BM calculates the flow of data from sensors gathering climate information, posts to social media sites, digital pictures and videos, purchase transaction records, cell phones, banking regulations and other sources is so strong that every day we create 2.5 quintillion bytes of it. Taken as a whole, that also means 90% of all the data in the world today has been created in just the last 2Ys alone. Technology is expanding at such a rapid pace; one has to wonder whether the human mind can keep pace with it all.
Our readers know we talk frequently about the importance of technology. In the 1980s and 1990s virtually every bank process went through a transformation from green eye-shades, pencils and paper, to computer-based processes. This was the first IT transformation in the banking industry and the result was greater accuracy, efficiency and a lot more data.
The industry has exploded beyond that now and new generations of IT transformation are taking place. One is due to customer preferences, as the primary interface has shifted to online banking and mobile is ramping up quickly. The other transformation may be one which bankers think of less but is equally important - using the data to analyze it for your competitive benefit.
For example, one of the most powerful uses of data and analysis is for loan underwriting. The accurate assessment of risk is absolutely critical to the process and this should include at least two areas. One is the customer and analysis of all aspects of the business they do with the bank, their personal financial information and credit history. The other is to look at specific data on loan types and by the region where the credit will be located. A good loan pricing solution should provide a risk-adjusted return considering all those factors. Having access to this kind of data is revolutionary when compared to underwriting even 10Ys ago and has reduced risk for banks that follow this type of process.
Data is not available in certain circumstances though, so it is a means to an end but not the end itself. For example, finding a probability of default on a given loan in a rural area may not be statistically valid (too small a sample) or even possible (banks may not share the information).
The sheer level of data flowing in and around your bank should not scare you, so don't be afraid to think outside the norm. For our problem above, in many small towns and rural areas it can be difficult to find comparable properties for real estate underwriting so this is where your knowledge of the market comes in. The big banks will not have their typical underwriting tools to leverage here, as their models are built with a nationwide scope. This can give you a competitive advantage but pricing consistency is important. One way to proactively address this is to be certain to document your own bank's data in the same language and using the same metrics as more widely available data in pricing.
The use of data and analytics can be a differentiator. In revolutions of the past, banks with ATMs had a competitive advantage until everyone got on board. The internet has created the same advantage for early adaptors and it is likely data and analytics will be a differentiator for a while as well. Using data to drive growth, understand nuances of risk and reduce costs should help bankers going forward. Embrace technological change to keep leading the way.