The banking industry generates an enormous amount of data every day. Some of it comes from ATM logs, ACH transactions, SMS and online banking sessions, voice response systems, and more. Years ago, it wasn’t possible to collect, process, or store massive and complex data sets. Businesses had limited ways, if any, to leverage such data.
Today, there are a variety of technologies that have made big data a pivotal innovation driver in different industries. Big data analytics allows organizations to explore vast data sets to uncover insights like patterns and correlations, customer behavior, market trends, and so forth. This information helps managers to make informed decisions.
Impact of Big Data in Banking
Any financial institution that doesn’t jump onto the big data analytics train will have itself to blame for losing revenue. Studies have shown that the banking sector can attain about 18 percent revenue growth by making use of big data.
According to C-Suite banking executives, the modern customer wants highly personalized services. Big data in banking can help to meet customer demands, grow their business, and improve security and compliance. Here’s how banks can achieve this.
Enhanced Risk Management
Banks utilize business intelligence tools to identify potential risks related to lending money. With big data algorithms, lenders can identify customers with poor credit scores and decide whether to approve or decline their loan application. Big data analytics also assists banks in evaluating market trends and determine the opportune time to raise or lower interest rates for specific clients.
Big data in banking reduces the chance of data entry errors when filling out forms. By analyzing customer data, the system detects anomalies. Similarly, the bank can detect irregular transactions and potential fraud incidents and act accordingly.
For instance, if a person usually makes payments using a credit/debit card, an attempt to withdraw all their funds via ATM should be a matter of concern. It could mean a fraudster is trying to steal from the customer. The bank can call the account holder to clarify if the withdrawal is legitimate. Analyzing transactions using big data analytics has helped banks to ward off many fraudulent actions.
Personalization of Banking Solutions
Clients today detest the traditional one-size-fits-all approach to banking. People want banks that understand their needs and present sensible solutions. Consumers are likely to ignore banks that continually send mismatched offers. Annoyed customers won’t browse the rest of the portfolio, yet it could contain more exciting products.
Insights from big data analytics can help marketers to identify the type of products customers already have and what they would possibly want. They can then target individuals with products and services tailored for them from the point of understanding their needs. By doing this, banks can solve existing problems, win customer loyalty, and differentiate themselves from other financial institutions.
Big data can help banks to cross-sell auxiliary products more effectively by performing predictive analytics using wire data, check data, bill pay data, and credit card/debit card data. To succeed, the organization should focus on the value a product brings and the propensity of an individual to purchase it. A high-saving customer, for example, may be interested in tax-free investment opportunities like mutual funds.
Without information, organizations cannot avoid spamming consumers with unwelcome offers. For instance, it’s not worth the effort to market a short-term loan to a low-spending individual who is struggling with debts.
Banking technology and big data tools such as Hadoop and Fiserv can help automate the job. They can search through large data sets and enable financial institutions to make insightful decisions.
Transaction Channel Identification
Banks can benefit from understanding their customers’ preferred payment channels. Take the example of a business customer who prefers to make payments using paper checks. A business banker can reach out to this client and discuss treasury management service options that could help the customer’s business processes.
Businesses that are lagging in the big data analytics race are undoubtedly losing out in many areas. By utilizing big data in banking, banks are winning and retaining customers by offering personalized services and heightening security. Banks, on the other hand, are discovering new business opportunities while making their workplaces more conducive for their staff.
By utilizing big data in banking, banks are winning and retaining customers by offering personalized services by learning more about their customers’ needs. Banks are also discovering new business opportunities while improving risk management.
Insight Financial Marketing has over seventeen years of experience in helping banks identify opportunities to improve customer loyalty, grow revenue, and reduce potential risk through big data processing and analytics. Contact the IFM team to learn how your institution can begin to reap the benefits of utilizing big data in banking.
Rob Reale is an Associate Partner and National Sales Manager responsible for business development and sales at Insight Financial Marketing. Rob began working in the Mortgage Banking industry in 1990 and currently helps the financial service industry leverage unique and innovative solutions.