Using Quality Data to Retain Customers in Banking

Remember the days of “going to the bank?” This used to be a “thing,” a regular occurrence, often a destination, to withdraw money, make a deposit, speak to a financial planner, or just to see the friends that had been made throughout the years.  With the advent of online banking, “going to the bank” is no longer somewhere you go, but something you do. Despite offering the same services, the inability to build as many personal relationships has forced financial institutions to come up with new ways to differentiate themselves to help retain their customers.

According to The Financial Brand, to remain competitive, banks are investing extensively in IT systems because institutions have started to recognize that their vast data can help build relationship advantages, which is the first step to safeguard customer loyalty.

But with so much data flowing into and out of these systems, the only way for it to be useful is if it can be vetted and guaranteed to be of the highest quality.  To check for data quality, organizations can use an Enterprise Data Analysis platform, which certifies the success of any data migration and system conversion project by helping to eliminate inaccurate information or process errors.

With automated, independent and real-time analysis, financial institutions can reduce the risks associated with migrating data or converting systems, while ensuring the accuracy and completeness of the entire process.

Only an Enterprise Data Analysis platform can produce quality control systems to prevent the propagation of bad data. The platform must provide a mixture of techniques to deliver valuable data and those include:

• Identity Matching and Behavior Profiling – Discovering the true identity of your customer is the first step to protecting your business and growing your bottom line. Many times, a consumer appears to be a new prospect, but is actually a returning customer. If the previous customer relationship resulted in bad debt or other risks to your company, it would be great to know before allowing them back into your ecosystem where they might re-offend. By accurately identifying the customer, you reveal all past information from previous encounters with that person.

• Data Quality – Ensure the integrity of data by automatically monitoring data flow as it continuously moves throughout your enterprise. Transposed numbers, invalid account numbers, and empty values are just some of the issues that can derail processing, resulting in poor data quality. 

• Balancing and Reconciliation – Validate all enterprise information, instead of just sample sets and spot checks, to consequently eliminate the need for manual balancing. This allows for reductions in errors and reruns, duplicate payments and paper cost, and eliminates duplicate records and file processing. The confidence of knowing that all of your transactions and reports within your general ledger are trustworthy, will keep your business operations compliant and safe.

• Improve the Customer Journey – Whether its sending the right forms at the right time, providing 100% accurate account statements, or knowing when a customer is likely to churn – a good system of business rules and analytics can help your organization improve the customer journey, which will ultimately help you retain customers and improve your bottom line.

Customer experience is a major determining factor when it comes to a person sticking with their bank. The ability of an Enterprise Data Analysis platform to create personalized customer experiences doesn’t depend on the quantity of your data, but rather the quality of data that can be translated into actionable intelligence to build customer loyalty.