Because of the confidential nature of data in banking services, most of the financial institutions have been slow in adapting to big data even though they do realize that there are huge benefits in terms of customer centricity.
As of today, 12% of banks are in the process of deploying big data through a big data consulting company, 25% of banks are expanding their big data implementations, while 38% are exploring their options and 25% are experimenting by implementing big data in limited environments.
There are various factors that influence the decision of banking services of whether or not to integrate big data. The following are the considerations that go into the decision-making:
1. Efficient risk management that helps detect errors and frauds in real-time
Business Intelligence (BI) tools that function on top of big data to provide analytics can identify the risks in sanctioning loans to potential customers. Banks can analyze the market trends according to regional data available and decide on lowering or increasing interest rates in that segment.
Errors while copying data from forms manually are reduced to minimum. Other data entry errors are also rectified before they can affect the working of the bank, as big data analytics can point out anomalies in customer data.
Bank frauds often go unnoticed till they disrupt the functioning of the banking services. With big data, banks can identify fraudulent transactions or entries at the onset as they vary from the acceptable standards set in the analytics dashboards.
2. Analyze consumer behavior and provide personalized banking solutions
Often, banks miss out on customers, as they do not connect emotionally with them. Sales representatives and relationship managers can leverage the inputs from the big data analytics that help identify investment patterns of the customers, their financial and personal backgrounds, and their motivations to invest, so that they can provide personalized investment solutions that are a combination of accounts, insurances, loans. Essentially, complete systematic investment plans that will ensure that the customers trust the bank with their finances.
3. Regulatory compliances are easier to file using big data
68% of bank employees say that their biggest concern in banking services is ensuring that they meet all the regulatory compliances set by the Government.
BI tools can help analyze the regulatory requirements by checking each individual application from the customers. When the regulatory compliance criteria are fed to the analytical dashboard, the business rules can be applied to big data to validate customer applications.
4. Performance analytics using big data help in budgeting and innovation
Branch goals are based on employee performance, and the targeted revenue for the year. Big data analytics can generate suggestions based on the figures available from the current sales of employees, and help bank allocate budget for each branch.
Even the services themselves can be analyzed for performance, to know what works and what needs to be changed. This fosters innovation amongst the marketing teams.
5. Maximize lead generation
Big data not only helps in existing customer retention but also in converting new customers through the personalized solutions that are discussed above in point 2.
1. Difficult to harness siloed data
Banking services data is highly diverse, and stored in different departments. It is difficult to profile a customer based on his/her investment behavior as his accounts, loans, insurances, etc. may be spread over various branches and departments of the bank. Big data needs to collate all such data first, in order to provide comprehensive intelligence.
2. Legacy infrastructure needs to be upgraded before integrating big data capabilities
Most banking solutions are not equipped to handle constant influx of data, which is a pre-requisite for big data, even if they have moved to cloud solutions. Integrating big data requires a complete revamp of most of the existing bank solutions in partnership with a big data consulting company.
This is not easy to implement, as the system needs to be constantly up even when the changes are being deployed.
3. Dedicated resources and tie ups with big data consulting company mandatory for correct implementation
As mentioned above, it is highly unlikely that banks would have in-house data experts in big data, and hence, partnership with firms specializing in designing, developing and deploying big data solutions is a must.
4. Big data is not yet viewed as a strategic asset
Non-technical managers and top-level executives often bypass the need of big data by relying more on human decisions rather than the automated marketing solutions offered by the analytics dashboard. This results either results in the bank not opting for big data or sidelining the actionable inputs from the big data implementations.
5. Customer concerns about privacy
Although the data logged by big data systems is anonymous at the high level, if the bank wishes, they can track behavior patterns of each individual customer. It is advantageous in detecting illegal activities, but is a serious security threat to the customer if it falls into wrong hands. Several concerns have already been raised with the Government about monitoring the use of big data.