Big Data or Big BS in Banking

Banking is becoming increasingly and irreversibly data driven. The amount of information we collect on customers exceed most industries, with the possible exception of the medical industry. Much like medical, the information we collect is highly personal. Since outsiders (i.e. thieves) want our money, this data is constantly under hacker siege.

But, in my experience, bankers don't often effectively use the data they have for good. By good, I mean to improve customer service, market intelligence, and bank performance. Instead, we have core processors and component systems that don't communicate well, or are not used to their fullest potential. I reflected on this while reading a Harvard Business Review article on Making Advanced Analytics Work for You. The article contained three benefits from big data: Multple Data Sources, Prediction and Optimization Models, and Organizational Transformation. I thought of three similar approaches that are more applicable to banking.

Here is what I came up with.

How Financial Institutions Can Benefit From Big Data

1.  Manage Risk

Boards of Directors/Trustees, regulators, bureaucrats, consultants, etc. all have ideas on how to manage risk. Some are mandated, so I don't have anything further to say there. But data to help manage risk should be simple and effective. By effective, I mean it should lead to the decision to mitigate, accept, or reject the risk. Interest Rate Risk reports are a good example of data required to make this decision. But what are we trying to accomplish with IRR reports? I would say we are trying to estimate the impact to net interest income from interest rate fluctuations. By knowing these impacts, we can manage our balance sheet accordingly to mitigate the risk. But the 100-page report that includes items such as economic value of equity calculations seems unnecessary to me. I know of no bank that is valued in this manner. Let the quants disagree.

2. Measure Performance

Again, this can be a simple endeavor. There are managers that do detailed reporting to manage the performance of their direct reports, such as manual closed account reports that take time to prepare, and often don't lead to actionable items. But what if, instead of measuring branch managers by such granular information as account closings, you managed them on revenue growth. That is what one of our clients did using a similar report to the below table. Imagine the behavior changes and profit improvement that could ensue if branch managers owned their income statement.

3. Marketing Data

Think of a gung-ho business banker that wants to expand his or her client base and portfolio. Can he or she peruse the customer base via NAICS code around a geographic area to look for opportunities to expand relationships and profitability with existing customers? Is this data merged with non-customer data, so he or she can see businesses within a geographic footprint to bring in new customers? Can he or she see the customers of the bank's insurance, or investment subsidiary? Even if the bank aggregated the data, can the gung-ho banker access it without navigating some bureaucracy? I doubt it. We need to feed our front line this data so they can institute effective business development efforts to improve their performance.

These are the top three uses of Big Data for banking, in my humble opinion. In a time when we are deluged with huge volumes of information, how we capture and distribute relevant data is more important than the amount of data we capture. Narrow your needs, and build the applications to use data effectively and you will have an advantage over your competitors.

Do you believe that the sheer amount of data available inhibits effective use of data?

~ Jeff