Digitalization has helped immensely in ease of doing business. Every banking and financial institute has shifted to online methods for their transactions. This shift has caused huge influx of data. According to a study by IBM, 2.5 billion gigabytes of data is produced globally every day and it predicts that by 2020, 40 trillion gigabytes of data will be produced in a day. Big data helps in data analytics by storing useful information. Banks and finance sectors have also struck a chord with big data, as these institutions have a huge pile of data in their database. The 3 v’s (velocity, variety, and volume) of big data can be used in banks and finance sector as follows:
Velocity: speed of data processing is of vital importance for banks as thousands of transactions happen within a minute.
Variety: Banks store transaction history, credit scores and credit history, customer’s information, risk assessment report. This forms a plethora of a variety of data to be analyzed.
Volume: Many financial institutions generate terabytes of data in a day, so they require a huge space to store their data.
The following points will show how big data can help in boosting the business of banks and finance sectors:
Spending patterns of customers: With a huge pile of data available, banks can now analyze a customer’s spending habits using their transaction and payment history through data analytics.
Risk Management: Using business analytics tool a bank can analyse a customer’s income and expenditure. This can help them in loan screening, mortgage evaluation and insurance. With predictive analytics, banks can predict a customer’s loan payment behaviour. Predictive analytics can help a bank in lowering the risks.
Customer Segmentation and Profiling: With the help of data available in the form of transaction history customers can be segmented in multiple profiles. With BI techniques banks can choose an appropriate profile for a certain marketing scheme.
Prevention and Detection of Fraud: With big data analytics, banks can detect any suspicious activity, and act upon it to stop any fraudulent activity.
Data is only as good as the information we can gain from it. An effective data analytics tool can help a bank in coping with the rigorous demands of the industry.