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Banking Special Issue: Relationship Banking

Table of Contents


The Future of Bank Imaging

The green wall

Relationship Banking

Proof of Deposit

POD Case Study

Designing Forms for a Banking Environment

File Folders

Thrashing Folders

Staging Folders

Using COLD (a case study)

Check Processing: The need for Speed

Printing Your Own Checks

Check Processing 101

Images from the Fed

First National Bank & the Fed (case study)

Glossary of Bank Image Technology


by Joe Devlin

Proof of Deposit Proves Critical


Stuck with high costs and high employee turnover, banks have proven more than willing to try technology that reduces costs and levels the workload to manageable proportions. Hence the interest in image-enabled proof of deposit systems. Here's a look at the latest POD products, and how one bank is using them to save early 50 percent in labor costs.

By Joe Devlin .

Image-enabled proof of deposit systems are a growing business opportunity for VARs.


In the critical proof of deposit (POD) stage the face value of checks is compared with the deposit statements and entered into the bank's computer database. The bank encodes or prints the correct dollar amounts on the bottom of the checks, bundles them into groups (cash letters), and delivers them to the other banks via the Federal Reserve system.

A data entry clerk scans and enters the check dollar amount (courtesy amount). The deposit slip is scanned and matched against the adding machine tapes of the deposits the teller received during the shift. If the deposit slip and courtesy amount don't match, the check is kicked back for a closer look. Or, in some situations, minor variations may be ignored, or the check amount may be defaulted to one number when the amounts entered are close.

In the conventional POD process, an encoding machine sends the checks to employees who enter the dollar amounts. This system is simple, relatively failproof and provides a good means of checking hard to read numbers. When the clerk can't decipher the courtesy amounts, they can simply read the handwritten, scripted dollar amounts. The cost of processing checks can be enormous, especially for bigger banks that process millions of checks every month. Labor costs are high and, because of the varying workloads, the work is very hard on data entry personnel. Federal law dictates that checks without sufficient funds to cover them must be returned to the issuing bank within 24-hours or the bank of deposit must cover the loss. Now, just imagine a situation where there is a heavy snowfall at the end of the week before a four day weekend.

The following Monday there could easily be four times the normal workload. Usually data entry personnel can't leave work until all the checks are processed (otherwise, the banks would have to swallow the cost of all the bad checks it receives). Not surprisingly, the turnover rate in this line of work is quite high.

The concept of image-enabled POD is relatively simple: give the computer first crack at recognizing the courtesy amount and deposit slip amounts of the check. After all, we live in the age of microprocessors where computer time is cheaper than human time, isn't it?

The software that recognizes the handwritten numbers on checks is a special- purpose intelligent character recognition engine called a Courtesy Amount Reader (CAR). Checks and deposit slips are sent through a reader/sorter that prepares check images and deposit slips for the CAR engines, which are engineered to read the courtesy amounts and extract the correct numbers from the chicken scratch entered there, no easy task. In fact, the very best CAR engines on the market boast 40 to 60 percent success rates.

This is simply not accurate enough for many document types. Would a legal secretary, for example, bother to correct a document recognized with only 80 percent efficiency? Not likely - it'd be much faster to type the document from scratch!

But with checks it's a different story. Forty percent accuracy means 40 percent fewer checks that the bank must pay someone to enter. The process is simple, if the recognition engine exceeds a certain confidence level, and that number matches the amount recognized on the deposit slip, then accept that amount. If the amounts don't match, pass the image to a human data entry clerk who verifies the recognition or keys in the correct amount. That's why bank MIS people will tell you that they are ecstatic about any automatic process that can automate 40 to 50 percent of the POD process for them.

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