen to the full episode above to hear Jamie's insights on verification strategy, partner integrations, AI-enabled lending, and what's next for mortgage automation. 

August 18, 2026 by Ted Wentzel
The Email Address as Your Most Powerful Identity Signal

The why behind Experian's acquisition of AtData What happens when a comprehensive email intelligence database joins a global leader in data, analytics and fraud prevention? The acquisition of AtData adds 25+ years of building a complete view of email as an identity signal. Financial institutions can recognize, engage and protect customers unlocking a new standard for the way their teams work and the customer experience. That's what Experian's acquisition of AtData delivers. How we got here Not all email addresses tell the same story. Some are newly created. Some exhibit bot-like patterns. Some are inconsistent with every other signal you have about that person. Imagine a real customer. You have a job. You shop online. You have a primary email from your employer, a personal Gmail you've used for 15 years, and an old Yahoo address you still use for shopping because you've been using it since college. You're an engaged customer who interacts with brands, makes purchases and pays bills on time. But each system sees a different version of you. When you apply for credit, the lender sees one email. When you shop, the retailer sees another. When you sign up for a service, you might use the third. For financial institutions: You slow down the approval process to manually verify identity or approve applicants without the full picture. For retailers: You can't tell which version of "customer" is the most engaged, so you either over-mail or under-serve. For fraud systems: Sees a new account created under one email and flags it as suspicious because it doesn't have the history. This was the original problem AtData was built to solve in 1999. Twenty-five years later, that problem didn’t go away, it became more complex. Email fragmentation and device sharing are more common, and identity theft is more sophisticated. Capabilities that now work together Experian has built sophisticated identity and fraud solutions backed by consumer data resources and decades of expertise in credit and risk. AtData brought the ability to assess whether an email address is trustworthy, reachable and consistent—at scale, in real time. Experian is now making email intelligence foundational, not optional. This matters for: Fraud prevention and risk management: Distinguishing a returning customer from a new threat. Knowing whether an email is newly created, exhibiting bot-like patterns or inconsistent with other identities is crucial. Compliance: Building audit trails that can explain identity decisions. Email data history and behavioral signals create the documentation needed to defend your decisions. Credit: Verifying identity in a world where traditional signals are shifting. Email signals provide a persistent, durable identifier that confirms who someone actually is. Marketing: Reaching the right person across email, mail and digital channels. Email intelligence reveals which addresses are actively engaged and reachable. Research shows email remains one of the highest-ROI marketing channels outperforming paid search and social advertising1. The problem every marketer faces: You end up burning budget on addresses that bounce, are unmonitored or are associated with users who never open mail. For credit marketing specifically, email enables faster, more targeted delivery of firm offers across channels, something that's increasingly important in a post-cookie world. "Email is a persistent identifier in a fragmented world. It's what connects a person's postal address, phones, devices, behaviors—the full picture of who they are. By embedding that into our infrastructure, we're not just adding another data point. We're fundamentally improving how businesses understand who their customers are."- Ashley Knight, Senior Vice President, Financial Services and Data Why now? AI is reshaping how decisions are made in every industry. Models are getting faster, more automated and more embedded in core workflows. But AI is only as effective as the data behind it. Fragmented data + fast models = faster, larger-scale misclassifications. In an era of synthetic identities, AI agents, deepfakes and AI-generated activity, the value of durable, persistent, real-world data signals has increased dramatically. Deloitte’s Center for Financial Services projects that generative AI could drive fraud losses in the U.S. up to $40 billion by 2027, a 32% growth rate since 2023. And email sits at the center of it with business email compromise already being one of the most common and costly fraud types. People change phones, move homes and swap devices, but they often hold onto their email for years. That's the signal that protects your business, and the one we've built into the core of how we help you make decisions with confidence. View the press release here

August 6, 2026 by Zohreen Ismail
Building Financial Opportunity Through Purpose-Driven Partnership

Discover how the National Urban League and Experian partner to expand financial literacy and create economic opportunity.

August 6, 2026 by Scarlet Nickel

Subscribe to our Newsletter

Enter your name and email for the latest updates.

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Subscribe to our Newsletter

Don't miss out on the latest industry trends and insights!
Subscribe