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Strategic Planning for Mobile Users

It is a safe bet to assume that many credit union strategic plans include a focus on engaging mobile users. One of the first steps in engaging a consumer is to better understand them.

The Federal Reserve’s report, Consumers and Mobile Financial Services 2015, is nothing short of a treasure trove of data that has been reformulated into potential decision information. We’ll explore data about the various ages in which consumers use mobile banking.

Excerpt from Consumers and Mobile Financial Services 2015 Report

Mobile banking users by age
Table provided by Board of Governors of the Federal Reserve System

 

At first blush it may seem to confirm what we hear far too often – that Millennials should get the biggest allocation of marketing dollars – but it is important to not just view usage in absolute numbers. Understanding trends, including the rate of growth, can be eye opening. Viewing data from various perspectives can open doors for new opportunities.

If you take the data from the table above and dig deeper, you will see that, while one year does not make a trend, it should not be ignored that the youngest group had a decline in usage (see below). Meanwhile, the age groups that tend to have deeper relationships with their credit union show a healthy rate of growth.

Growth rate of mobile banking users by age group

 

Take Advantage of the Treasure Trove of Data

If you haven’t started already, dig into the data your credit union has for your members. As we’ve seen with so many strategic planning clients, once you have reliable data about what your members are doing with your credit union, you can easily start asking more of the right questions. Answering these types of questions, based on relevant data, will likely result in actionable decision information.

Keep in mind the more questions you answer, the more questions you will have!

Just a few questions to ask when strategically planning for mobile banking:

  • What are the behaviors of our members who are using our mobile banking at least 4 times per month for the last year?
  • What trends can be turned into strategic opportunities?
  • Do they have deeper, more productive, relationships than those who are not actively using mobile banking? If not, what can we do to deepen these relationships? For example, do mobile banking users tend to have more or less loans with us?
  • What other delivery channels do our mobile banking users tend to use, and for what?
  • If our mobile banking users are also consistently doing routine transactions in a branch or through the call center, why? Are there roadblocks that can be removed to make members’ lives easier that can also help us control costs?
  • What is our NSF/ODP income average per member for mobile banking users versus non-mobile banking users?
    • Some credit unions find that the average is lower for mobile banking users. If that is the case, then what does the credit union need to do to replace the inevitable decline in this type of revenue?
  • What age groups are using mobile banking to transfer funds to another financial entity?
    • Can we use this information to send targeted, relevant marketing messages to these members?
  • If we offer the likes of Apple Pay and Samsung Pay, how can we determine if our cards are top of their mobile wallet?

Many credit unions have access to this type of invaluable data, without significant hard costs to access it. The biggest investment is the time to ask and answer the thought-provoking questions. However, investing time to tap into member data and turn it into relevant decision information to drive strategic discussions, decisions, and planning is no longer optional.

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Strategic Planning: The Future of Money?

M-Pesa60 Minutes recently aired a segment on M-Pesa, an alternative currency, which is the preferred method for financial transactions in Kenya. In essence, M-Pesa allows cell phones to perform nearly all financial transactions without the use of a bank account or credit card. The full segment, which goes into more detail than this blog, is located here.

What makes the M-Pesa model unique from other non-traditional competition is the fact that financial institutions are taken out of the equation. The M-Pesa model is sometimes referred to as “bankless banking.” The 60 Minutes segment refers to the cell phone as a “bank in your pocket,” that allows customers to get a loan, pay bills, buy goods, and withdraw cash using PIN security. A plethora of mobile kiosks allows for easy conversion of cash to virtual currency; the need for branches, ATMs, and tellers is virtually non-existent. Less brick and mortar allows for better rates and lower fees for customers.

M-Pesa growth since 2009

No doubt, M-Pesa is an intriguing model but its long-term sustainability remains uncertain. M-Pesa in its exact form may not be the disruptor that changes banking in the United States. However, it is often the next idea (or the one after that) which springboards change.

Credit union planning sessions should include test drives of the future, including a banking environment where non-traditional competition like M-Pesa is a threat. Role playing a scenario like M-Pesa will also allow the credit union to see changes it may need to make today to ensure relevancy in the future.

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Observations from ALM Model Validations: For the Real Assumptions, Go to the Source

Do the assumptions outlined in your ALM model’s assumption summary line up with what is actually being utilized to calculate the results? In performing model validations, we have seen numerous instances in which there were discrepancies between reported assumptions and what was used in the actual reporting. This situation creates unnecessary challenges and makes it difficult for credit union staff or examiners to evaluate the reasonableness of the assumptions being utilized. Additionally, it can become much more difficult to understand the results and the changes in results.

For example, in one validation the deposit maturity assumptions actually used in the simulation were roughly 2x as long as the assumptions reported in the assumptions summary report. In another validation, the assumed rate changes (betas) on deposits were different than reported in the summary pages.

Mistakes can happen but it is important to identify issues and correct them as quickly as possible. One of the best ways to do this is to go right to the source. If you do not have reports directly from the model that detail exactly which assumptions are being utilized, you should work with your ALM provider to receive them with each simulation. It is good to check the summary information against the model at least annually and any time there are changes in assumption methodology. Seeing what is actually in the model provides better oversight and helps to avoid the risk of someone unintentionally entering something incorrectly in a summary.

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If You Think Changes in Payments Won’t Impact Your ALM and Interest Rate Risk Management―Think Again

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There are bites – small and LARGE – being taken out of credit unions’ non-interest income. Just consider:

  • PINless PIN.
  • Apple Pay – or the pay option du jour (e.g., Bitcoin, Samsung Pay, etc.).
  • The increase in payments via ACH, including P2P.
  • The decline in ODP.

As these bites are being taken out of revenue, decisions will need to be made as to how to compensate for the loss, not to mention the additional expenses associated with managing multiple payment options for your members.

If credit unions are not willing to accept lower earnings, then viable options are to:

  • Generate other non-interest income that is not influenced by interest rates.
  • Generate new interest income by accepting additional credit risk, interest rate risk, or both.
  • Be fanatical about continuous process improvement to better manage expenses.

Many high-functioning credit unions are proactively digging deep into their risks to non-interest income. They are quantifying the bites – small and LARGE – and forecasting trends to understand, well in advance, the potential impact to income. Identifying these risks, long before they become an unfortunate reality, opens up many viable options for risk management and mitigation.

Decision-makers of high-functioning credit unions are then investing the time to have strategic discussions. These forward-thinking discussions help decision-makers make rational, in-depth, strategic decisions versus having a knee-jerk reaction if the risks become reality. If it becomes necessary to take on additional credit risk or interest rate risk, then it can be done in a deliberate manner allowing decision-makers adequate time to test additional risks in small, manageable increments.

History has proven that the point at which you address a problem is directly related to the number of viable and desirable options you have to solve it. Don’t wait to address this issue.

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Observations from ALM Model Validations: Extremely Profitable New Business ROA in Static Balance Sheet Simulations

In this installment of our series on observations from model validations, we’ll focus in on the results from traditional income simulations, specifically static balance sheet simulations. We often see results that show low risk despite the credit union having a material amount of fixed-rate, long-term assets.

Take the example below which shows the NII results from a static balance sheet simulation. In year 1, the NII volatility is -15.62% in a +300 bp rate environment, which would be considered lower risk, and year 2 is even better at -8.45%. Keep in mind most policies have NII volatility limits of 20-30%, so this particular credit union looks pretty good. But why?

Static balance sheet showing NII volatility
While there could be a number of reasons, what we’ve found is that static balance sheet simulations assume the new business will always be extremely profitable if rates increase. The example below shows a credit union that has a base case ROA of 0.78% that jumps 153 bps to 2.31% in a +300 rate environment.

Static balance sheet simulation with new business ROA over 2{f36f94659acab79cca6adb0c2cb87abd9a89960d2b05b787f21b160005154717}

As we discussed in a previous blog, Observations from ALM Model Validations: Cost of Funds Back Testing, static balance sheet simulations assume that the deposit mix will not change as rates change, even though history suggests otherwise. It also generally assumes that a credit union could never have the loan-to-asset ratio drop, and often assumes the institution will be able to raise its loan rates 100% of the rate change.

Clearly, relying on a new business ROA north of 2% is not reasonable. These unrealistic assumptions about new business understate the risk of an institution.

Often when presented with this evidence, the response is that there is no way that such a high ROA is being assumed because the results show a decline in ROA for the first year (see example below). The reason is that often places only look out one year, maybe two. So the new business impacts the results but is smaller than the existing business.

To prove this out, look out at year 5 in your static simulation. You may not fully see the ROA over 2%, since most institutions are having the strain from their existing commitments holding the ROA back, but it is likely you will see an ROA that is above a level the credit union has ever experienced. If you want to see an even less defendable answer, look out at year 5 for a +500, and you will most likely see an ROA that far exceeds the earnings experienced the last time rates were at 5% (2006-2007).

Static balance sheet hiding critical new business assumption

Seeing results from this perspective, it is hard to call a static balance sheet a risk simulation.