Improving ROA and Data Visualization At Your Bank

The Data on Net Interest Margin

Data visualization is the presentation of data in a pictorial or graphical format. It enables decision-makers to see analytics more easily, grasp difficult concepts, identify new patterns, and explain outliers. Interactive data visualization also exists that allows users to step further into the data by drilling into charts and graphs in more detail. Most bankers are familiar with the application of data visualization with business dashboards that reveal trends or outliers. 

The concept of data visualization has been around for centuries. However, very few community banks are deploying the concept today.  We feel that big data is a great opportunity for community banks, but many banks are challenged in finding value in any data investment. Data visualization is one tool that can help bankers find value in their data no matter what size.  In this article, we use simple data visualization to highlight one relationship between net interest margin and bank performance.     

Data Visualization and Loan Profitability


Data visualization allows users to see data quickly, understand it more effectively, and identify which factors influence behavior and relationship between cause and effect. One of the earliest examples of data visualization occurred in 1854 in London, England.  In that year, in just ten days, 500 people died in one neighborhood from cholera.  No one understood the source of the deaths except John Snow, an epidemiologist who realized that the water supply was spreading the disease.  He plotted the geographical location of each death with a bar chart, showing that the closer to a water supply he plotted, the greater the number of deaths (shown in the graph below).  This information convinced the public that a sewage system was needed in London.  A very simple graph transformed a substantial amount of data into an easily identifiable and actionable remedy to mitigate a life-threatening disease.

Map of the London in 1854 showing the cholera outbreak around a particular neighborhood

Similar actionable reporting can help bankers understand how to price new loans, renewals and explain the relationship between spread and fee income, and profitability.  By using tools that take into account loan risk rating, loan size, geographic region, industry, and relationship profitability, bankers can make better pricing decisions for loans and deposits.  The graph below shows the relationship at the end of 2018 between net interest margin (NIM) and return on assets (ROA) for all banks in the country.  The graph demonstrates that no relationship exists between a bank’s NIM and performance as measured by ROA.   The correlation coefficient (R2) between these two variables is 0.05 (essentially no explanatory relationship).  We have measured the relationship between these two variables every year, and the relationship has been consistently absent – banks that can generate higher net interest margin are not more profitable as measured by ROA.

All Banks Charting NIM and ROA Correlation showing a cluster of data points with a 5% correlation

 We believe that this lack of relationship between NIM and ROA is the result of the other factors that are more important to profitability, such as loan credit quality, loan size, and relationship value. Because the vast majority of community banks do not use a risk-adjusted return-on-capital (RAROC) loan pricing model, the only variable that bankers focus on is loan yield – the most easily measured loan variable.  However, for a specific loan, NIM is one of the least important attributes to bank performance.  The market is efficient enough that strong borrowers, with sizable credits and that are willing to give a total relationship to a lender, also obtain lower-priced loans. 

The most common rebuttal by community bankers to using a RAROC loan pricing model is that small banks are price takers and need to price to market to win loans. That is a specious argument because the primary benefit of a loan pricing model is not to calculate the expected profitability of a loan, but to allow management to decide to make or not make the loan based on that measure - these are two very different objectives. 


First, we need to assume that the market for loan pricing is not perfectly efficient – mistakes are made on credit and relationship returns.  Let’s then take an example of a bank that makes 1% of the loans in a geographic area.  Let’s assume that the bank sees all loans that are made in the area and in a given year there are 10,000 loans.  The bank will make 100 of these loans (1% of the total loans originated).  Without a loan pricing model, the bank cannot expect to identify the most profitable loans in the area.  In fact, by having the ability to precisely quantify only the loan yield, the bank will, at the margin, make inefficient decisions by imprecisely measure credit quality, cross-sell opportunity, and business efficiencies. 




Data visualization can be a very effective tool for bankers to identify drivers of loan profitability.  This tool, coupled with a RAROC loan pricing model, can help community banks succeed by focusing on factors that drive loan profitability such as credit quality, relationship value, and business efficiencies instead of NIM.