Big data is not just changing the data landscape of healthcare, human science, telecom, and online retail industries, but is also transforming the way financial organizations treat their massive data.
As shown in the following figure, a study by McKinsey on big data use cases claims that the financial services industry is poised to gain the most from this remarkable technology:
The data in financial organizations is relatively easier to capture compared to other industries, as it is easily accessible from internal systems—transactions and customer details—as well as external systems—FX Rates, legal entity data, and so on.
Quite simply, the gain per byte of data is the maximum for financial services.
Where do we get the big data in finance?
The data is collected at every stage—be it onboarding of new customers, call center records, or financial transactions. The financial industry is rapidly moving online, and so it has never been easier to capture the data. There are other reasons as well, such as:
- Customers no longer need to visit branches to withdraw and deposit money or make investments. They can discuss their requirements with the Bank online or over the phone instead of physical meetings. According to SNL Financial, institutions have shut 2,599 branches in 2014 against 1,137 openings, a net loss of 1,462 branches that is just off 2013's record full-year total of 1,487 branches opened. The move brings total US branches down to 94,752, a decline of 1.5 percent. The trend is global and not just in the US.
- Electronic channels such as debit/credit cards and mobile devices, through which customers can interact with financial organizations, have increased in the UK, as shown in the following figure. The trend is global and not just in the UK. Mobile equipment such as computers, smartphones, telephones, or tablets make it easier and inexpensive for customers to transact, which means customers will transact more and generate more data.
- Since customer profiles and transaction patterns are rapidly changing, risk models based on smaller data sets are not very accurate. We need to analyze data for longer durations and be able to write complex data algorithms without worrying about computing and data storage capabilities.
- When financial organizations combine structured data with unstructured data on social media, the data analysis becomes very powerful. For example, they can get feedback on their new products or TV advertisements by analyzing Twitter, Facebook, and other social media comments.