Abstract:
The present invention is a method and apparatus that uses a plurality of predetermined segments to group credit applicants to evaluate each applicant&#39;s credit risk. The segments are based on at least one of reported trades, reported delinquency, bank card utilization, and length of said credit history. A score is generated for each applicant based on a unique scorecard designed for each segment The unique scorecards allow more accurate credit risk assessment by evaluating each applicant in view of that segment&#39;s tendency to be bad credit risks.

Description:
FIELD OF THE INVENTION 
     The invention relates to a method and apparatus for assessing the credit risk of bank card applicants. Specifically, the method and apparatus relate to developing a segmentation tree to group bank card applicants into similar sub-populations and then building a custom scorecard for each of the sub-populations. 
     BACKGROUND OF THE INVENTION 
     Individuals seeking bank cards from a financial institution typically fill out applications providing information and requesting a bank card. Financial institutions lend credit to individuals based on the information provided in the applications and their credit history. The financial institution reviews the application provided by the applicant and reviews the applicant&#39;s credit history. The goal of the financial institution is to asses the credit risk of each individual bank card applicant so they will not extend credit to an individual that is a poor credit risk. 
     To assess the credit risk of each individual, the financial institution will develop a score for each applicant based on certain information. The applicant receives points for each item of information analyzed by the financial institution. The amount of points awarded for each item, the items actually analyzed, and the scores necessary for approval vary from financial institution to financial institution. 
     In today&#39;s market, financial institutions are approving more and more bank cards and are experiencing increased competition from other financial institutions for the applicants. Financial institutions would generally grant the applicant a bank card provided the applicant has an acceptable source of income and is not 120 days or more past due with another account. 
     The decision to approve or deny the applicant&#39;s request for a bank card was based on a scoring system. The financial institution scored each bank card applicant based on source and level of income as well as whether the applicant was ever 120 days past due. The scoring system used to evaluate each applicant and the minimum score required for approval was applied uniformly by a financial institution to all its applicants. Each institution had a single scorecard and approval score with which to assess the credit risk of all its bank card applicants. The problem faced by many financial institutions is that a significant number of bank card applicants approved become 90 days past due in the first two years or even declare bankruptcy. The financial institution is faced with the choice of increasing the score required for approval or closely monitoring the approved applicant&#39;s use of the bank card. Increasing the score would result in declining a large number of the applicants. This choice would cause a lot of the potential customers to be driven to the financial institution&#39;s competitors. Monitoring the approved applicant&#39;s use would require an increase in the cost to the financial institution for maintaining the bank card. 
     SUMMARY OF THE INVENTION 
     In accordance with a broad, general feature of this invention, a method for assessing credit risk and apparatus adapted for performing the method are provided. The method involves developing a segmentation tree, building a custom scorecard for each segment developed by the segmentation tree, grouping applicants into sub-populations corresponding to each segment, and applying the custom scorecard to the applicants within the corresponding segment. 
     The apparatus includes a central processor with a data bank into which data is written and from which data is read, a work station for processing applications, and a communications link for providing access to central processors outside the financial institution. 
     The method lowers the risk of a financial institution approving a poor risk application. By developing custom scorecards for different sub-populations of applicants, a financial institution is able to more accurately assess the credit risk of each applicant. A custom scorecard takes into account information that has been determined to be the most relevant for the applicants in that sub-population. This allows the financial institution to use a scorecard designed for a particular group of applicants based on that groups tendency to be a poor credit risk. 
     Use of an automated system to implement the generation of the custom scorecards and scoring the applications further lowers the cost to the financial institution of assessing credit risk. Automation of the credit risk assessment allows the financial institution to quickly, effectively, and inexpensively process a large number of applications. Automation also allows accurate assessment of each application generating reliable uniformity to the process. The method and apparatus lower the risk and cost to the financial institution in approving or denying bank card applications. 
     Other objects and features of the invention will become apparent as the description proceeds, especially when taken in conjunction with the accompanying drawings illustrating the invention, of which there are seven sheets and three embodiments. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 illustrates a scorecard segmentation scheme according to one embodiment of the invention; 
     FIG. 2 illustrates a segmentation scheme according to one embodiment of the invention with sub-population groupings for a representative sample; 
     FIG. 3 illustrates a segmentation chart involving twelve nodes including sample data according to a second embodiment of the invention; 
     FIG. 4 is a table summarizing the sample results for a method utilizing eight segmentation groups according to one embodiment of the invention; 
     FIG. 5 is a table illustrating the credit bureau match rates for the sample analyzed according to one embodiment of the invention; 
     FIG. 6 illustrates a segmentation scheme according to a third embodiment of the invention; and 
     FIG. 7 illustrates a segmentation scheme according to the third embodiment of the invention with sub-population groupings for a representative sample. 
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     All of the applicants who request bank cards are grouped into a total population. The total population is broken down into segments using a segmentation tree. The segmentation tree generates a plurality of segments into which similarly situated applicants can be grouped. Each segment of the segmentation tree defines a group based on information related to credit history. Sub-populations of the applicants are defined based on the segments generated by the segmentation tree. 
     FIG. 1 illustrates a segmentation tree  14  according to one embodiment of the invention. The segmentation tree  14  breaks down a total population  12  into eight segments. The segments are generated based on four pieces of credit history information. The segmentation tree  14  includes a first branch  16 , a second branch  18 , a third branch  20 , a fourth branch  22 , a fifth branch  24 , and a sixth branch  26 . 
     The first branch  16  is developed using the amount of detail in the credit history. The first branch  16  creates two groupings, one group creates a first segment  28  and a second group  30  continues for further segmentation. The first segment  28  defines a sub-population that has a thin credit file. A thin credit file encompasses credit histories of people that have not had a bank card or too short of a credit history. An applicant with a thin file generally refers to an applicant with fewer than three trades. The group  30  includes credit histories with thick credit files. Thick credit files means the history has data on three or more trades. 
     The second branch  18  further segments group  30 . The second branch  18  is developed using the delinquency reported in the credit history. The second branch  18  creates two groupings  32  and  34  which are segmented further by the segmentation tree  14 . The group  32  includes credit histories with no recent delinquencies and only minor delinquencies in the entire history. Minor delinquencies are where the account was not 60 or more days delinquent. Recent delinquencies are ones that occurred within the last six months. The group  34  includes credit histories with recent and/or severe delinquencies. Applicants with no delinquencies or very old minor delinquencies are generally refereed to as having a clean credit history. Those with moderate or severe and recent delinquencies are generally referred to as having a dirty credit history. The severity index used to determine whether a credit history is clean or dirty is taken as a combination of severity and recentness of the delinquency. In one embodiment, a clean credit history would have a severity index value smaller than four. 
     The third branch  20  further segments group  32 . The third branch  20  is developed using the bank card utilization reported in the credit history. The third branch  20  creates three groupings, one group defines a second segment  36 , one group defines a third segment  38  and the last defines a group  40  which is segmented further. The second segment  36  defines a sub-population having credit histories with low bank card utilization. Bank card utilization is a percentage of the ratio of balance to limit on revolving and national trades. Low utilization for the second segment  36  is a utilization of 25% or less. The third segment  38  defines a sub-population having credit histories with medium bank card utilization. The medium utilization of the third segment  38  is a utilization of 25% to 70%. The group  40  includes credit histories with high bank card utilization of at least 70%. 
     The fourth branch  22  further segments group  40 . The fourth branch  22  is developed using the time span of the credit history. The fourth branch  22  creates a fourth segment  42  and a fifth segment  44 . The fourth segment  42  defines a sub-population having credit histories going back more than ten years. The fifth segment  44  defines a sub-population having credit histories going back ten years or less. Applicants with credit histories that span more than ten years are generally referred to as having old credit histories and applicants with credit histories spanning ten years or less are generally referred to as having new credit histories. 
     The fifth branch  24  further segments group  34 . The fifth branch  24  is developed using the bank card utilization reported in the credit history. The fifth branch  24  creates a sixth segment  46  and a group  48  which is segmented further. The sixth segment  46  defines a sub-population having credit histories with low bank card utilization of 40% or less. The group  48  includes credit histories with bank card utilization of greater than 40%. 
     The sixth branch  26  further segments group  48 . The sixth branch  26  is developed using the time span of the credit histories. The sixth branch  26  creates a seventh segment  50  and an eighth segment  52 . The seventh segment  50  defines a sub-population having old credit histories spanning more than ten years. The eighth segment  52  defines a sub-population having new credit histories spanning ten years or less. 
     A separate scorecard is developed for each of the eight segments defined by the segmentation tree  14 . Each scorecard can be developed to accurately score similarly situated applicants in each of the eight defined sub-populations. As can be seen in FIG. 2, each of the defined sub-populations has a different bad account percentage. In addition, the criteria indicating whether an account is likely to become bad varies from sub-population to sub-population. 
     In order to test the validity of the defined sub-populations, a representative sample of past applicants were rescored with the new methodology and compared with their actual credit history. The goal was to estimate the probability that an account would be 90 or more days past due in the first two years. Bad accounts were defined as accounts that were 90 or more days past due and the rest were termed good. FIG. 2 shows the breakdown of the percentage of the sample with the percentage of accounts for each of the eight segments developed using the segmentation  14 . 
     The total population  12  included 100 percent of the representative sample and had a five percent bad account rate. The first segment  28  included the portion of the sample that had thin credit files or no bankcards. This represented 6.5 percent of the total sample and the sub-population defined by the segment  28  had a 6.7 percent bad account rate. The rest of the total population  12  with thick credit files including bankcards were placed in the group  30 . Those in group  30  were split next into the two groups  32  and  34 . This split was based on the severity and recentness of any delinquencies. Group  32  included those applicants in the sample with thick credit files and no or old delinquencies (clean) in their file history. The group  32  included portions of the sample with thick credit files and moderate or recent delinquencies (dirty). The portion of the total population  12  that fit into group  32  were further split based on bankcard utilization. The second segment  36  defined a sub-population of the sample with thick, clean credit files where the bankcard utilization was 25 percent or less. The sub-population defined by the second segment  36  included 29.9 percent of the representative sample and had a 1.4 percent bad account rate. The third segment  38  defined a sub-population with thick, clean credit histories and a bankcard utilization of 25 to 70 percent. The sub-population defined by the third segment  38  included 27.9 percent of the sample and had a 4.2 percent bad account rate. 
     The group  40  included portions of the total population  12  with thick, clean credit histories and a bank utilization of more than 70 percent. The group  40  included 11.4 percent of the representative sample and had a 9.1 percent bad account rate. Based on the high rate, the group  40  was further segmented into the fourth segment  42  and the fifth segment  44  based on the time span of the credit history. The old credit histories (more than 10 years) where grouped in the fourth segment  42  and the new credit histories (10 years or less reported) where grouped in the fifth segment  44 . 
     The group  34  included those members of the sample with thick, dirty credit histories. The group  34  was also split based on bankcard utilization. The sixth segment  46  defined a sub-population with thick, dirty credit histories and a bankcard utilization of 40 percent or less. The sixth segment  46  included 13.4 percent of the sample and had a 5.1 percent bad account rate. The group  48  included those members of the total population  12  with thick, dirty credit histories and a bankcard utilization of 41 percent or greater. The group  48  was split further based on the time span of the credit histories. A seventh segment  50  included those members of the population  12  with thick, dirty credit files and bankcard utilization of 41 percent or higher with old credit histories. The seventh segment  50  included 4.6 percent of the sample and had a 9.5 percent bad account rate. The eighth segment  52  included those members of the total population  12  with thick, dirty credit histories and 41 percent or greater bankcard utilization with a new credit history. The eighth segment  52  included 6.2 percent of the sample and had a 12.8 percent bad account rate. 
     FIG. 4 shows a breakdown of the eight defined sub-populations with the number good and the number bad from the representative sample. A scorecard for each of the eight sub-populations was developed taking into account the likelihood an account would ever be 90 days or more past due. The scorecards were developed using the criteria validated with the sample population. Each scorecard was tailored to accurately analysis the members in the sub-population to which it pertained. 
     Next, the sample population was analyzed using the newly created scorecards. A test run of the system was conducted to indicate which of the sample population applications would be accepted and which would be rejected. The applications rejected were the ones that the scorecard indicated would likely be 90 days or more past due. The credit bureau files were then analyzed to determine the actual credit history of the sample population to determine the accuracy of the scorecards. FIG. 5 summarizes the credit bureau match rate for the sample population scored by the custom scorecards. 
     FIG. 3 illustrates a second embodiment according to the invention using a classification and regression tree (CART)  100  to break a total population  112  into sub-populations or nodes. The CART  100  includes 12 nodes. As the total population is divided, separate sub-populations or groups are defined. Each node defines a sub-population and each group is further divided by the CART  100 . 
     The total population  112  is first split by determining whether the credit history has a satisfactory trade ratio. The first node  114  defines a sub-population with a less than 71 percent satisfactory trade ratio. The potion of the total population  112  with a greater than 71 percent satisfactory trade ratio are included in group  116 . Group  116  is further broken down into two groups  118  and  120  based on bankcard utilization. Those members of group  116  with a bankcard utilization of less than 40 percent are included in group  118 . Those members of group  116  with a bankcard utilization greater than 40 percent are included in group  120 . 
     Group  118  is further divided based on the number of bankcards currently reported as active in the credit history. A second node  122  defines a sub-population with a greater than 71 percent satisfactory trade ratio, less than 40 percent bankcard utilization, and no currently active bankcards. The group  124  includes the remaining members of the group  118 . The group  124  includes members of  118  with at least one currently active bankcard in their credit history. The group  124  is further divided based on the delinquency severity and recentness. 
     A third node  126  defines a sub-population with a greater than 71 percent trade ratio, less than 40 percent bankcard utilization, at least one currently active bankcard, and no severe or recent delinquencies. A group  128  includes the remainder of the group  124  which have one or more severe or recent delinquencies. 
     Group  128  is further divided based on the number of delinquencies for every 30 trades. A fourth node  130  defines a sub-population having a satisfactory trade ratio greater than 71 percent, less than 40 bankcard utilization, one or more currently active bankcards, at least one recent or severe delinquency, and one or less delinquency every 30 trades. A fifth node  132  defines a sub-population with a greater than 71 percent satisfactory trade ratio, less than 40 percent bankcard utilization, at least one currently active bankcard, one or more recent or severe delinquencies, and two or more delinquencies every 30 trades. 
     The group  120  includes the portion of the total population  112  that has a greater than 71 percent satisfactory trade ratio and a greater than 40 percent bankcard utilization. The group  120  is divided into two groups  134  and  136  based on the time span of the credit history. The group  134  includes those members of group  120  that have 192 or less months reported in their credit history. The group  136  includes members of the group  120  with 193 or more months reported in the credit history. 
     The group  136  is further divided based on the recentness and severity of delinquencies reported in the credit history. A sixth node  138  defines a sub-population with a greater than 71 percent satisfactory trade ratio, greater than 40 percent bankcard utilization, 193 or more months reported in the credit history, and a two or less severity index. A seventh node  140  defines a sub-population of the total population  112  with a greater than 71 percent satisfactory trade ratio, greater than 40 percent current utilization, 193 or more months reported in the credit history, and a severity index of more than two. 
     The group  134  is divided based on bankcard utilization. An eighth node  142  defines members of total population  112  with greater than 71 percent satisfactory trade ratio, greater than 40 percent bankcard utilization, 192 or less months reported in the history, and greater than 70 percent bankcard utilization. The group  144  includes those members of group  134  with less than 70 percent bankcard utilization. Therefore, group  144  is going to include credit histories with a bankcard utilization between 40 and 70 percent. 
     The group  144  is further divided based on the severity and recentness of delinquencies reported in the credit history. A ninth node defines a sub-population having a greater than 71 percent satisfactory trade ratio, greater than 40 percent bankcard utilization, less than 192 months reported, less than 70 percent bankcard utilization, and a severity index of more than three. A group  148  includes those members of the group  144  having a severity index of three or less. 
     The group  148  is divided further based on the timespan of the reported credit history. A tenth node  150  defines members of the total population  112  with a 71 percent or greater satisfactory trade ratio, a bankcard utilization between 40 and 70 percent, a severity index of three or less, and less than 120 months reported in the credit history. A group  152  includes the members of the group  148  with greater than 120 months reported in the credit history. The group  152  therefore includes members of the total population with a satisfactory trade ratio of greater than 71 percent, bankcard utilization between 40 and 70 percent, and a credit history greater than 120 months but 192 or less months reported. 
     The group  152  is split into an eleventh node  154  and a twelfth node  156 . The eleventh node  154  includes members of the group  152  with a bankcard utilization between 40 and 63 percent. The twelfth node  156  includes members of the group  152  with a bankcard utilization greater than 63 percent but less than 70 percent. 
     FIG. 6 illustrates a segmentation tree  214  according to a third embodiment of the invention. The segmentation tree  214  breaks down a total population  212  into seven segments. The segments are generated based on three pieces of credit history information. The segmentation tree  214  includes a first branch  216 , a second branch  218 , a third branch  220 , and a fourth branch  222 . 
     The first branch  216  is developed using the amount of detail in the credit history. The first branch  216  creates two groupings, one group creates a first segment  224  and a second group  226  continues for further segmentation. The first segment  224  defines a sub-population that has a thin credit file. A thin credit file encompasses credit histories of people that have not had a bank card or too short of a credit history. An applicant with a thin file generally refers to an applicant with fewer than three trades. The group  226  includes credit histories with thick credit files. Thick credit files means the history has data on three or more trades. 
     The second branch  218  further segments group  226 . The second branch  218  is developed using the delinquency reported in the credit history. The second branch  218  creates two groupings  228  and  230  which are segmented further by the segmentation tree  214 . The group  228  includes credit histories with no recent delinquencies and only minor delinquencies in the entire history. Minor delinquencies are where the account was not 60 or more days delinquent. Recent delinquencies are ones that occurred within the last six months. The group  230  includes credit histories with recent and/or severe delinquencies. Applicants with no delinquencies or very old minor delinquencies are generally refereed to as having a clean credit history. Those with moderate or severe and recent delinquencies are generally referred to as having a dirty credit history. The severity index used to determine whether a credit history is clean or dirty is taken as a combination of severity and recentness of the delinquency. In one embodiment, a clean credit history would have a severity index value smaller than four. 
     The third branch  220  further segments group  228  based on the rate of accumulating revolving debt. The third branch  220  is developed using the revolving balance acceleration reported in the credit history. The third branch  220  creates three groupings. One grouping defines a second segment  232 , one grouping defines a third segment  234 , and another grouping defines a fourth segment  236 . The second segment  232  defines a sub-population having credit histories with low revolving balance acceleration. Revolving balance acceleration is a dollar figure calculated by taking the total current revolving balance shown in a credit history and dividing that total by the number of months the credit history has been on file with the credit bureau. The low revolving balance acceleration for the second segment  232  is an acceleration of $35 or less. The third segment  234  defines a sub-population having credit histories with medium revolving balance acceleration. The medium acceleration of the third segment  234  is an acceleration of $36 to $135. The fourth segment  236  defines a sub-population having credit histories with high revolving balance acceleration. The high acceleration of the fourth segment  236  is an acceleration of greater than $135. 
     The fourth branch  222  further segments group  230 . The fourth branch  222  is developed using the revolving balance acceleration reported in the credit history. The third branch  222  creates three groupings. One grouping defines a fifth segment  238 , one grouping defines a sixth segment  240 , and another grouping defines a seventh segment  242 . The fifth segment  238  defines a sub-population having credit histories with low revolving balance acceleration. The low revolving balance acceleration for the fifth segment  238  is an acceleration of $40 or less. The sixth segment  240  defines a sub-population having credit histories with medium revolving balance acceleration. The medium acceleration of the sixth segment  240  is an acceleration of $40 to $110. The seventh segment  242  defines a sub-population having credit histories with high revolving balance acceleration. The high acceleration of the seventh segment  242  is an acceleration of greater than $110. 
     A separate scorecard is developed for each of the seven segments defined by the segmentation tree  214 . Each scorecard can be developed to accurately score similarly situated applicants in each of the seven defined sub-populations. As can be seen in FIG. 6, each of the defined sub-populations has different characteristics. In addition, the criteria indicating whether an acceleration is low, medium, or high varies from sub-population to sub-population as shown by segments  232 ,  234 , and  236  versus segments  238 ,  240 , and  242 . 
     A separate scorecard is developed for each of the seven segments defined by the segmentation tree  214 . Each scorecard can be developed to accurately score similarly situated applicants in each of the seven defined sub-populations. As can be seen in FIG. 7, each of the defined sub-populations has a different bad account percentage. In addition, the criteria indicating whether an account is likely to become bad varies from sub-population to sub-population. 
     In order to test the validity of the defined sub-populations, a representative sample of past applicants were rescored with the new methodology and compared with their actual credit history. The goal was to estimate the probability that an account would be 90 or more days past due in the first two years. Bad accounts were defined as accounts that were 90 or more days past due and the rest were termed good. FIG. 7 shows the breakdown of the percentage of the sample with the percentage of accounts for each of the seven segments developed using the segmentation  214 . 
     The total population  212  included 100 percent of the representative sample and was split into the first segment  224  and the group  226 . The first segment  224  included the portion of the sample that had thin credit files or no bankcards. This represented 27.6 percent of the total sample and the sub-population defined by the segment  224  had a 2.83 percent bad account rate. The rest of the total population  212  with thick credit files including bankcards were placed in the group  226 . 
     Those in group  226  were split next into the two groups  228  and  230 . This split was based on the severity and recentness of any delinquencies. Group  228  included those applicants in the sample with thick credit files and no or old delinquencies (clean) in their file history. The group  230  included portions of the sample with thick credit files and moderate or recent delinquencies (dirty). 
     The portion of the total population  212  that fit into group  228  were further split based on revolving balance acceleration. The second segment  232  defined a sub-population of the sample with thick, clean credit files where the revolving balance acceleration was $35 or less. The sub-population defined by the second segment  232  included 9.9 percent of the representative sample and had a 0.37 percent bad account rate. The third segment  234  defined a sub-population with thick, clean credit histories and a revolving balance acceleration of $36 to $135. The sub-population defined by the third segment  234  included 12.4 percent of the sample and had a 1.08 percent bad account rate. The fourth segment  236  defined a sub-population with thick, clean credit histories and a revolving balance acceleration of more than $135. The sub-population defined by the fourth segment  236  included 6.1 percent of the sample and had a 2.70 percent bad account rate. 
     The portion of the total population  212  that fit into group  230  were further split based on revolving balance acceleration. The fifth segment  238  defined a sub-population of the sample with thick, dirty credit files where the revolving balance acceleration was $40 or less. The sub-population defined by the fifth segment  238  included 22.9 percent of the representative sample and had a 1.34 percent bad account rate. The sixth segment  240  defined a sub-population with thick, dirty credit histories and a revolving balance acceleration of $41 to $110. The sub-population defined by the sixth segment  240  included 13.2 percent of the sample and had a 2.92 percent bad account rate. The seventh segment  242  defined a sub-population with thick, dirty credit histories and a revolving balance acceleration of more than $110. The sub-population defined by the seventh segment  242  included 9.4 percent of the sample and had a 3.54 percent bad account rate. 
     While we have illustrated and described preferred embodiments of my invention, it is understood that they are capable of modification and we therefore do not wish to be limited to the precise details set forth, but desire to avow myself of such changes and alterations as fall within the purview of the following claims.