Schemes to Utilize Data in Banking Sector To Enhance Credibility and Customer Engagement

According to a survey conducted by the Small Business Administration (SBA), the number of loans taken by small business was in the range of 600 billion $. Technology is reshaping the very core of banking and allied activities.

However, there has been a fundamental shift in the approach of financial institutions since the digitization of banking processes. Twenty years before a financial institution would know every client personally. The clients will be having an acquaintance with the bank directly or indirectly. It worked as a double check on the credibility of the client, his present financial status, the integrity of his assets and lead to other prospective clients.

However, financial institutions in the digital era lack this kind of bonding with their customers. Today financial institutions care less about the customer relationship building but more about selling third party products.

The past decade was one of the most precarious decades in the financial sector with many countries facing bankruptcy, be it Greece or Venezuela. The bitcoins, petro-currencies, etc took this opportunity to disrupt the world economic order. So going back to the basics of banking seems to be the intelligent solution and luckily we have the right technology for it, i.e. Big Data Analytics.

Big Data analytics can help financial institutions build closer customer relationships and predict where the customer is heading financially. Some of the essential approaches to make such a system are :

Introduce data classification based on the customer type:

One of the primary reasons for the underuse of data from financial institutions is the difficulty in categorizing them. An excellent way to split the data flow for easy analysis would be by defining the starting points. Divide the data into two parts viz. Data of existing customers and Data of prospective customers. Further classifications in the analytics tool will help to pinpoint data flow. It could factor in on their spending habits, sudden behavioral change in expenditure and share trading, etc.

Structure of the massive influx of bank data to make it usable

Financial institution hold more data than any other industry. However, real efforts haven't happened in making them usable.
Prepare a standard flow process to capture data like the one below :

Data collection
Data storage
Analysing Data
Utilising Data

Create more pathways for data collection

Most of the clients will be having a virtual presence on the internet. Financial institutions can tap in on these user-generated data for analytics. It could provide insights into the customer’s social life and can help correlate signs of instability. It can help them identify prospective customers through networking. Further, it helps to monitor a customer’s credit and spending patterns in real time possible.
Pathways can be created through data collection through mobile app development, by creating a culture of data collection at branches, through text messaging and emails, etc.

Create Big Data Analytics tool with inputs from credit agencies

Creating data collection point is one thing, but to make it meaningful, you need detailed and precise analysis. The inputs from data collection points when merged with the credit agency’s data could give the actual financial status of customers. The lending institutions can look into the credit history of the client and measure their reliability. It will reduce risks in loans to a great extent.
It will help in mapping prospective customers who have high ratings. The banks can use this data to sell loan products and for expanding the business.

Use the analytics for Customer relation building

A bank stands on its worthy customers. But often financial stresses can create anomalies in the relationship with the customer. Here, Big Data can be used to win back aggrieved customers and to make up with them. The data from social media, mobile banking apps, etc. can be used to send them presents on birthdays or at least wishing them on special occasions. People forgive and move forward faster when they are happy.
Feedback points can be installed into such systems to generate data which will help in revising and restructuring the financial agency’s. It will help the governor’s, or manager’s to frame policies depending on the current expectations of customers and markets.

As technology widens its net, there are more chances of disruptions and irregularities from anarchist elements. A paradigm shift in banking with customer relations building and Big Data can be a solution. The future of financial institutions and banking, in particular, is very much dependent on Big Data analytics for churning out more meaningful insights from the vast data generated every day by the users.