Big Data Is Now An Essential Tool For Banking In The 2020s

Big Data is generally used to describe extremely large and rapidly changing volumes of data. Typically these are large data sets that include both structured and unstructured data and without a well-organized approach the volumes of data can be overwhelming.

Why is Big Data important for any modern business across all industries?

Typically, most companies focus on a much smaller set of data that they can control. A structured customer database, or a transaction history. This offers control over very specific processes, but does not allow analysts to create much insight. With Big Data there is the opportunity to mine the information to discover trends or correlations that may not be immediately obvious.

Why is this of particular importance to banks and the broader banking industry?

Banks are full of data. There is account information, customer information, rules and procedures around loans, and how interest should be applied. All customer processes have rules and mountains of data, but the real value can be found when applying additional data and then using a Big Data approach to try to find correlations that are not obvious or expected.

The critical difference is the use of Artificial Intelligence – and usually cloud-based storage so there is no limit to the amount of data used. Using smart systems that can scan internal and externally created data automatically and infer connections that would be hard for a human to manage with such a volume of data.

Credit analysis is a good example. Typically credit checks focused on internal data and were very specific. The bank looks at how much the customer has on deposit, how much they have already borrowed, their income, and any other factors, such as ongoing spending patterns over a period of several months.

Now it’s possible to introduce many more sources of information into this decision-making process. This might be more general home price dynamics in the area where the purchase is taking place, broad macro-economic trends related to home loans, property values in general, and the customer’s rental history if they are buying property for the first time. This can allow a much more accurate and dynamic approach to a process such as agreeing a credit.

However it’s not always as easy as just deciding we will now be a Big Data bank… a joined-up approach to how data is managed and used has to be applied across the organization first. Typically, these steps are required:

1. Break down the data silos, so data from across the organization can be accessed (this is where the cloud and a data lake approach may be useful

2. Introduce governance, so you have guidance and rules around data use

3. Leverage analytics tools – so you can move faster when starting to create new insights

4. Create an integrated data model, so data can move seamlessly

British bank, OakNorth, is a good example. By ensuring their data exists in the cloud they can rapidly innovate all aspects of their business lending service. Managing Big Data is all about creating new actionable insights. This can lead to faster and better service, new services and innovation, or an entirely different approach to managing a customer relationship. There are genuine business outcomes from this greater insight.

Big Data has moved on from being a desirable tool that can aid innovation to an essential tool that can support improved customer insights and new ways to manage existing business.

Banking is an industry that has always been filled with data. We have already wrote why Big Data in banking is set to explode. Moreover, the COVID-19 coronavirus pandemic has offered data analysts some great opportunities to study Big Data trends across the world. Read more about Big Data in a time of pandemic.