Examples of Counterfeit Checks

From content alteration to mismatched data

Check Content Alteration

Why are financial entities having a hard time catching content alteration on checks?

Alteration of check contents is another type of check fraud that challenges banks and financial institutions. There are different schemes and techniques that allow perpetrators to modify information on the original valid checks. Some schemes require special tools and sophistication, for example lifting bank officer approval stamps from one check and including it on another check of higher value or altering the MICR line with bogus information. Other alterations are easy: in many cases check writers leave spaces and gaps when populating the various fields; dollar amounts, payees and dates can be simply altered using a pen. Here is where image analysis and pattern recognition technology offers efficient solutions to uncover check fraud.

Courtesy Amount / Legal Amount Mismatch Detection

Using ICR to read cursive handwriting, hand-print, and machine print

The core capability of recognition technology is to recognize cursive handwriting, hand-print, and machine print—individually or in any combination. For nearly a decade, this technology has been used to read the numeric amount (“Courtesy Amount”) and the text amount (“Legal Amount”) fields on checks providing the most reliable solution for Proof of Deposit (POD) and remittance applications.

Proprietary, universal algorithms and state-of-the-art technology allow the location of the amount fields on any check, regardless of the document’s layout or style, and yield high accuracy and read rates even if extraneous elements such as noise, inscriptions, and teller receipt or cash register print data are present on the document. This technology also reads all kinds of other fields on checks and financial documents: payee name, check date, check number, MICR line, different alpha, alpha-numeric and numeric fields, etc.

Cross-Validation Matters

Advanced algorithms that reduce manual labor

Positive pay services eliminate the need to review each check, helping companies gain control of the exceptions process and significantly reducing write-offs. Until recently, positive pay systems relied heavily on manual labor in reading the data from a check image and thus were very expensive to implement and manage.

Sophisticated algorithms not only read numeric and text amounts and catch the potential discrepancy between them, but they also consider behavioral characteristics in writing checks and distinguish mismatches that may result from fraudulent activity from the typical check writer’s errors and abbreviations. This helps to reduce the number of false alarms and unnecessary human intervention.

Courtesy and legal amount are automatically recognized and cross-validated to detect discrepancies