What is Big Data and how it is useful for Businesses ?

Big Data as the name suggests is a collection of humongous amounts of data that can’t be stored, managed and analyzed by traditional databases. Big data analytics uses advanced statistical and mathematical model such data mining, AI (artificial Intelligence), etc. to realize accurate & data backed business decisions. It helps businesses segregate their customers on the basis of product preference, age, purchasing powers; provide personalized services to their customers; realize cross-selling potential and many more.

Why Big data Analytics in Banking?

Banking sector is changing rapidly. Banks not only record millions of transactions daily but also these transactions are real time in nature. Gone are those days when we had to stand in long queues to deposit/withdraw cash or transfer money. These things are done in a fraction of second and that to from anywhere at any time. This is a win-win situation for both customers as well as banks. Customers get banking facility at their fingertips at the same time banks can optimize their resources to cut operating cost without compromising on customer service. The influx of huge data that the bank is getting through these transactions can help banks understand their customer better, offer personalized services to its customer, discover various product penetration, segregate product based on customer behavior (such as purchasing pattern among different age group, demography etc.), measure product performance, analyze market pattern, continuously improve customer service and so on.

But with these advancements come a lot of security threats. This is established by the fact that financial institutions across the globe loses in excess of 5% of their annual revenue due to fraudulent transactions. However, the actual damage is much higher. Further, a money laundering fine of around $ 5.6 billion issued globally in 2019. Moreover, financial crimes are rising globally with the rise of online transactions. For instance, in UK, approximately 12000 banking fraud cases were registered in 2018-19 which increased at an alarming rate of 40% from the crimes reported in previous fiscal. To our surprise, Cyberscout estimated that around 85 percent of identity theft is unnoticed by traditional monitoring tools.

So, whether it is identifying threat sophistication, lowering compliance costs or exploring new opportunities to understanding their customer completely, banks require advanced analytics tools like Big data analytics.

Types of Big Data

Banks generate 2.5 quintillion bytes of data every day. This huge amount of data available to banks needs to be processed to generate valuable insights. To do this, data is classified in three broad categories listed below: –

Structured– These are organized sets of data and have a fixed format.

Unstructured- These sorts of data lack are unorganized and do not have a clear format. A typical example is emails.

Semi-structured– These types of data are unstructured but have keywords to process them.

Advantages of Big Data in Banking

With the advent of banking facilities at customer fingertips, banks have the opportunity change their business model from conventional brick & mortar banking facilities to adapting and building a revolutionary online presence to cater their clients according to their specific needs and make their online platform robust and secure. Big data is a boon for banking Industry. Following are the major benefits of deploying Big Data analytics in banking.

Rise of fintech players in India

India beats China and USA in total number of Digital transactions done in 2020-