In a previous blog (HERE), we took got bankers used to basic programming as we went hands-on learning Python, one of the most popular and versatile coding languages for banking applications. By the end of the lesson, we created a tutorial on how to program your own ROI calculator. For this article, we take another step towards teaching bankers the power of machine learning as we expand on the computational power of computer programming.
Banker To Banker
The most common complaint we hear from commercial bank calling officers is the lack of time available to prospect for new clients. Commercial lenders, banking, and calling officers spend less than one-third of their time selling. The majority of their time is spent on administrative work, credit and internal meetings. Furthermore, commercial calling officers get a second meeting with a prospect only 20% of the time, and a third meeting less than 10% of the time.
The European Banking Authority (EBA) just completed a survey of 37 of the largest banks on the Continent and showed the status of popular new technology initiatives (HERE). In this brief article, we recap the findings to give banks another perspective about potential technology projects.
As a general statement, banks offer too many options for certificates of deposits (CDs). Consider that the average bank offers 12 different maturities, some “specials,” plus several different tiers of pricing within each maturity. We have seen banks with as many as 42 different CD options which is inefficient for every party. The problem is too many CD offerings can increase a bank’s cost, confuse its customers and, worst of all – damage its overall deposit performance. In this article, we look at a counterintuitive strategy for increasing deposit performance.
Relationships between different variables can be very complex in the real world and banking is a perfect example of this complexity. The human mind, however, is skewed to perceive relationships in a linear fashion and this can lead bankers to make the wrong decisions. With the odd shape of the yield curve, we are seeing smart bankers making very profitable lending decisions because they understand their business model, the shape of the yield curve and the nonlinearity of commercial loan pricing.
We recently taught a banking class in bank technology and there was this pervasive undertone that artificial intelligence was the domain of large banks. Nothing could be farther from the truth. The state of machine learning is such that almost any banker with the curiosity to understand can leverage machine learning for a variety of tasks from marketing, to credit, to risk management.
While we prefer to target customers for intent instead of demographic background, the advertising world still largely breaks things down by age range. As such, banks that look to mass marketing within their communities still have to play the demographic game. However, this is an opportunity for community banks as many large banks still are targeting their mobile ads to just Gen Z and Millennials.
Sometimes in banking, the closing of a particular loan or deposit transaction drags on for no other reason than the customer is reluctant to agree to the terms either for spoken or unspoken reasons. At CenterState, we have learned some valuable lessons from other banks that have helped us close more transactions and can help every relationship manager gain more of an advantage to cut down closing times and increase their closing percentage. In this article, we break down these five lessons.
For all banks, the flattening yield curve is impacting profitability. The difference between the Two-Year swap and the Ten-Year swap rate is around 12 basis points. For banks over $15B, this flattening moves net interest margin (NIM) lower and then improves past the one year mark. However, for community banks under $15B, the flat curve not only moves net interest margin down, but this lower profitability becomes worse over time.