Banking, financial services and insurance (BFSI) is a sector where institutions deal with enormous data where the operations are almost entirely digital now. Every function in the industry leaves a digital footprint creating tons of data. As the number of financial records keep increasing, organizations are more inclined to use Big Data Services to store information, analyze and derive business insights. It helps them to leverage their data and take important decisions. 

How it all began in the Banking industry? 

When banks started to digitize their operations, leading to creation of lots of data, they were looking to adopt Big Data technologies for leveraging them to create positive business impact. Data analytics simplified the monitoring of customer data and enabled better understanding of their customers. And the banks also started to trust technology on handling the high customer volumes. This also improved the service levels of the banking institutions. 

Thanks to Big Data, banks can now provide customers with the exact resources required based on their transactional behavior. The real-time customer evaluation improves the profitability and overall performance of the banking industry. 

The driving factors of Big Data in Banking 

The Banking industry is poised to become an integral component of the Big Data flood. The ability to process the data goldmines faster and deliver the insights accurately gives the competitive edge for any organization. Here are the 4 V’s of Big Data in Banking which acts as the lifeline for the banking enterprises. 

1) Volume – The space consumed by the data. 

2) Variety – It is the different types of data processed, right from transaction details to risk assessment reports and credit scores. 

3) Velocity – The speed at which data gets added to the database. 

4) Value – Correlating the results of Big Data Analytics to make the best business decisions. 

How Big Data supports Banking institutions? 

Banks use Big Data technologies for an intelligent analysis of their data. The analysis results open new doors to offer new solutions in detecting frauds such as credit card frauds and internal frauds. They also enable faster loan approvals. It helps to optimize business operations and improve customer service. Here are some of the ways how Big Data is helping banking executives.

1) Fraud Detection 

The most common application of big data is combating financial frauds in the financial industry. Credit card enterprises use data analytics to detect frauds. In case two POS payments occur in two different locations at the same time, then the transaction would be tagged as fraud. Big Data consulting services help to find frauds within the regulated financial institutions. Such internal fraud detections involve getting the data on employee transactions, web browser history, and phone conversations.

2) Credit Scoring and identifying potential borrowers

Predictability is an important criterion for money lending organizations. This is where Big Data gives the advantage over traditional loan approval process. Banks and financial institutions use credit decision engines to rate the customers’ credit score. This in turn predicts the borrowers’ payment behavior and creditworthiness. The robust computational engines help the organizations to make quick decisions and increase profitability. It also helps to avoids unnecessary risks by lending money to every applicant.

Banks see how customers withdraw their money in cash. There would be two different types of customers. First type: The customers would withdraw all money available on the payday. Second type: The customers would only withdraw some money at intervals, while using the remaining amount from cards. This customer data is filtered, and the latter customers are offered short-term loans with high payout rates.   

3) Better Customer Experience 

It becomes difficult for customers to choose a bank, when there are already many in the industry. Customer experience is a deciding factor that stands out. Data analytics services provide custom analysis according to each customer type. This gives the financial institutions the niche edge in enhancing their services and offerings. 

4) Personalized Marketing

Banking and financial organizations use Big Data to deliver a personalized marketing scheme to their customers. The potential customers are chosen based on the individual expenditures. The institutions also use sentiment analysis to analyze the customer behavior on social media. This helps them to draft a credit risk assessment, thereby offering customized services. 

5) A new approach to Operations Management 

Big data is used to bring immense value in the areas of operational risk assessment and management, credit management, and fraud management. Big Data & Analytics can detect fraud signals based on machine learning. It also helps in predicting the illegitimate transactions accurately, thereby signaling a caution flag. 

Conclusion 

Big Data gives a helping hand to the BFSI industry to completely understand the requirements from every type of customer. It is a powerful technology to align business strategies in the banking sector with global trends in customer service. This also enables to optimize the operational costs and enhance banking services step-by-step. The implementation of Big Data practices on a large scale has just started to see a big impact on the BFSI industry.