Among the many critical transactions in a bank is managing checking accounts. What was customary was to send the depositor copies of their actual issued checks. This system involves people, transportation, and postage costs. With the onset of e-commerce, naturally banks began offering online services and check imaging is one of them.
Check imaging is supplying customers with electronic images of canceled checks instead of returning the actual checks. It has improved the banking system therefore improved customer service, transaction speed and fraud prevention. But providing poor check imaging service can significantly affect a bank in three broad areas: customer retention, cost, and legal ramifications. It can result in the following:
*Unhappy customers because of image mismatching, which happens when a customer’s check is incorrectly matched to another customer’s account.
*Higher costs are incurred as the high degree of mismatching was requiring the efforts of an entire full-time team to rectify the issues.
*Potential lawsuits in which The Privacy Act and the Patriot Act have increased the need for information to be accurately stored and appropriately communicated. Violating the Privacy Act leaves financial institutions open to lawsuits from those individuals and corporations whose information went astray.
Banks therefore wouldn’t want any of these to happen. That is why they turn to Lean Six Sigma. First they identified three key areas from which defects can arise, namely images are available; images received are correct; and images are legible. Using the Pareto diagram, they are able to examine the distribution of possible defects and get down to business.
The first action was to implement a Critical to Customer safeguard, ensuring the errors did not pile up at the customers’ doors. By implementing a tracking number checkpoint, mismatches would be caught before they reached the customer. The second action was to implement improved training and sorter operator incentives so as to ensure proper recovery procedures at the time of malfunction.
The team also built a Value Stream Map of the process. The Value Stream Map included key performance data such as wait times, setup times, rework loops and processing times. Creating the map highlighted wasted time and effort that usually isn’t apparent, even to people embedded in the process. Each step was categorized into value-add or non-value-add, and the team was able to identify and eliminate significant waste and dramatically decrease cycle time by removing non-value-add steps in the process. The result: the team was able to cut the cycle time by 50 percent, raising productivity levels and driving out $500,000 in cost.
