Big Data analytics in the banking sector

Vladimir Fedak
May 29, 2018 · 6 min read

Big Data Analytics can become the main driver of innovation in the banking industry — and it is actually becoming one. We list several areas where Big Data can help the banks perform better.

Investments in Big Data analytics in banking sector totaled $20.8 billion in 2016, according to the IDC Semiannual Big Data and Analytics Spending Guide of 2016. This makes the domain one of the dominant consumers of Big Data services and an ever-hungry market for Big Data architects, solutions and bespoke tools.

Within this wealth of investments, the allocation of funds mostly targeted the customer support, risk assessment, decision-making support and researching for new profit opportunities along with investing in new markets, lowering time-to-market and funding the blockchain projects, as the PwC Global FinTech Report, published March 2016, shows.

The trend is growing and in 2017 these numbers became only bigger. The amount of data generated each second will grow 700% by 2020, according to GDC prognosis. The financial and banking data will be one of the cornerstones of this Big Data flood, and being able to process it means being competitive among the banks and financial institutions.

As we already elaborated while listing the types of Big Data tools IT Svit uses, the really big data flows can be described with 3 v’s: variety, velocity, and volume. Here is how these relate to the banks:

  • Variety stands for the plenitude of data types processed, and the banks do have to deal with huge numbers of various types of data. From transaction details and history to credit scores and risk assessment reports — the banks have troves of such data.
  • Velocity means the speed at which new data is added to the database. Hitting the threshold of 100 transactions per minute is easy for a respectable bank.
  • Volume means the amount of space this data will take to store. Huge financial institutions like the New York Stock Exchange (NYSE) generate terabytes of data daily.

However, as we explained in the article on the Big Data visualization principles, the 3 v’s are useless if they do not lead to the 4’th one — value. For the banks, this means they can apply the results of big data analysis real time and make business decisions accordingly. This can be applied to the following activities:

  • Discovering the spending patterns of the customers
  • Identifying the main channels of transactions (ATM withdrawal, credit/debit card payments)
  • Splitting the customers into segments according to their profiles
  • Product cross-selling based on the customers’ segmentation
  • Fraud management & prevention
  • Risk assessment, compliance & reporting
  • Customer feedback analysis and application

Below we elaborate on the examples of using Big Data in these fields of the banking industry.

Customer spending patterns

Transaction channel identification

Customer segmentation and profiling

Product cross-selling

Fraud management & prevention

Risk assessment, compliance & reporting

Customer feedback analysis and application

Final thoughts on using Big Data in the banking sector

We are all used to perceive the banks as huge buildings with cool marble halls where the clerks work with the customers. In the last 10 years, the banks invested heavily into modernizing their offers and providing mobile access to their services. In the next 5 years, they will have to learn to empower their operations with Big Data analytics, AI/ML algorithms, and other high-tech tools.

Initially, this story was posted on my company’s blog —

Data Driven Investor

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