Too many buzzwords are used today by the technology community and specifically the financial and banking community. Among these tems we can hear of “Cloud”, “IoT” (Internet of Things), “Open Banking” and “Machine Learning”, and definitely the term “Big Data”, but just like the other mentioned terms a clear definition of the latter is not so easy to provide.
According to Joris Lochy, Product Manager and Big Data Expert at Minizze, Brussels; he considers that the general consensus is that “Big Data” is the collective term used for the contemporary methodologies and technologies used to collect, organize, process and analyse large, diverse (structured and unstructured) and complex sets of data, while “customer / real-time / predictive analytics” mainly refers to specific types of analyses done on these data sets to find patterns and create business value. However, since the ultimate business goal of Big Data is not the data itself, but to get business insights into the data, the analytics part of the chain is the most visible and important for a business user, which explains why the terms are often interchanged.
Characteristics of Big Data, the 5 Vs:
Spanish giant BBVA Bank tried to define the five keys to making big data a huge business which were later widely known as the Big Data’s 5 Vs
Volume: a vast quantity of data (i.e. terabytes or petabytes) to be handled. These huge amounts of data make it impossible to be processed by traditional data processing tools within reasonable time delays.
Velocity: Big Data technologies should be able to process both batch and real-time data. For real-time data, quick analysis for (near) real-time insight generation can be a necessity for the business.
Variety: multiple types of data should be supported, i.e. from highly structured data to unstructured info like text, video, audio, blogs, tweets, Facebook status updates…
Veracity: The fourth V is veracity, which in this context is equivalent to quality. We have all the data, but could we be missing something? Are the data “clean” and accurate? Do they really have something to offer?
Value: Finally, the V for value sits at the top of the big data pyramid. This refers to the ability to transform a tsunami of data into business.
Influence on the Banking and Financial Sector
In an article published in finextra magazine entitled “Big Data in the Financial Services Industry – From data to insights”, Mr. Lochy argues that the financial services sector is among the most data-intensive sector in the global economy, and the impact of Big Data on the sector clear.
Banks have huge amounts of customer data (i.e. deposits/withdrawals at ATMs, purchases at point-of-sales, payments done online, customer profile data collected for KYC…), but due to their storage structure as product-oriented organizations (not data oriented), they are not very good in utilizing these rich data sets.
Due to the increasing and changing customer expectations and the increased competition of Fintech players, the financial services sector can simply not permit itself to leave those huge amounts of data unexploited. Instead banks and insurers should leverage the existing (and new) data sets to maximize customer understanding and gain a competitive advantage.
Potentials of Big Data for the Banking Sector:
Big data gives you a full view on your business: from customer behavior patterns to internal process efficiency and even broader market trends. This means you can make informed, data-driven decision and, subsequently, obtain business results.
Its allows you to optimize and streamline your internal processes with the help of machine learning and AI. As a result, you get a significant performance boost and reduced operating costs.
Big data analytics in banking can be used to enhance your cybersecurity and reduce risks. By using intelligent algorithms, you can detect fraud and prevent potentially malicious actions.
1- Slow in Innovation
The banking sector has always been relatively slow to innovate: 92 of the top 100 world leading banks still rely on IBM mainframes in their operations. No wonder fintech adoption is so high. Compared to the customer-centric and agile startups, traditional financial institutions stand no chance.
However, when it comes to big data, things get even worse: most legacy systems can’t cope with the growing workload. Trying to collect, store, and analyze the required amounts of data using an outdated infrastructure can put the stability of your entire system at risk.
As a result, organizations face the challenge of growing their processing capacities or completely re-building their systems to take up the challenge.
2- The bigger the data, the higher the risk
Secondly, where there’s data there’s risk (especially taking into account the legacy problem we’ve mentioned above). It is clear that banking providers need to make sure the user data they accumulate and process remains safe at all times.
Yet, only 38% of organizations worldwide are ready to handle the threat, according to ISACA International. That is why cybersecurity remains one of the most burning issues in banking.
Plus, data security regulations are getting stringent. The introduction of GDPR has placed certain restrictions on businesses worldwide that want to collect and apply users’ data. This should also be taken into account.
3- Big data is getting too big
With so many different kinds of data and its total volume, it’s no surprise that businesses struggle to cope with it. This becomes even more obvious when trying to separate the valuable data from the useless.
While the share of potentially useful data is growing, there is still too much irrelevant data to sort out. This means that businesses need to prepare themselves and bolster their methods for analyzing even more data, and, if possible, find a new application for the data that has been considered irrelevant.
Despite the mentioned challenges, the advantages of big data in banking easily justify any risks. The insights it gives you, the resources it frees up, the money it saves – data is a universal fuel that can propel your business to the top.
The question is how to use big data in banking to its full potential.