The data revolution is one of the biggest technology changes facing banks at the moment, and efforts to effectively harness data should be at the forefront of any financial institution’s strategy. But when it comes to making the best use of digital information, is there a difference between simply having access to this information and truly understanding what data we already have and how it can be used effectively?12
A few key use cases for big data have been around for some time. When it comes to fraud detection and prevention, for example, big data analytics tools can offer banks much better, real-time insight into potentially fraudulent transactions. But let’s not underestimate the potential that technology brings forth.
The benefits of being data-driven
Being able to take full advantage of big data analytics brings a wide range of benefits, from offering better products and services to consumers to gaining a deeper customer understanding on a more personal level.
For instance, banks may have traditionally segmented their customers into a number of groups based on key data such as demographics and income that determines what marketing offers are presented to them. But a data-driven approach does away with this in order to deliver more targeted messaging that reflects each individual’s unique circumstances, habits, and needs.
Overall, effective use of data provides banks with the possibility to move away from the use of general assumptions, which have pigeon-holed customers into categories, and move closer to real-world facts in order to make more informed decisions about aligning their customers with targeted products and services.
Another example of effective use of available real-time data is financial institutions improving their operations to become far more agile, as they now have the ability to lean into those changes rather than abruptly react, which quite often results in additional costs.
Making the move to a data-driven bank
Putting data at the heart of everything a bank does may seem like a major undertaking, particularly for smaller community banks that may not have the resources of their larger competitors. Indeed, a recent white paper from Moody’s Analytics noted that the biggest barrier to such moves is making sense of the “overwhelming number of options available”.
However, it also highlighted a few key best practices that all community banks should follow if they are to make data-driven banking a success. Among these, it said banks must prioritize and support investments that help centralize data and standardize process. Such investments must also be reinforced with new policies, training, and change management initiatives. Making sure senior staff can act as champions for the new systems, explaining how they will help drive success, is also vital.
This highlights that when it comes to making the transformation to a data-driven bank, having the right technology tool is only part of the equation to remove the complexities associated with data analytics. Banks will also need to put the proper ‘greeter’ programs in place so that employees actively embrace the change of modernization in order to acquire the confidence to make decisions based on the insights being provided through technology.
As senior director at Moody’s Analytics Nancy Michael stated: “Leveraging advanced data analytics and business intelligence tools is an investment that, if properly implemented, should pay dividends in the form of higher quality loans, better customer service and increased operational efficiency.”