As more and more organizations across industry verticals implement business intelligence (BI) to enhance their efficiency and reduce their workload, financial institutions are also no longer far behind in the technology race. Analytics and the use of machine learning have helped banking and financial institutions sort the data to increase its client retention capabilities. Big data is fed into BI solutions to discern the customers who are willing to carry on their business with the institution and those most likely to end their contract. These insights can be leveraged by banks and finance companies to offer more attractive incentives to drive customer satisfaction and increase loyalty.
The data analysis regarding discounts and other incentives has helped banks reduce financial loss and increase their profit margins by eliminating the unnecessary offers to clients that cannot pay back loans. Moreover, business intelligence analysis provides banks with the advantage of deciphering customer preferences and expectations from their credit and debit card statements, attraction to specific bank features, investment trends, point-of-sale data, online banking statements, and mobile payments. With these insights, the banks can build a customer-centric business model to match their customer preferences.
Even though it is hard for financial institutions to analyze the exponential amount of unstructured data and maintain data privacy, the positive effects outweigh the whole challenges of using data analytics.
