Turning Goals from Wishes to Outcomes

Community banks should measure their goals and objectives against four tests in order to craft sustainable approaches and outcomes.

Community banks set goals: growth targets for loans or deposits, an earnings target for the security portfolio, an return on equity target for the year. But aggressive loan growth may not be a prudent idea if loan-to-asset levels are already high entering a credit downturn. Earnings targets can be dangerous if they are pursued at any cost, regardless of risk. However, in the right context, each of these can lead to good outcomes.

The first test of any useful goal is answering whether it’s a good idea.

One personal example is that about a year ago I set a new goal to lose 100 pounds. I consulted with my doctor and we agreed that it was a good idea. So then we moved to the second test of a useful goal: Is it sustainable?

As “Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones” author James Clear puts it: “You do not rise to the level of your goals, you fall to the level of your process and systems.”

What good would my weight loss goal be if it wasn’t sustainable? If the approach I took did not change my habits and instead put me through a shock program, there would be little reason to doubt that the approaches and habits that led me to create this goal would bring me back there again. The only way to pursue my goal in a sustainable fashion would changing my habits — my personal processes and systems.

Banks often pursue goals in unstainable ways as well.

Consider a bank that set a goal in June 2018 of earning $3 million annually from its $100 million securities portfolio with no more than 5 years’ duration (sometimes called a “yield bogey”). Given a choice between a 5-year bullet agency at 2.86% and a 5-year, non-call 2-year agency at 3.10%, only the latter meets or beats the goal. A 3.10% yield earns $310,000 for this portfolio.

In June 2020, the callable bond got called and was replaced by a similar length bond yielding only 40 basis points, or $40,000, for the remaining three years. The sustainable plan would have earned us $286,000 for the past two years — but also $286,000 for the next three. To make earnings sustainable, banks always need to consider multiple scenarios, a longer timeframe and potentially relaxing their rigid “bogey” that may cost them future performance.

 The third test of a useful goal is specifying action.

The late New York Governor Mario Cuomo once said, “There are only two rules for being successful: One, figure out what exactly you want to do, and two, do it.”

In my case, I didn’t do anything unsustainable. In fact, I did not do anything at all to work toward my long-term goal. When I checked my weight six months later, it should not have surprised me to see I had lost zero pounds. A goal that you do not change your habits for is not an authentic goal; it is at best a wish.

My wish had gotten exactly what you would expect: nothing. Upon realizing this, I took two material steps. It was not a matter of degree, but of specific, detailed plans. I changed my diet, joined a gym and spent $100 to fix my bicycle.

The fourth test of a useful goal is if it is based on positive changes to habits.

Banks must often do something similar to transform their objectives from wishes to authentic goals. Habits — or as we call them organizationally, processes and systems — must be elevated. A process of setting an earnings or yield bogey for the bond portfolio relied on the hope that other considerations, such as call protection and rate changes, wouldn’t come into play.

An elevated process would plan for earnings needs in multiple scenarios over a reasonable time period. Like repairing my bike, it may have required “spending” a little bit in current yield to actually reach a worthy outcome, no matter which scenario actually played out.

If your management team does not intentionally pursue positive changes to processes and systems (habits), its goals may plod along as mere wishes. As for me, six months after making changes to my habits, I have lost 50 pounds with 50 more to go. Everything changed the day I finally took the action to turn a wish into a useful goal.

Practical AI Considerations for Community Banks

A common misconception among many community bankers is that it isn’t necessary to evaluate (or re-evaluate for some) their use of artificial intelligence – especially in the current market climate.

In reality, these technologies absolutely need a closer look. While the Covid-19 crisis and Paycheck Protection Program difficulties put a recent spotlight on outdated financial technology, slow technology adoption is a long-standing issue that is exacerbating many concerning industry trends.

Over the last decade, community banks have faced massive disruption and consolidation — a progression that is likely to continue. It’s imperative that bank executives take a clear-eyed look at how advanced technologies such as AI can support their business objectives and make them more competitive, while gaining a better understanding of the requirements and risks at play.

Incorporating AI to Elevate Existing Business Processes
This may seem like a contrarian view, but banks do not need a specific, stand-alone AI strategy. The value of AI is its ability to improve upon existing structures and processes. Leadership teams need to be involved in the development process to identify opportunities where AI can tangibly drive business objectives, and manage expectations around the resources necessary to get the project up and running.

For example, community banks should review how AI can automate efficiencies into their existing compliance processes — particularly in the areas of anti-money laundering and Bank Secrecy Act compliance. This application of AI can free up manpower, reduces error rates and help banks make informed decisions while better serving their customers.

It’s necessary to have a strong link between a bank’s digital transformation program and AI program. When properly incorporated, AI helps community financial institutions better meet rising customer expectations and close the gap with large financial institutions that have heavily invested in their digital experiences.

Practical Steps for Incorporating AI
Once a bank decides the best path forward for implementing AI, there are a few technical and organizational steps to keep in mind:

Minimizing Technical Debt and “Dirty Data”: AI requires vast amounts of data to function. “Dirty data,” or information containing errors, is a real possibility. Additionally, developers regularly make trade-offs between speed and quality to keep projects moving, which can result in greater vulnerability to crashes. Managing these deficiencies, “or technical debt,” is crucial to the success of any AI solution. One way to minimize technical debt is to ensure that both the quantity and quality of data taken in by an AI system are carefully monitored. Organizations should also be highly intentional about the data they collect.More isn’t always better.

Minimizing technical debt and dirty data is also key to a smooth digital transformation process. Engineers can add value through new and competitive features rather than spending time and energy addressing errors — or worse, scrapping the existing infrastructure altogether.

Security & Risk Management: Security and risk management needs to be top-of-mind for community bankers any time they are looking to deploy new technologies, including leveraging AI. Most AI technologies are built by third-party vendors rather than in-house. Integrations can and likely will create vulnerabilities. To ensure security and risk management are built into your bank’s operating processes and remain of the highest priority, chief security officers should report directly to the CEO.

Managing risks that arise within AI systems is also crucial to avoid any interruptions. Effective risk management ties back to knowing exactly how and why changes affect the bank’s system. One common challenge is the accidental misuse of sensitive data or data being mistakenly revealed. Access to data should be tightly controlled by your organization.

Ongoing communication with employees is important since they are the front line when it comes to spotting potential issues. The root cause of any errors detected should be clearly tracked and understood so banks can make adjustments to the model and retrain the team as needed.

Resource Management: An O’Reilly Media survey from 2018 found that company culture was the leading impediment to AI adoption in the financial services sector. To address this, leaders should listen to and educate employees within each department as the company explores new applications. Having a robust change management program — not just for AI but for any digital transformation journey — is absolutely critical to success. Ongoing education around AI efforts will help garner support for future initiatives and empower employees to take a proactive role in the success of current projects.

At a glance, implementing AI technologies may seem daunting, but adopting a wait-and-see approach could prove detrimental — particularly for community banks. Smaller banks need to use every tool in their toolkit to survive in a consolidating market. AI poses a huge opportunity for community banks to become more innovative, competitive and prosperous.

Artificial Intelligence: Exploring What’s Possible

Banks are exploring artificial intelligence to bolster regulatory compliance processes and better understand customers. This technology promises to expand over the next several years, says Sultan Meghji, CEO of Neocova. As AI emerges, it’s vital that bank leaders explore its possibilities. He shares how banks should consider and move forward with these solutions. 

  • Common Uses of AI Today
  • Near-Term Perspective
  • Evaluating & Implementing Solutions