Abstract:
A method is provided of processing a check in a payee positive pay system. The method comprises receiving a check from a presenting bank, obtaining an amount of the check received from the presenting bank, recognizing a payee name from the check received from the presenting bank, selecting one of a plurality of recognition confidence thresholds based upon the amount of the check, and based upon the selected one of the recognition thresholds, comparing the recognized payee name with a payee name contained in a check data file which has been previously received from a payor of the check to determine if there is a match of payee names.

Description:
BACKGROUND 
       [0001]    The present invention relates to processing checks in a payee positive pay system, and is particularly directed to methods of processing a check in a payee positive pay system. 
         [0002]    Check fraud is a problem which is costing banks significant amounts of money. One type of check fraud includes counterfeiting a check. Another type of check fraud includes forging a payor signature on a legitimate blank check. Still another type of check fraud includes altering a legitimate check, such as altering the amount of the check or altering the payee of the check. 
         [0003]    There are a number of known products available in the marketplace to detect fraudulent checks. One such product is a “positive pay” system in which a payor of a check provides his/her bank (i.e., the paying bank) with details of issued checks. These details are contained in a positive pay file which is electronically sent from the check payor to the paying bank. When a presenting bank presents one of the issued checks to the paying bank, the paying bank captures check data from the presented check and compares the captured check data to check details retrieved from the positive pay file to verify that the presented check has not been altered. The comparison is based primarily on the amount of the check and the serial number of the check to enable the paying bank to catch altered check amounts and duplicate checks. 
         [0004]    A known enhancement to positive pay systems also captures the payee name to verify that the presented check has not been altered. These enhanced positive pay systems are known as “payee positive pay” systems. In a typical payee positive pay system, a recognition engine is used to perform ink character recognition on a check to ensure that the payee name appearing on the check matches the payee name which the check payor originally put on the check. When the recognition engine is unable to ensure that the payee names match, the check is rejected and presented to a human operator for manual review. 
         [0005]    From time to time, a check has a correct payee name thereon, but is flagged as being a mismatch with the payee name that the check payor originally put on the check. This occurrence is known as a “false positive”. As an example, the rate of false positives may be ten percent. The total number of checks being processed by the recognition engine is usually relatively large. For example, over a 100,000 checks could be processed by the recognition engine in a single day. If 100,000 checks are processed and the rate of false positives is ten percent, then there would be approximately 10,000 checks for the human operator to manually review. Since the number of false positives presented to the human operator for manual review would be relatively large, an unfavorable business case may arise where the cost to review exceeds the cost of the fraud losses avoided. Or equally problematic, the manual review process may be unsuccessful because of the “needle in the haystack” syndrome in which the human operator may not be alert enough to identify and sort out the relatively few checks from a group of thousands of checks presented for manual review. It would be desirable to reduce the number of false positives presented to the human operator for manual review so that the human operator can focus on fewer checks and, therefore, perform the job more quickly and with greater accuracy. 
       SUMMARY 
       [0006]    In accordance with an embodiment of the present invention, a method is provided of processing a check in a payee positive pay system. The method comprises receiving a check from a presenting bank, obtaining an amount of the check received from the presenting bank, recognizing a payee name from the check received from the presenting bank, selecting one of a plurality of recognition confidence thresholds based upon the amount of the check, and based upon the selected one of the recognition thresholds, comparing the recognized payee name with a payee name contained in a check data file which has been previously received from a payor of the check to determine if there is a match of payee names. 
     
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0007]    In the accompanying drawings: 
           [0008]      FIG. 1  is a schematic diagram representation of an example known payee positive pay system; 
           [0009]      FIG. 2  is a flow diagram which depicts steps of a known process used in the system of  FIG. 1 ; 
           [0010]      FIG. 3  is a flow diagram which depicts steps of a known sub-process in the known process of  FIG. 2 ; 
           [0011]      FIGS. 4A and 4B  are tables which illustrate a known relationship between a confidence threshold and the number of false positives associated with this confidence threshold used in the known sub-process of  FIG. 3 ; 
           [0012]      FIGS. 5A and 5B  are tables similar to the tables of  FIGS. 4A and 4B , and which illustrate another known relationship between a different confidence threshold and the number of false positives associated with this confidence threshold; 
           [0013]      FIG. 6  is a flow diagram, similar to  FIG. 3 , which depicts steps of a sub-process in accordance with an embodiment of the present invention; and 
           [0014]      FIGS. 7A and 7B  are tables which illustrate a relationship, in accordance with an embodiment of the present invention, between tiered recognition confidence thresholds and the number of false positives associated with these tiered recognition confidence thresholds used in the sub-process of  FIG. 6 . 
       
    
    
     DETAILED DESCRIPTION 
       [0015]    A known payee positive pay system  10  is illustrated in  FIG. 1 . As shown in  FIG. 1 , a check payor  12  issues a check  13  to a check payee  14  who, in turn, cashes the check at a presenting bank  16 . The presenting bank  16  then presents the check  13  to a paying bank  18  in conventional manner. 
         [0016]    When the check payor  12  issues the check  13  to the check payee  14 , as just described hereinabove, the check payor electronically transmits a positive pay file  20  to the paying bank  18 . The positive pay file  20  and the process of electronically sending the positive pay file to the paying bank  18  are conventional and, therefore, will not be described in detail. Briefly, the positive pay file  20  includes details of checks which have been issued by the check payor  12 . The check details include check amount and payee name. Thus, the positive pay file  20  includes details of the particular check  13  which the check payor  12  issued to the check payee  14 . 
         [0017]    Referring to  FIG. 2 , a flow diagram  100  depicts steps of a known payee positive pay process. In step  102 , the paying bank  18  receives the positive pay file  20  from the check payor  12 . Further, the paying bank  18  receives checks from the presenting bank  16  requesting payment for the checks, as shown in step  104 . In step  106 , check data, such as check account number and check serial number, is extracted from a check (e.g., the check  13 ). 
         [0018]    A determination is made in step  108  as to whether or not the check  13  has corresponding check data contained in the positive pay file  20  which has been received from the check payor  12 . If there is no corresponding check data contained in the positive pay file  20 , the process proceeds to step  116  to alert a human operator (such as a person at the paying bank  18 ) of a potentially fraudulent check. The process then proceeds to step  126  in which a determination is made as to whether another check is available to be processed. If the determination in step  126  is affirmative, the process returns to step  106  to process the next check. Otherwise, the process ends. 
         [0019]    If there is corresponding check data contained in the positive pay file  20  as determined in step  108 , the process proceeds to step  112  to verify that the check  13  has not been altered. The verification occurring in step  112  is based upon a number of conventional positive pay tests, as is known. These conventional tests in a payee positive pay process would include a known sub-process  200  (such as shown in  FIG. 3 ) of payee name recognition and comparison of the recognized payee name with a payee name previously received from the check payor  12 . The known sub-process  200  shown in  FIG. 3  comprises step  202  in which a recognition engine performs recognition on the payee name from the check  13 . Then, in step  204 , the recognized payee name from step  202  is compared with a payee name previously received in the positive pay file  20  from the check payor  12 . The sub-process  200  shown in  FIG. 3  is conventional in known payee positive pay systems and, therefore, will not be described in detail. 
         [0020]    A determination is then made in step  114  as to whether or not the check  13  has been verified in step  112 . If the determination in step  114  is negative, the process proceeds to step  116  to alert a human operator of a possible fraudulent check. However, if the determination in step  114  is affirmative, the process proceeds to step  124  in which approval is provided to make payment in the amount of the check  13  to the presenting bank  16 . The process then proceeds to step  126  in which a determination is made as to whether another check is available to be processed. If the determination in step  126  is affirmative, the process returns to step  106  to process the next check. Otherwise, the process ends. 
         [0021]    Referring to  FIGS. 4A and 4B , a Table I and a Table II are illustrative of a known relationship between a confidence threshold and the number of false positive items associated with the confidence threshold when the known sub-process  200  shown in  FIG. 3  is carried out. It should be noted that a check item is considered to be a mismatch when the recognition confidence is determined to be below the confidence threshold. As shown in Table I in  FIG. 4A , when a confidence threshold of ninety-five (95) is used, there is a ninety-nine and one-half (99.5) percent detection rate of true fraud and a ten (10) percent rate of false positives. Given a total number of 100,000 check items and a ten percent rate of false positives for all check amounts, there would be a total number of 10,000 false positives (i.e., 10% of 100,000 items), as shown in Table II in  FIG. 4B . 
         [0022]    Referring to  FIGS. 5A and 5B , a Table III and a Table IV are illustrative of a known relationship between a different confidence threshold and the number of false positive items associated with this confidence threshold when the known sub-process  200  shown in  FIG. 3  is carried out. Again, it should be noted that a check item is considered to be a mismatch when the recognition confidence for the particular check item is determined to be below the confidence threshold. As shown in Table III in  FIG. 5A , when a confidence threshold of fifty (50) is used, there is an eighty (80) percent detection rate of true fraud and a one (1) percent rate of false positives. Given again a total of 100,000 check items and now this time a one percent rate of false positives for all check amounts, there would be a total number of 1000 false positives (i.e., 1% of 100,000 items), as shown in Table IV in  FIG. 5B . 
         [0023]    It should be apparent that in the known relationships depicted in  FIGS. 4A ,  4 B,  5 A, and  5 B, the total number of false positives for all check amounts decreases as the confidence threshold is set lower. However, there is a drawback to setting a lower confidence threshold because some check items will be missed as a true positive. While it may be acceptable to miss some lower amount checks which are true positives, it would not be acceptable to miss some higher amount checks which are true positives because this would result in too great of a financial loss. 
         [0024]    Referring to  FIG. 6 , a sub-process  300  in accordance with an embodiment of the present invention is illustrated. The sub-process  300  illustrated in  FIG. 3  is used in place of the known sub-process  200  shown in  FIG. 3 . More specifically, in step  302 , recognition is performed on the payee name from the check  13 . The amount of the check  13  is obtained from the positive pay file  20 , as shown in step  304 . In step  306 , one of a plurality of recognition confidence thresholds is selected based upon the check amount obtained in step  304 . These plurality of confidence thresholds are tiered as will be better explained hereinbelow with reference to  FIGS. 7A and 7B . Then, in step  308 , based upon the selected one of the recognition confidence thresholds in step  306 , the recognized payee name from step  302  is compared with a payee name previously received in the positive pay file  20  from the check payor  12 . 
         [0025]    Referring to  FIGS. 7A and 7B , a Table V and a Table VI are illustrative of a relationship, in accordance with an embodiment of the present invention, between the plurality of recognition confidence thresholds and the number of false positives associated with these plurality of confidence thresholds. As shown in Table V in  FIG. 7A , when the amount of the check item is up to $1000, a first confidence threshold of fifty (50) is used. When the first confidence threshold of fifty is used, there is an eighty (80) percent detection rate of true fraud and a one (1) percent rate of false positives. Also, as shown in Table V in  FIG. 7A , when the amount of the check item is between $1001 and $20,000, a second confidence threshold of eighty-four (84) is used. When the second confidence threshold of eighty-four is used, there is a ninety-seven and one-half (97.5) percent detection rate of true fraud and a three and one half (3.5) percent rate of false positives. Further, as shown in Table VI in  FIG. 7A , when the amount of the check item is over $20,000, a third confidence threshold of ninety-five (95) is used. When the third confidence threshold of ninety-five is used, there is a ninety-nine and one half (99.5) percent detection rate of true fraud and a ten (10) percent rate of false positives. 
         [0026]    Assuming that only about seventy-five (75) percent of all the check items being processed has an amount up to $1000 and given a false positive percentage of one percent for these checks, there would be a total of 750 false positives, as shown in Table VI in  FIG. 7B . Similarly, assuming that only about twenty (20) percent of all the check items being processed has an amount between $1001 and $20,000 and given a false positive percentage of 3.5 percent for these checks, there would be a total of 700 false positives, as shown in Table VI in  FIG. 7B . Again, similarly, assuming that the remaining five (5) percent of all checks has an amount over $20,000 and given a false positive percentage of ten percent for these checks, there would be a total of 500 false positives, as shown in Table VI in  FIG. 7B . Accordingly, the total number of all false positives using the tiered confidence thresholds of  FIGS. 7A  is 1950 (i.e., 750+700+500) as shown in  FIG. 7B . 
         [0027]    It should be apparent that the use of a plurality of different recognition confidence thresholds (i.e., the tiered confidence thresholds shown in Table V in  FIG. 7A ) results in a relatively higher percentage (10% in this example) of false positives for those check items which have amounts over $20,000, and a relatively lower percentage (1% in this example) of false positives for those check items which have amounts up to $1000. The percentage of false positives for those check items which have amounts between $1001 and $20,000 is 3.5% which is between the 10% (for check amounts over $20,000 ) and the 1% (for check amounts up to $1000). 
         [0028]    It should be noted that the total number of false positives in Table VI in  FIG. 7B  for check amounts over $20,000 and presented for manual review is 500. This 500 number in Table VI in  FIG. 7B  is the same as the total number of false positives in Table II in  FIG. 4B  for check amounts over $20,000 (i.e., 5% of all checks (100,000) is equal to 5000 checks, and a 10% false positives rate makes the total number of false positives equal to 500). While the total number of false positives in Table VI in  FIG. 7B  would be the same as the total number of false positives in Table II in  FIG. 4B  for amounts over $20,000 , it should be noted that the total number of false positives in Table VI in  FIG. 7B  for all check amounts (i.e., 1950) is significantly less than the total number of false positives in Table II in  FIG. 4B  for all check amounts (i.e., 10,000). The reduction from 10,000 to 1850 false positives for all check amounts is a significant reduction of the total number of checks which need to be reviewed by the human operator. Since the human operator has significantly fewer checks to review, the operator can better focus on the relatively fewer checks. With better focus, the operator can perform the job of reviewing the checks more quickly and with greater accuracy. 
         [0029]    Moreover, it should be noted that even though the total number of checks which need to be reviewed by the human operator has been reduced, the total number checks in the relatively higher amounts (i.e., over $20,000 ) in Table VI in  FIG. 7B  is substantially the same as the total number of checks over $20,000 in Table II in  FIG. 4B . In this regard, note from  FIG. 4B  that the total number of checks over $20,000 is 5000 (i.e., 5% of 100,000), and that the number of false positives for these 5000 checks is 500 (i.e., 10% of 5000). This number of 500 false positives associated with  FIG. 4B  is the same as the number of 500 false positives associated with  FIG. 7B . Accordingly, by using a plurality of different recognition confidence thresholds as illustrated in  FIGS. 6 ,  7 A, and  7 B, the result is a significant reduction of total number of false positives for all check amounts (as evidenced by the reduced number of false positives from 10,000 to 1950 for all check amounts as just described hereinabove) with essentially no reduction of the total number of false positives in the higher amount checks (as evidenced by the unchanged number of 500 false positives for check amounts over $20,000 also as just described hereinabove). 
         [0030]    Although the above description of  FIG. 7A  describes three different dollar ranges with different confidence thresholds, it is conceivable that less than three (i.e., only two) or more than three different dollar ranges with different confidence thresholds be used. It should also be noted that all of the numbers used in the tables of  FIGS. 7A and 7B  are just examples to show relationships. Accordingly, the specific dollar ranges illustrated in  FIG. 7A  are only examples, and the specific confidence thresholds illustrated in  FIG. 7A  are also only examples. 
         [0031]    Further, although the above description describes the presenting bank  16  presenting a physical check to the paying bank  18 , it is conceivable that the presenting bank may present an image of the check, instead of the physical check, to the paying bank. The presenting bank  16  would electronically transmit the image of the check to the paying bank  18 . 
         [0032]    Also, although the above description describes the check payor  12  electronically transmitting the positive pay file  20  to the paying bank  18 , it is conceivable that a check data file which is other than a positive pay file be electronically transmitted instead of the positive pay file. It is conceivable that the check data file includes check information such as the check account number and check serial numbers of completed checks which have been issued by the check payor  12 . It is also conceivable that the check payor  12  may log onto a website to type in the check information on a web-based form on a screen, or may access a custom application and enter in the check information. 
         [0033]    The particular arrangements disclosed are meant to be illustrative only and not limiting as to the scope of the invention. From the above description, those skilled in the art to which the present invention relates will perceive improvements, changes and modifications. Numerous substitutions and modifications can be undertaken without departing from the true spirit and scope of the invention. Such improvements, changes and modifications within the skill of the art to which the present invention relates are intended to be covered by the appended claims.