In our article on ‘What is Big Data and Why Does It Matter?’ we established how access to data is now vital to the success of a business. We defined Big Data as large volumes of information that are easily described by the five V’s: volume, velocity, variety, value, and veracity. Big Data can either be structured, in that it is organized and easy to analyze, or unstructured, which means it is unable to be stored in definite or particular formats. It can even be semi-structured, which does not conform to data’s formal structures. No matter what type of data it is, the amount of information generated is greatly disrupting how industries work. One industry where Big Data is having a huge impact is banking.
Big Data, big investments
Data has become so prominent that companies are willing to invest in it, with 97.2% of businesses doing so. Forbes reports that 56% of CEOs have admitted that digital improvements increased their company’s revenue. This has led to large-scale corporations and banks investing a lot of money into the technology. CEO of IT Svit’s Fladimir Fedak states that the banking sector invested $20.8 billion in Big Data analytics in 2016. The banking industry has always kept large amounts of data on their customers, but through analytics this can now be used to improve the customer experience. This is why the investments are so big.
Formulating preventive measures
As banks become more aware of their customer needs, preferences, and behaviors through analytics, they are able to pick up on certain spending patterns, and take into account other factors such as macroeconomic conditions and a customer’s income. This means that banks are able to better assess risks, screen loans, evaluate mortgages, and cross-sell insurance. In observing transaction channels, they are also able to target customers who retain their money on their credit and debit cards to invest in short-term loans that have high payout rates. Not only does analytics track personal financial habits it also allows banks to easily detect fraud when something uncharacteristic happens to a customer’s account. This means the bank can quickly respond and limit any losses.
Machine learning and artificial intelligence
Banks are also using machine learning (ML) and artificial intelligence (AI) in combination with big data to improve customer retention. The Balance reports how AI can be used with predicative analytics to create algorithms that predict who among their current customers are most likely to lessen their involvement with the bank and maybe leave. As a result, the bank can react and work with the customer in order to reduce the risk of them leaving.
Bouncing back from losses
One US bank has even adopted this strategy to study the behavior of its private bankers and the discounts they were offering to their customers. McKinsey & Company’s financial services identified that it was the bank’s analytics that revealed that there was a pattern in unnecessary discounts. The private bankers claimed that they were only offering discounts to valuable customers and made up for the losses elsewhere with other high-margin businesses. After correcting the patterns that the analytics pointed out, revenues rose by 8% in just a few months.
Despite the hurdles, over 90% of the world’s top 50 banks make use of analytics, and this number will only increase in the near future. Statistics published on Maryville University show that banking will be among the top industries to use analytics in 2020, with the overall data industry predicted to have a compound annual growth rate of 11.7%. Worldwide revenue for Big Data is even predicted to reach $203 billion in the same year. A clear indication of how influential data has become and why the top financial institutes like banks are keen to invest billions into developing the technology, as well as integrating it into their own systems.
Big Data is rapidly revolutionizing the finance industry. As the widespread use of Big Data increases more banks will come to rely on it to improve customer service and their own internal systems. Even with the advances shown above, we are still only at the beginning of what Big Data has to offer.