How Big Data can Help Community Banks
Big Data tools not only simplify lengthy analytical procedures in any industry, but they also provide a competitive advantage to banks. With new regulations, banks are looking at ways to make compliance procedures more effective and accurate. Big Data in banking is slowly gaining momentum and becoming an inevitable necessity across the banking industry. As traditional data management structures become obsolete, the community banks struggle to comply with external competitive and regulatory pressures.
The need to execute analytics tools in the community banking system is gradually becoming more of a compulsion than an option. Gartner’s 2014 CIO survey shows that financial firms encourage investing in data analytics when it comes to technology spending. While big banks have the clear advantage of understanding their customers through costly investments in data analytics, community banks appear to be a bit cautious in adopting the innovative trend. In order to implement the Big Data tools, community banks are faced with hindrances that revolve around high cost, lack of expertise and data security.
Community banks cannot always afford to implement analytics tools and infrastructure, plus hire professionals required for a new database.
In addition, data security related to private customer information is a sensitive issue for many community banks. Most thrive in local communities where there is a high level of trust between the bank and the customer. Through personal interactions and long-established relationships, community banks tend to have a deeper knowledge of the credit decisions of their customers than large banks do.
With the implementation of Big Data, community banks can keep close supervision and detect any real time fraudulent acts. Through predictive analytics, the banks can identify and monitor any discrepancies in customers’ account and even forecast a loan default. The community banks would also be able to detect high-risk accounts which can help them in making more informed decisions. But the reluctance in sharing private information remains an issue to many of these banks since they flourish on trust and confidence of their customers.
However, many community banks are embracing analytics solutions to make data-driven decisions that are entirely based on accurate risk analysis and transparency.
The rising interest in these banks to compete with and distinguish themselves from the technology-driven competitors is slowly gaining momentum. They are partnering with the cloud offering companies like Verizon Enterprise Solutions, Amazon, IBM and Google to get storage for Big Data and Analytics at a lower cost. Implementation of such services help community banks in creating a balance between cost and reward through real-time market feeds and social trends. Besides forming partnerships with services attained at a relatively lower cost, data visualization tools provide data trends to more employees within the bank instead of just a few staff members.
As operations grow increasingly complex, the need for data analytics tools also becomes essential for these smaller banks. According to the Bank of North Carolina (BNC), data visualization software could also help in simplifying the process of data reporting. Through SAS Visual Analytics, the data obtained is more enhanced and accurate than through traditional spreadsheets. Data visualization tools are reliant, less costly and help banks pick up trends quickly.
MX provides a similar kind of visual platform for banks which helps account aggregation, auto-categorization and money management features for potential account holders. Tools like Insight and Target help apply a more customized approach to different account holders at one time. Such analytical tools not only help position community banks amongst their competitors but also assist in tracking potential campaigns in less than five minutes with no need for IT. The deployment of such tools helps grasp the customers’ needs in real time.
Visual Analytics also proves useful during mergers and acquisitions by finding inaccuracies and thus confirming the authenticity of the data.
By leveraging Big Data, community banks can manage credit, liquidity and interest risk and serve the community better. As established perceptions of community banks slowly adapt to this change, a balanced approach through the integration of smart data governance, low cost and customer privacy can reinstate the lost trust and confidence of the people in the current banking system.
Link: How Big Data can Help Community Banks