Big Data Analytics in Banking Sector

Big Data Analytics has become one of the main drivers of innovation in the banking industry. According to the IDC Semiannual Big Data and Analytics Spending Guide of 2016, investments in Big Data Analytics in the banking industry totaled $20.8 billion. The intense amount of investment makes the banking domain one of the dominant consumers of Big Data architects, solutions and bespoke tools. 

The PwC Global Fintech Report 2016 shows the allocation of funds within the amount of investment mostly targets customer support, risk assessment, decision making support and researching for new profit opportunities. Also, the investment in new markets, lessening the time-to-market and funding the blockchain projects. The trend has only increased the numbers in 2018. According to the GDC prognosis, the amount of data generated every second will grow up to 700% by 2020. The financial and banking data will be one of the cornerstones of the Big Data flood, and being able to process it all would be competitive in the financial institutions and banks. The Big Data flows can be really described in 3 v’s in businesses: variety, velocity, and volume.

It is described below how Big Data Analytics can be related to the banks: 

• Variety: Variety stands for the abundance of the data types processed, and the banks do have to deal with the variety of data. From the transaction history to credit scores and risk management reports – banks have troves of such data. 
• Velocity: It means the speed at which new data is being added to the database. Hitting the threshold of 100 transactions per minute is easy for a respectable bank. 
• Volume: Volume is defined as the amount of space required by the data to be stored in. Financial institutions like NYSE (New York Stock Exchange) generate terabytes of data every day. 

To be precise, Big Data analysis real time can be used to make business decisions accordingly. It can be applied to the following activities: 

• Customer spending patterns: The banks have direct access to the historical data regarding the customer spending patterns. Having an eye on every transaction, they know how much money is their customer being paid, how much is being saved, how much was sent to the utility providers, etc. By putting filters like festive seasons and macroeconomic conditions the banks can understand if the customer’s salary is growing. This is one of the fundamental factors for risk assessment, loan screening, mortgage evaluation and cross-selling of several financial products like insurance. 
• Transaction channel identification: The banks benefit greatly by understanding their customers’ financial behavior. They understand whether their customers withdraw the cash available on the payday, or they prefer to keep their money in their credit/debit cards.
• Customer segmentation and profiling: As soon as the analysis if customer spending patterns and favored transaction channels is complete, the customer database can be segmented according to various appropriate profiles. Knowing the financial profiles of all the customers, it helps the banks to evaluate the expected spending and income next month and make detailed plans to secure the bottom line and maximize incomes. 
• Fraud management and prevention: Understanding the usual spending patterns of an individual helps figuring out if something unexpected happens. If an investor who prefers to pay from his cards suddenly withdraws all the money from his account, it might imply that the card might have been used by a fraudster. A call from the bank for clearance can help to understand easily whether it is a legitimate claim or a fraudulent behavior that the card owner doesn’t have any idea about. Analyzing all types of transactions helps to cut down the risk of fraudulent activities. 
• Product cross-selling: Offering the investors a better return on interest can stimulate them to spend more and frequently. But is it worth providing a short-term loan to someone who is already struggling to repay a debt? Precise analysis of the customers’ financial backgrounds ensures the bank is able to cross-sell auxiliary products more efficiently and better engage the customers with personalized offers. 
• Risk assessment, compliance, and reporting: While trading stocks or screening a candidate for a loan, a similar procedure can be used for risk management. Analyzing the patterns of previous credit history of a customer can ease in assessing the risk of issuing a loan. Big Data algorithms can also help in compliance, audit and reporting issues in order to arrange the operations and remove the managerial overhead. 

• Customer feedback analysis and application: The customer can leave a response towards customer support through a feedback form, but they’re much likely to share their opinion through social media. Big Data tools can refine through this public data and collect all the mentions of the bank’s brand to respond adequately. When the customers see the bank hears and values their opinion and makes the improvements they demand — their loyalty and brand advocacy grows greatly. 

The companies must evolve and grasp new technologies if they want to succeed. Adopting Big Data analytics and diffusing it into the existing banking sector workflows is one of the key elements of surviving and prevailing in the rapidly evolving business environment of the digital millennium. 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.