Bankers know the value of the data they hold, but one key issue revolves around the best way to clean and access it all. Consider research from International Data Corporation (IDC) that projects worldwide revenues for big data and business analytics will soar to around $151B this year (+12% vs. 2016). According to IDC
, banking is one of five industries making the largest investments in 2017 in big data and business analytics so it is the subject of our discussion today.
Big data, which includes not only data gathering and analytics but also human behavior patterning and complex risk assessment modeling, has grown tremendously in the past few years.
While there are countless benefits that the industry's biggest players have been able to recognize because of their size, this advantage is starting to diminish. As data platforms and analytical tools have become increasingly sophisticated and affordable, they have also become more accessible. As a result of such advancements, community banks are now beginning to have the ability to mine customer data too.
From transaction data to financials, other records and credit ratings, there is a wealth of information about customers that can be gained from using data more effectively.
Certainly, doing so given the constraints of where the data came from, how messy it is and what the core systems will allow you to do comes into play too, but the trend is nonetheless slowly starting to move across the banking industry and others.
Eventually, the hope is that when you combine basic data with behavioral data, supercharged analytics will enable your bank to more quickly and accurately identify the products and services customers' most likely want when they are most receptive to buying those services.
Leveraging such things as the types of products customers have historically purchased; what kind of digital devices they use; the types of products they browse online and even their social media profiles and connections can help over time.
Another way to go is to ask customers for their preferences. According to a 2016 EY survey, more than 50% of tech savvy clients had no problem sharing information with their bank - as long as it was reciprocal.
An example of how this could work - if data analysis indicates a specific customer has been looking at businesses, the bank could take advantage of this information by presenting that same customer with a competitive offer for a loan. Still other data could be leveraged to perhaps even determine the creditworthiness of a new business customer so products and services can be better tailored to fit a given need.
Of course, the benefits of data analytics are not limited to analyzing bank customers, but can also help curb everyday business risks as well - everything from identifying fraud, to managing risks, monitoring the day-to-day activities of employees, and even financial trends can be part of this.
Though there is no doubt that community banks should embrace big data, it is important to do so in the smartest way possible. Start by referring back to your strategic plan to see how your bank intends to evolve over the next few years. Knowing this will allow you to see how data can be used to help meet your goals and to get you thinking about it.
No matter the path you take, suffice it to say the industry looks like it will keep digging into the data all around it, so community bankers should too.