Need for Big data analytics in Banking and financial sector

The banking and financial services industry produces a massive amount of data, totaling around 2.5 quintillion bytes. Every operation in this market leaves a digital footprint backed by data, which not only provides the banking industry with limitless opportunities to profit from that data but also poses a threat of fraud and data breach at the same time.

To respond proactively to these organizational dynamics, analysts must produce fast, accurate intelligence in order to assess risk and opportunities. However, data overload and an ever-changing information environment make this a difficult endeavor.

Innefu’s Prophecy, and Sas are examples of such solution to all your big data analytics needs. According to a study reported by the Business Wire, the global revenue for Big Data and business analytics solutions is expected to reach $260 billion by the end of 2022, and it’s time we used it optimally. This aids in enhancing the overall profitability, propelling the industry into a growth cycle.

Powerful search mechanisms :

The capabilities of Big data analytics tool help search important leads from the voluminous data available. The system is integrated with Data Sanitization and Data Type Segregation features that organize data in structured indices, helping in better analytics and tracking of all transactions.

Fraud detection and cybersecurity :

Using smart algorithms, Big Data-enabled systems can detect fraud. They help identify financial frauds by identifying anomalies in the data stream or behavioral patterns that are potentially fraudulent, thereby providing enormous value to the bank.

Customer management :

Big data analytics also offers inputs at each stage in the customer lifecycle, which considerably enhances customer experience. This helps in customer acquisition, experience enhancement, retention, and better customer relationship management. Such mechanism also help in personalized marketing by analyzing customer behavior patterns. By examining such patterns already present in locations where various products are offered, cross-selling of products can be done effectively. With the use of this study, banks will be able to target their sales and marketing efforts and determine which specific services should be sold to which customers. And all of this leads to cross-selling that is more successful, boosting revenue and improving customer relations. Cross-selling another product to an existing customer is very helpful because it might be difficult for banks today to keep one profitable customer.

Sentiment And Feedback Assessment :

Feedback and sentiment analysis are critical for system improvement, loophole detection, and proper work distribution. They are essential for emphasising both weaknesses and strengths. Analysis results will be erroneous if sentiment or feedback analysis is performed wrongly. Thus, the system as a whole will be inaccurate because the pattern recognized will also be inaccurate. By using big data analytics in the banking industry, banks are able to offer a strong strategy for analysing customer behaviour and responses. As a result, such feedback or sentiment analysis will aid in identifying and understanding prospective growth opportunities. If used frequently, they can also help reveal shortcomings in service delivery.

Loan Approvals and credit :

Lenders are becoming more sophisticated in terms of how they assess loan applications. Even though not everyone has a high CIBIL score/credit rating, they should still be able to get loans. Even with a few adverse things on their credit records, some people are still responsible borrowers. There’s also another category of people that have never developed credit. In such cases, it is essential to make this process of loan approval easy. Artificial intelligence and data analytics might make it easier for unconventional customers to get authorised. Using big data analytics tools, credit history and financial records of the customers can be generated. The system’s predictive capabilities will further calculate a credit score based on which the lender can decide if a person should be given a loan or not. Those who don’t have an already existing CIBIL score can also be eligible to get a loan because of system’s predictive and calculating algorithms.

More than 90 percent of the top 50 banks around the world are using advanced analytics. It’s time you also leveled up your banking analytics game and become efficient like never before!