Abstract:
A system and method is provided by which banks implement an automated process to identify and analyze payment related information, and, more particularly, to such a system and method requiring an identification of incoming payments correspondent banks route through competitors; the capture of information, including the correspondent bank the payment originated from, the competitor the payment was sent through, the account party (correspondent bank&#39;s customer), the payment beneficiary (customer), and payment ID information (reference numbers, date and amount) associated with such payments; the generation of an advisory message to the correspondent bank that payment can be made directly from their account (book transfer) and the generation of a report to correspondent banks identifying indirectly routed payments for the month.

Description:
BACKGROUND OF THE INVENTION 
       [0001]    1. Field of the Invention 
         [0002]    The present invention relates generally to a system and method by which banks implement an automated process to identify and analyze payment related information, and, more particularly, to such a system and method requiring an identification of incoming payments correspondent banks route through competitors; the capture of information, including the correspondent bank the payment originated from, the competitor the payment was sent through, the account party (correspondent bank&#39;s customer), the payment beneficiary (customer), and payment ID information (reference numbers, date and amount) associated with such payments; the generation of an advisory message to the correspondent bank that payment can be made directly from their account (book transfer) and the generation of a report to correspondent identifying indirectly routed payments for the month. 
         [0003]    2. Description of the Prior Art 
         [0004]    Electronic and online banking has long been known in the prior art, from the transfer of moneys between banks and other institutions to the ability of an individual to monitor and control his or her accounts via a secure Internet website. 
         [0005]    It should be appreciated that in today&#39;s world of electronic commerce, a bank&#39;s payments business forms an extensive “network” that extends well beyond its customers. Incoming and outdoing payment flows touch a bank&#39;s customers, it&#39;s customers doing business with other customers, correspondent banks directly and on behalf of its customers and their customers, its customers&#39; transactions to their customers with accounts at other banks, and even its customers&#39; transactions from their customers with accounts at other banks. The present invention recognizes that each of these payment flows gives rise to other opportunities to capture additional payment volumes through the data mining of existing payments information. 
         [0006]    There exists a significant opportunity for banks to increase the volume of high-value payments they receive, thereby generating additional revenues by creatively using information they already have available. The present invention is directed to a process whereby a user can leverage its correspondent bank network and corporate customer base to: (1) capture high-value payments that are now going to its competitors; (2) extend its business to its customers&#39; customers; (3) size its correspondent banks&#39; payments wallet; (4) allocate reciprocity based on correspondent banks wallet share; and (5) develop an “early warning system” to detect threshold shifts in payments business. 
         [0007]    As shall be appreciated, the prior art fails to specifically address either the problem or the solution arrived upon by applicant. 
       SUMMARY OF THE INVENTION 
       [0008]    It is a primary object of the present invention to provide a system and method by which banks implement an automated process to identify and analyze payment related information and act upon said information on a real-time or near real-time basis. 
         [0009]    It is another object of the present invention to provide such a system and method to identify potential new correspondent bank customers who have a significant volume of payment activity between that bank&#39;s customer and the user&#39;s customers. The pattern of activity will identify the top prospects based on volume of payments as well as trend in payment volumes and will also identify specific customer patterns that are driving existing volumes and trends in volume. 
         [0010]    It is another object of the present invention to provide such a system and method that provides the user with an in-depth understanding of the user&#39;s patterns of business and the opportunities they give rise to. 
         [0011]    It is still another object of the present invention to provide such a system and method that allows a user to capture high-value payments that are now going to the user&#39;s competitors. 
         [0012]    It is but another object of the present invention to provide such a system and method that allows a user to extend the user&#39;s business to the user&#39;s customers&#39; customers. 
         [0013]    It is yet still another object of the present invention to provide such a system and method that allows a user to size the user&#39;s correspondent banks&#39; payments wallet and allocate reciprocity based on correspondent bank wallet share. 
         [0014]    It is another object of the present invention to provide such a system and method that allows a user to develop an “early warning system” to detect threshold shifts in payments business. 
         [0015]    It is yet another object of the present invention to provide such a system and method that can significantly increase a user&#39;s high-value payments business by allowing the user to use the information the user currently has at the user&#39;s bank to work smarter. 
         [0016]    It is but another object of the present invention to provide such a system and method that can easily be customized to meet the user&#39;s needs. 
         [0017]    It is still another object of the present invention to provide such a system and method that is based on a readily available and existing framework, such as HP&#39;s Open Payments solution framework using technology such as HP Real Time Financial Services (RTFS) and HP OpenView BPI. 
         [0018]    It is another object of the present invention to provide such a system and method that estimates wallet size of correspondent bank&#39;s payments relative shares by competitors versus the user&#39;s. 
         [0019]    It is yet a further object of the present invention to provide such a system and method for identifying customer relationships at risk of significantly declining volumes or lost relationship. 
         [0020]    It is yet another object of the present invention to provide such a system and method that identifies how much business a user is giving it&#39;s correspondent bank in relation to the trend in business they are giving the user versus their competitors. 
         [0021]    It is a further object of the present invention to provide such a system and method that identifies how a correspondent bank, who has a relation with a user, is instead sending a payment to the user&#39;s customer through an intermediary bank who is the user&#39;s competitor. 
         [0022]    It is also an object of the present invention to provide such a system and method for estimating wallet size of customer payments relative shares by competitors versus a user. 
         [0023]    It is another object of the present invention to provide such a system and method that identifies customers who are transferring large amounts of funds to investment vehicles outside the bank and leaving a low overnight balance for investment opportunities to retain funds in the bank overnight. 
         [0024]    It is a further object of the present invention to provide such a system and method for identifying potential new customers who have a significant volume of payment activity between a user&#39;s customers within the Eurozone but not the home country of the bank, which pattern will identify the top prospects based on volume of payments and to show top Customer to Beneficiary pairs. 
         [0025]    It is also an object of the present invention to provide such a system and method that identifies potential new customers or existing customers who have a significant volume of payment activity in one currency but not in another. 
         [0026]    It is another object of the present invention to provide such a system and method that identifies potential new customers who have a significant volume of payment activity from existing customers so as to identify the top prospects based on volume of payments. 
         [0027]    It is still another object of the present invention to provide such a system and method that identifies patterns of conduct from a customer which may be suspicious when looking at inbound on-us transfers, outbound on-us transfers, inbound not on-us transfer and outbound not on-us transfers. 
         [0028]    It is yet another object of the present invention to provide such a system and method that allows a user to enlarge its business footprint in the euro-zone. 
         [0029]    It is another object of the present invention to provide such a system and method that allows conversion of corporate prospects to a user&#39;s customers to bring broad relationship benefits in addition to payments—including investment management, corporate finance, commercial lending, and investment banking. 
         [0030]    It is but another object of the present invention to provide such a system and method that will identify and prioritize opportunities and provide the actionable information to develop successful offers to convert candidate companies to becoming a user&#39;s customers. 
         [0031]    To the accomplishments of the foregoing objects and advantages, the present invention, in brief summary comprises a system and method by which banks implement an automated process to identify and analyze payment related information, and, more particularly, to such a system and method requiring an identification of incoming payments correspondent banks route through competitors; the capture of information, including the correspondent bank the payment originated from, the competitor the payment was sent through, the account party (correspondent bank&#39;s customer), the payment beneficiary (customer), and payment ID information (reference numbers, date and amount) associated with such payments; the generation of an advisory message to the correspondent bank that payment can be made directly from their account (book transfer) and the generation of a report to correspondent banks identifying indirectly routed payments for the month. 
     
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0032]      FIG. 1  is schematic diagram showing the indirect payment routing of the banking system and method of the present invention; 
           [0033]      FIG. 2  is an illustration of the Payment Opportunity Monitor of the banking system and method of the present invention; 
           [0034]      FIG. 3  is an illustration of the Correspondent Bank Overview “drill down” of the Payment Opportunity Monitor of the banking system and method of the present invention; 
           [0035]      FIG. 4  is an illustration of the Customer Pair Overview Screen of the Payment Opportunity Monitor of the banking system and method of the present invention; 
           [0036]      FIG. 5  is an illustration of the Alert Detail of the Payment Opportunity Monitor of the banking system and method of the present invention; 
           [0037]      FIG. 6  is schematic diagram showing the skipped payment that is the target of the banking system and method of the present invention; 
           [0038]      FIG. 7  is an illustration of the Skipped Payments Screen of the Payment Opportunity Monitor of the banking system and method of the present invention; 
           [0039]      FIG. 8  is an illustration of the Skipped Payments Alert of the Payment Opportunity Monitor of the banking system and method of the present invention; and 
           [0040]      FIG. 9  is an illustration of the Competitive Intelligence Screen of the Payment Opportunity Monitor of the banking system and method of the present invention; 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       [0041]    Referring to the drawings and, in particular, to  FIG. 1  thereof, the open payments target marketing system of the present is provided and is referred to generally by reference numeral  10 . The system  10  comprises a solution framework that improves a bank&#39;s ability to perform enterprise-wide and real-time functions. In particular, it uses technology such as HP&#39;s Real Time Financial Services (RTFS) or HP partners to create this capability. Another key component of the Open Payments Target Marketing System is the HP OpenView Business Process Intelligence (OpenView BPI) application. This technology captures real-time and near-real-time information from your payments systems and back-end systems. 
         [0042]    Briefly, HP OpenView BPI technology consists of “artificial probes” that can be placed in any system to capture information, without the requirement to make changes to existing systems. HP OpenView BPI does not impact system performance and captures information in real time, which features are critical for the successful use and operation of the present invention. HP OpenView BPI incorporates visual display tools, such as gauges, and an analytics engine. Data can reside in a data store, which can refresh information to a historical database. OpenView BPI works with industry-standard database systems. 
         [0043]    “Indirect payments” are incoming payments that a user&#39;s cobanks  100  route through its competitors  102 , which then send the payments to the user  104  for credit to the user&#39;s customers  106 . The user&#39;s correspondent banks  100 , and their customers, will gain significant benefits by sending these payments directly to the user&#39;s correspondent bank. Referring to  FIG. 1 , an indirect payment routing is shown, which routing illustrates the goals of the open payments target marketing system: to eliminate the intermediary correspondent bank (the user&#39;s competitor)  102  and have the user&#39;s correspondent bank  100  send the payment directly to the user  104 . 
         [0044]    The benefits to the user&#39;s correspondent banks of eliminating this intermediary step include:
       Lower charges for a single transaction as a book transfer   Reduced errors and a higher straight through processing (STP) rate, which results from eliminating unnecessary intermediary bank  110   s  and payment systems   Reduced complexity for inquiries and investigations   Faster posting of funds to the beneficiaries  108 ′ accounts   Extended cut-off times for book transfers   Enhanced liquidity management       
 
         [0051]    Referring now to screen shown in  FIG. 2 , the Payment Opportunity Monitor  200  of the present invention is illustrated. The Payment Opportunity Monitor  200  provides an overview of indirect payments received. The gauges  202  at the top of the figure provide an indication of the size of the opportunity for capturing indirect payments. Once the Open Payments Target Marketing System  10  is initiated, these gauges  202  are used to monitor the success of your program to convert these indirect payments to book transfers. 
         [0052]    The top-left gauge  202  is the Indirect Routed Payments Gauge  204  which shows the number of indirect payments received in real time. This is the size of the opportunity for converting indirect payments to book transfers. It should be appreciated that additional gauges  202  or metrics may easily be developed to track success rate over time against program goals. The center gauge at the top of  FIG. 2  is the Percentage Indirect Gauge  206 , which shows indirect payments as a percentage of total payments. This gauge  202  presents the size of the opportunity relative to total payment volume. Finally, the gauge  202  on the top right of the Monitor  200  is the Top Ten as Percentage of Total Gauge  208 , which shows the concentration of indirect payments among correspondent banks  100 . This gauge  202  shows how focused the opportunity is. This screen will identify major opportunities and help target business development activities. 
         [0053]    The three columns below the gauges  202  show correspondent banks  100  ranging from those representing the highest to the lowest opportunity. For example, First Bank accounts for 2,654 of the 40,344 indirect payments received so far. Alerts Sent  210 , shown in the right column of the Monitor  200 , indicates how many alerts have been sent to the correspondent banks  100  to notify them that these payments should be directly routed to the user&#39;s bank  104 . In  FIG. 2 , the reason the number of alerts is low relative to the number of indirect payments is largely due to the need for only one notification for a “customer pair”  212  payment that represents a pattern of payments on behalf of one of its customers  100  to one of the user&#39;s customers  106 . Therefore, the number of alerts will be relatively lower if the customer pair  212  payment concentration is high. 
         [0054]      FIG. 3  shows the Correspondent Bank Overview “drill down”  214  which illustrates in more detail the indirect payments being initiated by one of the correspondent banks  100 , in this example, First Bank. As can be seen, the same gauges that appear in the Payment Opportunity Monitor  200  of  FIG. 2  continue to provide a real-time overview of the indirect payment situation. In this “drill down”  214 , the columns below the gauges  202  show the customer pairs  212  involved in the indirect payments from First Bank. Each of First Bank&#39;s customers, which are making payments to the user&#39;s customers  106 , are shown in the first column. The second column shows each of the beneficiaries  108  that are the user&#39;s customers  106 . 
         [0055]    As the second row in the list shows, First Bank&#39;s customer, Flowers of Central America, has sent 426 payments to Property Management, Inc. This information can be used to work the opportunity from two sides—through First Bank and through Property Management—to keep these payments within the user&#39;s bank  104 . It should be appreciated that the top eight customer pairs  212  provide the bulk of the major opportunities—and this screen will help to focus efforts on these top opportunities. 
         [0056]    Illustrated in  FIG. 4  is the Customer Pair Overview screen  216  which illustrates the detail of each of First Bank&#39;s transactions that are going from Flowers of Central America to Property Management, a user&#39;s customer  106 . The fourth column in the Figure shows the intermediary bank  110  (the user&#39;s competitor  102 )—which the user wishes to eliminate from the payment process so that the user can capture these transactions as book transfers. The gauges  202  at the bottom of  FIG. 4  show the concentration of First Bank&#39;s indirect payments for the top intermediary bank  110   s . These are the banks from which the user desires to obtain their business. In this example, R&amp;H Bank has the top concentration of all indirect payments at 30 percent. 
         [0057]      FIG. 5  is an illustration of the Alert Detail  218  which the user&#39;s bank  104  would send to First Bank—advising it of the opportunity to directly route the payment from its customer, Flowers of Central America, directly to user&#39;s customer  106 , Property Management. The system  10  tracks alerts  218  against progress made in getting these indirect payments converted to the user&#39;s book transfers in line with the user&#39;s program goals. This data can be automatically transferred to any managing information (MIS) application, such as Microsoft® Excel, to track performance indicators. 
         [0058]      FIG. 6  is a schematic of the Open Payments Target Marketing System  10  of the present invention illustrating the ultimate objective of eliminating a user&#39;s customer&#39;s  106  payments to competitors  102  by keeping customer-to-customer payments on the user&#39;s books. Such payments, referred to as skipped payments, are payments made by the user&#39;s customers  106  to the user&#39;s customers  106 —but through another bank  102 . In other words, some of the user&#39;s customers  106  have accounts with competitor banks  102  and payments from the customers  106  are going to these competitor accounts  102  instead of being made on the user&#39;s books. 
         [0059]    In some cases, the user&#39;s customers  106  may not know that their customers—the beneficiaries  108  of the payment—have an account with the user&#39;s bank  104 . In other cases, when the user&#39;s customer  106  is the account party, they may be using routing instructions that were issued before their beneficiary  108  opened an account with the user&#39;s bank  104 . These transactions all represent potential revenue leakage and business directed through the user&#39;s competitor  102  that could be kept in house. 
         [0060]    There are compelling advantages for the user&#39;s customers  106  to send payments to their customers  106  directly to their accounts with the user&#39;s bank  104 . These advantages include:
       Lower charges for a single transaction and a book transfer   Reduced errors and a higher STP rate, resulting from eliminating unnecessary intermediary bank  110   s  and payment systems   Reduced complexity for inquiries and investigations   Faster posting of funds to the beneficiaries  108  account   Extended cut-off times for book transfers   Enhanced liquidity management       
 
         [0067]      FIG. 7  illustrates the Skipped Payments screen which shows the user&#39;s customer, US Alloy, and all of the beneficiaries  108  that are the user&#39;s customers  106  that US Alloy directs payments to. The top of the figure shows the concentration of US Alloy&#39;s skipped payments by intermediary bank  110 . In this example, US Alloy sent 75 payments to the user&#39;s customer  106 , Flowers of Central America. All of these payments were made to Flowers of Central America&#39;s account with TCO Bank. Therefore, there is an opportunity to convert all 75 of these payments to book transfers. Through the alert process in the Open Payments Target Marketing System  10 , the user  104  can get US Alloy to agree that all of its payments to Flowers of Central America will occur via book transfer. In this case, the user  104  can use the user&#39;s auto-repair capability to change US Alloy&#39;s payments to book transfers. Flowers of Central America appears in green type in the Figure, indicating that the user  104  has already obtained US Alloy&#39;s permission to work with Flowers of Central America to get its payment instructions changed. 
         [0068]    The right-most column in  FIG. 7  shows the number of days since the user&#39;s alert  218  was sent for each of the skipped payment patterns; The company names on the left-most column in green type indicate that US Alloy has authorized the user  104  to contact its beneficiary  108  and that arrangements have been made to convert these external payments to book transfers. Red type in the figure indicates that there has not been a response to the alert  218 ; and black type indicates US Alloy does not want its existing payment arrangements changed. 
         [0069]      FIG. 8  shows the Skipped Payment screen  220  of the Open Payment Target Marketing System  10 . This alert  218  requests US Alloy&#39;s permission to contact Flowers of Central America to arrange for direct credit of US Alloy&#39;s payments to Flowers of Central America&#39;s account with the user&#39;s bank  104 . At the same time, the user&#39;s  104  Open Payments Target Marketing System  10  would identify the other skipped payment opportunities that US Alloy is making to all of the user&#39;s other customers  106 . This intelligence would provide an excellent opportunity to discuss crediting these payments directly as well. 
         [0070]    The user  104  can use information from the user&#39;s customer base to identify highly qualified new payment customers using the instant system  10 . If the user&#39;s customers  106  are sending a large volume of payments to companies at the user&#39;s competitor banks  102 , these companies are ideal prospects for discussing the advantages of opening an account with the user&#39;s bank  104 . 
         [0071]      FIG. 9  shows the Competitive Intelligence screen  222  of the Payment Monitor  200 , showing the user&#39;s customer, Junpo Holding, and a list of the companies it is making payments to—with the recipient of the largest payments appearing at the top of the list. Some of these beneficiaries  108  are customers of the bank  104 , while others are not. The gauges  202  at the top of  FIG. 9  show the number of highly qualified payments prospects, based on the strong concentration of payment flows from Junpo Holding. The middle gauge  202  shows the number of potential book transfers that can occur if Junpo Holding&#39;s payments to bank customers  106  can be kept within the user&#39;s bank  104 . The gauge on the right of the figure shows the percentage of Junpo Holding&#39;s payments that are skipped payments—those that are being sent outside the user&#39;s bank  104  to companies that have accounts with the user&#39;s bank  104 . 
         [0072]    In this example, Human Geno Systems is the user&#39;s customer  106  and received  122  payments from Junpo Holding. It also received payments from five other customers  106  of the user&#39;s bank  104 . However, all of the payments from Junpo Holding were sent through other banks  102 . Using the Open Payments Target Marketing System  10  would enable the user  104  to identify the other five customers Human Geno receives payments from and work with Human Geno Systems to get these customer payments to go to directly to Human Geno System&#39;s accounts with the user  104 . 
         [0073]    As shown in the figure, Montgomery Shippers is not a customer of the bank  104 , but it received  195  payments from Junpo Holding, as well as payments from another 60 of the user&#39;s customers  106 . It is an excellent candidate to discuss an account relationship with, and the data provided from the Open Payments Target Marketing System  10  will aid the user&#39;s sales people in obtaining this business. 
         [0074]    Having thus described the invention with particular reference to the preferred forms thereof, it will be obvious that various changes and modifications can be made therein without departing from the spirit and scope of the present invention as defined by the appended claims. For example, the system can be used to provide value in other areas such as securities, foreign exchange, liquidity management, and compliance. 
         [0075]    The methodology of the instant invention can be practiced by a user to analyze hundreds of thousands of payment transactions, which in one embodiment could involve transactions between a user&#39;s customers  106  in their home country and their business counter-parties located in the euro-zone outside the home country. In such example, the universe for these payment transactions will encompass MT103+ records from the “data extraction start date” back to an earlier date which produces a total of approximately 1 million transactions, which number of records would produce statistically significant results. Such payment transactions would include book transfers, transactions which are initiated or received by correspondent banks  100 , as well as transactions that involve user-owned bank intermediaries. 
         [0076]    The diagram below illustrates the flow of transactions an sample project would capture—in this example where Regents Bank UK is the user: 

 
         [0077]    As the diagram above illustrates, the pilot will capture payments for which the:
       by order of party (account party) is a company in the euro-zone outside of Regents home country (in this case the UK) that is making a payment to a Regents Bank customer  106  based in the UK—whether through a correspondent  100  or a Regents Bank entity, including “on us” transfers; and   beneficiary of the payment from Regents Bank customer  106 , based in the home country, is in the euro-zone outside of the home country—whether through a correspondent  100  or a Regents Bank entity, including “on us” transfers.       
 
         [0080]    The data captured using this particular embodiment of the open payments target marketing system  10  of the present invention includes the account party  106 , beneficiary  108 , paying (by order of) bank(s) or receiving (beneficiary) bank, account information, date, amount, payment mechanism (clearing system, “on us” transfers) and reference number. The analysis of this information will identify the concentration of business opportunities between prospects outside of the home country and Regents Bank customers  106 . 
         [0081]    For example one high priority prospect in France may be doing business with 500 Regents Bank customers  106 —200 of which make payments to the French prospect, 200 of which receive payments from the French prospect and 100 of which both receive and make payments to the French prospect. The analysis will sum up these payments to provide sizing of the opportunity associated with converting this prospect to a Regents Bank customer. 
         [0082]    The system and method of the present invention  10  will develop and provide reports for the top 200 prospects that contain the detailed and actionable information Regents Bank requires to develop a comprehensive assessment and sales proposal for these prospects. This report would show the relationship between the prospect and Regents Bank&#39;s customers, including the concentration of flows between existing customers and the prospect. In addition to directing the sales efforts to the prospect companies, this information will identify opportunities to leverage well established, and strong, UK customers of Regents Bank  106  who can provide referrals to enhance the chances for a successful relationship acquisition. The top prospect reports will also analyze the impact of converting each prospect to a Regents Bank customer  106  on correspondent bank  100  relations. This analysis will identify the number of payments that will be shifted from each of these correspondent banks to payments made directly through Regents Bank  104 . 
         [0083]    This process is illustrated in the following diagram: 

 
         [0084]    The analysis would identify the correspondent banks  100  who will lose these payments when the prospect is converted to a Regents Bank client. The analysis will also identify the payments from prospects outside of the UK that are channeled through Regents Bank entities, including book transfers. This analysis will indicate whether Regents Bank already has some market share of these prospects payments business. While they will not be a “new” customer—there may be a substantial opportunity to obtain a larger share of their payments volume. Furthermore, the analysis may identify skipped payments, the situation that arises when a Regents Bank customer makes a payment to a bank in France (for example) when the beneficiary has an account with Regents Bank. 
         [0085]    The chart below illustrates the approach to opportunity analysis of the system  10  of the present invention: 

 
         [0086]    There are two dimensions to the opportunity analysis—the overall size of the opportunity and the concentration of the opportunity. Realization of the opportunity will be easier if there is a high concentration of payments with a small number of prospects. This will allow Regents Bank to concentrate efforts on converting a couple of hundred prospects and obtain a significant percentage of the overall potential benefit. The opportunity analysis may include a “what-if” approach to sizing the opportunity. For example—if 300 prospects account for 70% of the opportunity, what is the impact of converting various percentages of these prospects to customers. 
         [0087]    It is likely that conversion of prospects to clients may exceed the number of payments identified in the analysis since the analysis only shows the number of payments to and from Regents Bank customers. There may be significantly greater payment volumes to other UK customers. There is also the opportunity to get a share of these prospects&#39; payment flows to and from non-UK customers. Regents Bank will have experience with the impact of client acquisitions that will provide an adjustment factor we can incorporate in the analysis. As the diagram shows, there is also the opportunity to enhance this analysis through the merging of Regents Bank&#39;s CIF and company hierarchy files with this payment information. 
         [0088]    This analysis might indicate that some of these prospects are subsidiaries of Regents Banks clients, in which case existing client relationships can be directly leveraged to gain their subsidiaries&#39; business. CIF information could be associated with Regents Bank&#39;s CRM system to determine whether there have been, or are, sales efforts underway with these prospects. The CIF hierarchy file will also provide insight into an associated group of companies has a high concentration of the payment flows to and from the prospect. 
         [0089]    In this embodiment, the system  10  can provide the following deliverables to a customer:
       Identification, and business opportunity concentration, of prospect companies obtained from analysis of payment records   Reports, containing detailed/actionable, information for the top prospects   Reports analyzing the implications of prospect business acquisition on the user bank&#39;s correspondent banking relationships.   “What-if” analysis to size the market opportunity at various levels of conversion rates, including the corresponding ROI that will be achieved through the implementation of a production system.   Analysis of “split business” between user bank&#39;s  104  entities and correspondent banks  100 .   Analysis of aggregated payment flows at immediate and ultimate level parent level for subsidiaries of the user&#39;s customers  106 .   Based on the availability of CIF, and customer hierarchy information—
           Concentration analysis between prospects and company groups (i.e. consolidated at the ultimate parent level)   Identification of prospects that are subsidiaries (or affiliates) of existing user customers  106     Feasibility assessment of linking the CRM system to priority prospects   
               
 
         [0100]    It should be appreciated that the Open Payments Target Marketing System of the present invention has a variety of additional applications. For example, it may be used to identify potential new correspondent bank customers  100  who have a significant volume of payment activity between that bank&#39;s customer and the user&#39;s customers  106 . This pattern will identify the top prospects based on volume of payments as well as trend in payment volumes. It will also identify specific customer patterns that are driving existing volumes and trends in volume. 
         [0101]    The instant system may also be used to estimate the wallet size of a correspondent bank&#39;s  100  payments relative shares by competitors  102  versus the user  104  and to identify customer relationships at risk of significantly declining volumes or lost relationship. Other potential applications include identifying how much business a user is giving to its correspondent bank in relation to the trend in business they are giving the user versus the user&#39;s competitors and identifying how a correspondent bank who has a relation with a user is instead sending a payment to a user&#39;s customer through an intermediary bank who is the user&#39;s competitor. 
         [0102]    Additionally, the system  10  can be used to estimate wallet size of a customer payments relative shares by competitors versus the user&#39;s, to identify customers who are transferring large amounts of funds to investment vehicles outside the bank and leaving a low overnight balance, to identify potential new customers who have a significant volume of payment activity between a user&#39;s customers within the Eurozone but not the home country of the bank, and to identify potential new customers who have a significant volume of payment activity from a user&#39;s customers within the Eurozone but not the home country of the bank. 
         [0103]    Other examples of the system&#39;s  10  broad applications include identifying potential new customers or expanding a user&#39;s customer relationship to customers who have a significant volume of payment activity in one currency but not in another, identifying potential new customers who have a significant volume of payment activity from a user&#39;s customers, and identifying patterns of conduct from a customer which may be suspicious when looking at inbound on-us transfers, outbound on-us transfers, inbound not on-us transfer and outbound not on-us transfers. The patterns, analytics and actions associated with these applications is described herewith: 

       Identify Sending Institutions who are not our customers but would be good candidates to be our customer.       
 
       Pattern: 
     (Based on MT103 fields and the customer information file (CIF)) 
     1. Is the sending institution not a customer? 
       [0000]    
       
         
           
             Is field  51 A not a match to a customer name in our CIF? 
             If Yes then Go to Step 2. 
             If No. Go to next record. 
           
         
       
     
       This step is hereafter called Correspondent_Not_In_CIF 
     2. Save record to the Correspondent_Banking_New_Business File 
       [0000]    
       
         
           
             Fields 
               32 A—Value Date and Amount 
               50 A—Ordering Customer 
               51 A—Sending Institution 
               52 A—Ordering Institution 
               53 A—Senders Correspondent 
               56 A—Intermediary Institution 
               59 A—Beneficiary  108  Customer 
           
         
       
     
       Analytics: 
       [0116]    Ranking Order for Highest Potential New Customer based on Volume of Payments. Will aggregate the number of payments by Sending Institution and show historical trends. 
       Action: 
       [0000]    
       
         
           
             Sort Correspondent_Banking_New_Business File by  51 A 
             Tally Data
           If value date minus the current date is less than 30 days, count the number of payments and store in 30_Day_Data_Count field.   If value date minus the current date is less than 90 days, count the number of payments and store in 90_Day_Data_Count field.   If value date minus the current date is less than 180 days, count the number of payments and store in 180_Day_Data_Count field.   If value date minus the current date is less than 365 days, count the number of payments and store in 365_Day_Data_Count field.   
         
             (Here after called Tally_Data_Date — 30 — 90 — 180 — 365) 
             Sum 180_Day_Data_Count field for all  51 A and store in a 180_Day_Total_Payments field. 
             Divide each unique  51 A 180_Day_Data_Count field by 180_Day_Total_Payments to derive percent of total by each sending institution and store in 180_Day_Percent field. 
             Sort row by Descending based on 180_Day_Count field. 
             For each row, compute Cumulative_Percent by adding 180_Day_Percent field to the 180_Day_Percent field on the row above and place in Cumulative_Percent filed. 
             For each row, take the 30_Day_Data_Count field, divide by the Number_Business_Days_Last — 30_Days (Taken from the Investigation System) and store in field Average_Last — 30_Days. 
             For each row, take the 90_Day Data_Count field, divide by the Number_Business_Days_Last — 90_Days (Taken from the Investigation System) and store in field Average_Last — 90_Days. 
             For each row, take the 180_Day_Data_Count field, divide by the Number_Business_Days_Last — 180_Days (Taken from the Investigation System) and store in field Average_Last 180_Days. 
             For each row, take the 365_Day_Data_Count field, divide by the Number_Business_Days_Last — 365_Days (Taken from the Investigation System) and store in field Average_Last — 365_Days. 
           
         
       
     
         [0000]    
       
         
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
             
           
               
                   
               
               
                   
                 Total Volume 
                   
                   
                   
                   
                   
                   
               
               
                 Ordering 
                 Last 180 
                 180 Day % 
                 Cumulative 
                 Average Last 
                 Average Last 
                 Average Last 
                 Average Last 
               
               
                 Institution 
                 Days 
                 of Total 
                 % of Total 
                 30 Days 
                 90 Days 
                 180 Days 
                 365 Days 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 Bank A 
                 100,000 
                 62% 
                 62% 
                 506 
                 505 
                 556 
                 580 
               
               
                 Bank B 
                 39,000 
                 24% 
                 86% 
                 211 
                 225 
                 217 
                 200 
               
               
                 Bank C 
                 21,000 
                 13% 
                 99% 
                 140 
                 120 
                 117 
                 115 
               
               
                 Bank D 
                 2,300 
                  1% 
                 100%  
                 9 
                 14 
                 13 
                 10 
               
               
                 Total 
                 162,300 
               
               
                   
               
             
          
         
       
       
      
       
         
           
             How big is the wallet of our correspondent and what percentage of their business does it appear that we are getting. 
           
         
       
     
       Pattern: 
     (Based on MT103 fields and the customer information file) 
     1. Is the sending institution an existing customer? 
       [0000]    
       
         
           
             Is field  51 A=customer in our CIF? 
             If Yes Go to Step 2. 
             If No. Go to next record. 
           
         
       
     
       This step is hereafter called Correspondent in_CIF 
     2. Is the Sender&#39;s Correspondent one of our competitors (from specified list supplied by bank)? 
       [0000]    
       
         
           
             Is field  53 A is on the Competitor_List? 
             if yes include go to step 3. 
             if no, move to next record 
           
         
       
     
       3. Save record to Correspondent_Banking_Wallet_Sizing file 
       [0000]    
       
         
           
             Fields 
               32 A—Value Date and Amount 
               50 A—Ordering Customer 
               51 A—Sending Institution 
               52 A—Ordering Institution 
               53 A—Senders Correspondent 
               56 A—Intermediary Institution 
               59 A—Beneficiary  108  Customer 
           
         
       
     
       Analytics: 
     Identify the commercial payment volume percentage for each competitor who sends payments to us on behalf a correspondent bank who is our customer. 
     Action: 
       [0000]    
       
         
           
             Get our bank&#39;s market share from Clearing and Settlement Entity (e.g., fedwire, TCH, EBA, ECB, LVTS) and store in Market_Share field. 
             Take records in the Correspondent_Banking_Wallet_Sizing file and sort by  51 A (Sending_Institution) 
             Tally_Data_Date — 30 — 90 — 180 — 365 
             Use 30_Day_Data_Count for each Sending_Institution and array by institution in the Competitor_List to form the Sending_Insitution_By_Competitor — 30_Day field. 
             Populate Competitor_Volume — 30_Day field by dividing the Sending_Insitution_By_Competitor — 30_Day field by the Market_Share field for each competitor. 
             For each Sending_Insitution list our Debit_Volume for that institution over the past 30 days (taken from the bank&#39;s wire transfer system) and populate the Debit_Volume_By_Sending_Institution — 30_Day field. 
             For each Sending_Insitution sum the Competitor_Volume — 30_Day for each competitor and Debit_Volume_By_Sending_Institution — 30_Day and place in a Total_Volume — 30_Day field. 
             For each Sending_Insitution compute Competitor_Market_Share by taking Competitor_Volume — 30_Day and dividing it by the Total_Volume — 30_Day and populate in the Competitor_Market_Share — 30_Day field. 
           
         
       
     
         [0000]                                                                                              For last 30 days   Competitor 1   Competitor 2   Competitor 3   Competitor 4                Sending       %       %       %       %   Debit Volume of           Institution   volume   share   volume   share   volume   share   volume   share   our MT103   Total               Bank A   25,000   11%   35,000   15%   10,000   4%   60,000   26%   100,000   230,000       Bank B       Bank C       Bank D       Market Share   20%                    
3. Correspondent Banking: Threshold-Driven “Business at Risk” Warning System 
       Identify significantly decreased volume to our bank from a specific sending institution while our correspondent banks are not seeing similar reductions in volume.         
       Pattern: 
     (Based on MT103 fields and the customer information file) 
     1. Is the sending institution an existing customer? 
       [0000]    
       
         
           
             Correspondent_In_CIF? 
             If Yes Go to Step 2. 
             If No. Go to next record. 
           
         
       
     
       2. Is the Sender&#39;s Correspondent one of our competitors (from specified list supplied by bank)? 
       [0000]    
       
         
           
             Is field  53 A is on the Competitor_List? 
             if yes include go to step 3. 
             if no, move to next record 
           
         
       
     
       3. Save record in Business_At_Risk file 
       [0000]    
       
         
           
             Fields 
               32 A—Value Date and Amount 
               50 A—Ordering Customer 
               51 A—Sending Institution 
               52 A—Ordering Institution 
               53 A—Senders Correspondent 
               56 A—Intermediary Institution 
               59 A—Beneficiary  108  Customer 
           
         
       
     
       Analytics: 
     Identify what percentage each of our competitors has of our correspondent bank&#39;s commercial payment volume and who is trending up or down. 
     Action: 
       [0000]    
       
         
           
             Take records in the Business_At_Risk file and sort by  51 A (Sending_Institution). 
             Tally Data 30, 90, 180 days
           If value date minus the current date is less than 30 days, count the number of payments and store in 30_Day_Data_Count field.   If value date minus the current date is less than 90 days, count the number of payments and store in 90_Day_Data_Count field.   If value date minus the current date is less than 180 days but greater than 90 days count the number of payments and store in 90 — 180_Day_Data_Count field.   (Here after called Tally_Data_Date — 30 — 90 — 90 — 180)   
         
             Use 30_Day_Data_Count for each Sending_Institution and array by institution in the Competitor_List to form the Sending_Insitution_By_Competitor — 30_Day field. 
             Use 90_Day_Data_Count for each Sending_Institution and array by institution in the Competitor_List to form the Sending_Insitution_By_Competitor — 90_Day field. 
             Use 180_Day_Data_Count for each Sending_Institution and array by institution in the Competitor_List to form the Sending_Insitution_By_Competitor — 180_Day field. 
             For each Sending_Insitution list our Debit_Volume for that institution over the past 30 days (taken from the bank&#39;s wire transfer system) and populate the Debit_Volume_By_Sending_Institution — 30_Day field. 
             For each Sending_Insitution list our Debit_Volume for that institution over the past 90 days (taken from the bank&#39;s wire transfer system) and populate the Debit_Volume_By_Sending_Institution — 90_Day field. 
             For each Sending Insitution list our Debit_Volume for that institution over the past 91-180 days (taken from the bank&#39;s wire transfer system) and populate the Debit_Volume_By_Sending_Institution — 180_Day field. 
             For each Sending_Insitution sum the Competitor_Volume — 30_Day for each competitor and Debit_Volume_By_Sending_Institution — 30_Day and place in a Total_Volume —30 _Day field. 
             For each Sending_Insitution sum the Competitor_Volume — 90_Day for each competitor and Debit_Volume_By_Sending_Institution — 90_Day and place in a Total_Volume 90_Day field. 
             For each Sending_Insitution sum the Competitor_Volume — 180_Day for each competitor and Debit_Volume_By_Sending_Institution — 180_Day and place in a Total_Volume 180_Day field. 
             For each Sending_Insitution compute Competitor_Market_Share by taking Competitor_Volume — 30_Day and dividing it by the Total_Volume — 30_Day and populate in the Competitor_Market_Share — 30_Day field. 
             For each Sending_Insitution compute Competitor_Market_Share by taking Competitor_Volume — 90_Day and dividing it by the Total_Volume — 90_Day and populate in the Competitor_Market_Share — 90_Day field. 
             For each Sending_Insitution compute Competitor_Market_Share by taking Competitor_Volume — 180_Day and dividing it by the Total_Volume — 180_Day and populate in the Competitor_Market_Share — 180_Day field. 
             For each Sending_Insitution compute Our_Market_Share by taking Debit_Volume_By_Sending_Institution — 30_Day and dividing it by the Total_Volume — 30_Day and populate in the Our_Market_Share — 30_Day field. 
             For each Sending_Insitution compute Our_Market_Share by taking Debit_Volume_By_Sending_Institution — 90_Day and dividing it by the Total_Volume — 90_Day and populate in the Our_Market_Share — 90_Day field. 
             For each Sending_Insitution compute Our_Market_Share by taking Debit_Volume_By_Sending_Institution — 180_Day and dividing it by the Total_Volume — 180_Day and populate in the Our_Market_Share — 180_Day field. 
             Highlight changes in Competitor_Market_Share — 30_Day and Competitor_Market_Share — 90_Day where Competitor_Market_Share — 30_Day minus Competitor_Market_Share — 90_Day is greater than Threshold_Amount. (Manual input). 
             Highlight changes in Competitor_Market_Share — 90_Day and Competitor_Market_Share — 180_Day where Competitor_Market_Share — 90_Day minus Competitor_Market_Share — 180_Day is greater than Threshold_Amount. (Manual input). 
             Highlight changes in Our_Market_Share — 30_Day and Our_Market_Share — 90_Day where Our_Market_Share — 90_Day minus Our_Market_Share — 30_Day is greater than Threshold_Amount. (Manual input). 
             Highlight changes in Our_Market_Share — 90_Day and Our_Market_Share — 180_Day where Our_Market_Share — 180_Day minus Our_Market_Share — 90_Day is greater than Threshold_Amount. (Manual input). 
           
         
       
     
         [0000]                                                    For last 30 days                               Ordering   Competitor 1   Competitor 2   Competitor 3   Competitor 4   Debit Volume of       Institution   volume   volume   volume   volume   our MT103   Total                   Bank A 30 Days   3,000    5,000   2,000    9,000   10,000   29,000       Bank A last 90 Day   8,000   14,000   6,500   26,000   32,000   86,500       Bank A 90–180   8,200   13,000   6,300   12,000   45,000   84,500       Bank A 30 Days   10%   17%   7%   31%   34%       Bank A last 90 Day    9%   16%   8%   30%   37%       Bank A 90–180   10%   15%   7%   14%   53%                    
4. Correspondent Banking: Reciprocity Analyzer 
       Ensure that our level of business stays reciprocal with our correspondents level of business with us.         
       Pattern: 
     (Based on MT103 fields and the customer information file) 
     1. Is the sending institution an existing customer? 
       [0000]    
       
         
           
             Correspondent_In_CIF?
           If Yes Go to Step 2.   If No. Go to next record.   
         
           
         
       
     
       2. Is the Sending Institution in our Nostro_Account database? 
       [0000]    
       
         
           
             Is field  51 A=Nostro_Account_Bank (take from corporate list of Nostro Banks)
           If no move to next record   If yes move to step 3   
         
           
         
       
     
       3. Is the Sending Institution from the country we are analyzing? 
       [0000]    
       
         
           
             Input Search_County_Code 
             Look at  51 A for BIC 
             Cross ref to CIF for BIC to Country_Code 
             Does Country_Code match Search_Country_Code
           If no move to next record   If yes include send file to Reciprocity_Analyzer file   
         
           
         
       
     
       4. Save record to Reciprocity_Analyzer Database 
       [0000]    
       
         
           
             Fields 
               32 A—Value Date and Amount 
               50 A—Ordering Customer 
               51 A—Sending Institution 
               52 A—Ordering Institution 
               53 A—Senders Correspondent 
               56 A—Intermediary Institution 
               59 A—Beneficiary  108  Customer 
           
         
       
     
       Analytics: 
     Identify trends where the correspondent is sending a Payment Message through a correspondent. 
     Action: 
       [0000]    
       
         
           
             Take records in the Reciprocity_Analyzer file and sort by  51 A (Sending_Institution). 
             Tally_Data_Date — 30 — 90 — 90 — 180 and array. 
             For each row, take the 30_Day_Data_Count field, divide by the Number_Business_Days_Last — 30_Days (Taken from the Investigation System) and store in field Average_Last — 30_Days. 
             For each row, take the 90_Day_Data_Count field, divide by the Number_Business_Days_Last 90_Days (Taken from the Investigation System) and store in field Average_Last — 90_Days. 
             For each row, take the 90 — 180 Day_Data_Count field, divide by the Number_Business_Days_Last — 180_Days minus Number_Business_Days_Last — 90_Days (Taken from the Investigation System) and store in field Average_Last — 90 — 180_Days. 
             For each Nostro_Account_Bank list our Debit_Volume for that institution over the past 30 days (taken from the bank&#39;s wire transfer system) and populate the Debit_Volume_By_Sending_Institution — 30_Day field. 
             For each Nostro_Account_Bank list our Debit_Volume for that institution over the past 90 days (taken from the bank&#39;s wire transfer system) and populate the Debit_Volume_By_Sending_Institution — 90_Day field. 
             For each Nostro_Account_Bank list our Debit_Volume for that institution over the past 91-180 days (taken from the bank&#39;s wire transfer system) and populate the Debit_Volume_By_Sending_Institution — 180_Day field. 
             For each row, take the Debit_Volume_By_Sending_Institution — 30_Day, divide by the Number_Business_Days_Last — 30_Days (Taken from the Investigation System) and store in field Debit_Volume_Average_Last — 30_Days. 
             For each row, take the Debit_Volume_By_Sending_Institution — 90_Day, divide by the Number_Business_Days_Last — 90_Days (Taken from the Investigation System) and store in field Debit_Volume_Average_Last — 90_Days. 
             For each row, take the Debit_Volume_By_Sending_Institution — 90 — 180_Day, divide by the Number_Business_Days_Last — 90 — 180_Days (Taken from the Investigation System) and store in field Debit_Volume_Average_Last — 90 — 180_Days. 
             For each row take the Debit_Volume_Average_Last — 30_Days and divide it by the Average_Last — 30_Days to compute the Reciprocity — 30 field. 
             For each row take the Debit_Volume_Average_Last — 90_Days and divide it by the Average_Last — 90_Days to compute the Reciprocity — 90 field. 
             For each row take the Debit_Volume_Average_Last — 90 — 180_Days and divide it by the Average_Last — 90 — 180_Days to compute the Reciprocity — 180 field. 
           
         
       
     
         [0000]    
       
         
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
             
           
               
                   
                   
               
               
                   
                 Average 
                 Debit 30 
                 Average 
                 Debit 90 
                 Average 
                 Debit 180 
                 Reciprocity 
                 Reciprocity 
                 Reciprocity 
               
               
                   
                 30 Days 
                 Days 
                 90 Days 
                 Days 
                 180 Days 
                 Days 
                 30 
                 60 
                 180 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                 Nostro Account 
                 5,000 
                 6,000 
                 5,500 
                 6,100 
                 6,000 
                 6,250 
                 1.2000 
                 1.1091 
                 1.0417 
               
               
                 Bank A 
               
               
                 Nostro Account 
                 2,000 
                 2,500 
                 2,800 
                 3,300 
                 1,800 
                 2,200 
                 1.2500 
                 1.1786 
                 1.2222 
               
               
                 Bank B 
               
               
                 Nostro Account 
                 1,000 
                 1,300 
                 900 
                 900 
                 1,200 
                 1,100 
                 1.3000 
                 1.0000 
                 0.9167 
               
               
                 Bank C 
               
               
                   
               
             
          
         
       
     
       8. Corporate Banking: Competitive Wallet Share Analyzer 
     Pattern: 
     (Based on MT103 fields and the customer information file) 
     1. Is the Ordering Customer an existing customer? 
       [0000]    
       
         
           
             Is field  50 A=customer in our CIF? 
             If Yes Go to Step 2. 
             If No. Go to next record. 
           
         
       
     
       2. Is the Sending Institution one of our competitors (from specified list)? 
       [0000]    
       
         
           
             Is field  51 A on list? 
             if no, move to next record 
             if yes include in Corporate Banking: Competitive Wallet Share Analyzer file 
           
         
       
     
       3. Save record to Database 
       [0000]    
       
         
           
             Fields 
               32 A—Value Date and Amount 
               50 A—Ordering Customer 
               51 A—Sending Institution 
               59 A—Beneficiary  108  Customer 
           
         
       
     
       Analytics: 
     Identify what percentage each of our customers is sending through other banks rather than through us. 
     Action: 
       [0000]    
       
         
           
             Sort by descending order of MT103s for debit (payments we initiate) and credit (payments we receive) based on field  50 A 
             Sort by field  51 A (Sending Correspondent) 
             If value date minus the current date is less than 30 days, count the number of payments 
             If value date minus the current date is less than 90 days, count the number of payments 
             If value date minus the current date is less than 180 days, count the number of payments 
             If value date minus the current date is less than 365 days, count the number of payments 
             Divide each  50 A- 51 A pairing by our estimated market share to approximate total market volume 
           
         
       
     
         [0000]    
       
         
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
               
               
               
               
             
           
               
                   
                   
               
               
                   
                 Competitor 1 
                 Competitor 2 
                 Competitor 3 
                 Competitor 4 
                   
               
             
          
           
               
                   
                   
                 % 
                   
                 % 
                   
                 % 
                   
                 % 
                 Debit Volume of 
                   
               
               
                 For last 30 days 
                 volume 
                 share 
                 volume 
                 share 
                 volume 
                 share 
                 volume 
                 share 
                 our MT103 
                 Total 
               
               
                   
               
             
          
           
               
                 Customer A 
                 2,000 
                 20% 
                 3,500 
                 35% 
                 1,000 
                 10% 
                 600 
                  6% 
                 3,000 
                 10,100 
               
               
                 Customer B 
                 1,750 
                 28% 
                 750 
                 12% 
                 700 
                 11% 
                 500 
                  8% 
                 2,600 
                 6,300 
               
               
                 Customer C 
                 1,250 
                 21% 
                 1,000 
                 17% 
                 600 
                 10% 
                 625 
                 11% 
                 2,380 
                 5,855 
               
               
                 Customer D 
                   250 
                  7% 
                 600 
                 16% 
                 600 
                 16% 
                 1,150 
                 31% 
                 1,054 
                 3,654 
               
               
                 Market Share 
                 20% 
               
               
                   
               
             
          
         
       
     
       9. Corporate Banking: End of Day Liquidity Retention Targeting System 
       [0248]    Pattern: 
       (Based on MT103 fields and the customer information file) 
     Define Significant_Transfer_Amount 
     1. Is the outbound payment greater than Significant_Transfer_Amount? 
       [0000]    
       
         
           
             Is  32 A&gt;Significant_Transfer_Amount? 
             If Yes Go to Step 2. 
             If No. Go to next record. 
           
         
       
     
       2. Is it Near_End_Of Day? 
       [0000]    
       
         
           
             Is the time stamp after Near_End_Of Day (field  13 C)? 
             if no, move to next record 
             if yes include in End of Day Liquidity Retention file 
           
         
       
     
       3. Save record to Database 
       [0000]    
       
         
           
             Fields 
               32 A—Value Date and Amount 
               50 A—Ordering Customer 
               59 A—Beneficiary  108  Customer 
           
         
       
     
       Analytics: 
     To identify large payments at end of day to top beneficiaries  108  with a high percentage of transaction volume and $ value 
     Action: 
       [0000]    
       
         
           
             Aggregate Value for each  50 A 
             Is end of day balance for  50 A from DDA less than Retention_Target % of aggregated total?
           No—move to next customer   Yes Flag records.   
         
             Add up number of customers and beni 
             Review information in DB for last 30 days 
             List by  50 A where  59 A is greater than 20% of number transaction and value (field  32 A) is greater than 20% of total. 
             Rank order by  50 A- 59 A pair with highest transaction value. 
           
         
       
     
         [0000]    
       
         
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
             
           
               
                   
                   
               
               
                   
                 Investment 
                 Investment 
                 Investment 
                 Investment 
                 Investment 
                   
               
               
                   
                 Manager 1 
                 Manager 2 
                 Manager 3 
                 Manager 4 
                 Other 
                 Total 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                 Customer A 
                 2,000 
                 1,000 
                 500 
                 200 
                 200 
                 3,900 
               
               
                 Customer B 
                 800 
                 1,200 
                 600 
                 400 
                 300 
                 3,300 
               
               
                 Customer C 
                 600 
                 800 
                 1,000 
                 300 
                 500 
                 3,200 
               
               
                   
               
               
                 * in millions 
               
             
          
         
       
     
       10. Corporate Banking: SEPA Inbound Opportunity Identification and Targeting System 
     Pattern: 
     (Based on MT103 fields and the customer information file) 
     1. Is the Ordering Customer not an existing customer? 
       [0000]    
       
         
           
             Is field  50 A=not customer in our CIF? 
             If No then go to next record. 
             If yes. Go to step 2. 
             DEFINE HOME COUNTRY and EUROZONE 
           
         
       
     
       2. Is the country code outside the home country? 
       [0000]    
       
         
           
             Is field  50 A is not equal to home country? 
             If no, go to next record 
             If yes, go to step 3. 
           
         
       
     
       3. Is the country code a Euro Zone county? 
       [0000]    
       
         
           
             Is field  50 A=Eurozone country? 
             If yes to go step 4. 
             If no to go next record. 
           
         
       
     
       4. Save record to SEPA_Inbound file 
       [0000]    
       
         
           
             Fields 
               32 A—Value Date and Amount 
               50 A—Ordering Customer 
               51 A—Sending Institution 
               52 A—Ordering Institution 
               53 A—Senders Correspondent 
               56 A—Intermediary Institution 
               59 A—Beneficiary  108  Customer 
           
         
       
     
       Analytics: 
       [0285]    Ranking Order for Highest Potential New Customer based on Volume of Payments. Will aggregate the number of payments by Ordering Customer. 
       Action: 
       [0000]    
       
         
           
             Sort by  50 A 
             If value date minus the current date is less than 90 days count the number of payments (from field  32 A) 
             Take number of payment by each  50 A and divide by total number of payments in file to derive % of total by each Ordering Customer. 
             Show cumulative % of total for top 2, 3, 4, 5 etc. 
           
         
       
     
         [0000]    
       
         
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                   
                   
               
               
                   
                   
                 Total number 
                   
                   
               
               
                   
                 Ordering 
                 of payments 
                   
                 Cumulative 
               
               
                   
                 Customer 
                 90 days 
                 % of Total 
                 % of Total 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 Propsect A 
                 5,800 
                 7% 
                 7% 
               
               
                   
                 Prospect B 
                 3,500 
                 4% 
                 11% 
               
               
                   
                 Prospect C 
                 2,500 
                 3% 
                 14% 
               
               
                   
                 Prospect D 
                 2,200 
                 3% 
                 16% 
               
               
                   
                 Total Payment 
                 85,000 
               
               
                   
                   
               
             
          
         
       
     
       10. Corporate Banking: SEPA Outbound Opportunity Identification and Targeting System 
     Pattern: 
     (Based on MT103 fields and the customer information file) 
     ALL THE SAME CHANGES 
     1. Is the Beneficiary  108  Customer an existing customer? 
       [0000]    
       
         
           
             Is field  59 A=customer in our CIF? 
             If Yes then go to next record. 
             If No. Go to step 2. 
           
         
       
     
       2. Is the country code the home country? 
       [0000]    
       
         
           
             Is field  59 A=to home country? 
             If yes, go to next record 
             If no, go to step 3. 
           
         
       
     
       3. Is the country code a Euro Zone county? 
       [0000]    
       
         
           
             Is field  59 A=Eurozone country? 
             If yes to go step 4. 
             If no to go next record. 
           
         
       
     
       4. Save record to Database 
       [0000]    
       
         
           
             Fields 
               32 A—Value Date and Amount 
               50 A—Ordering Customer 
               51 A—Sending Institution 
               52 A—Ordering Institution 
               53 A—Senders Correspondent 
               56 A—Intermediary Institution 
               59 A—Beneficiary  108  Customer 
           
         
       
     
       Analytics: 
       [0307]    Ranking Order for Highest Potential New Customer based on Volume of Payments. Will aggregate the number of payments by Beneficiary  108  Customer. 
       Action: 
       [0000]    
       
         
           
             Sort by  59 A 
             If value date minus the current date is less than 90 days count the number of payments (from field  32 A) 
             Take number of payment by each  59 A and divide by total number of payments from top  100  to derive % of total by each beneficiary  108 . 
             Show cumulative % of total for top 2, 3, 4, 5 etc. 
           
         
       
     
         [0000]    
       
         
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                   
                   
               
               
                   
                 Benificiary 
                 Total number of 
                 % 
                 Cumulative 
               
               
                   
                 Customer 
                 payments 90 days 
                 of Total 
                 % of Total 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 Propsect A 
                 6,800 
                 6% 
                 6% 
               
               
                   
                 Prospect B 
                 5,700 
                 5% 
                 12% 
               
               
                   
                 Prospect C 
                 3,400 
                 3% 
                 15% 
               
               
                   
                 Prospect D 
                 3,100 
                 3% 
                 18% 
               
               
                   
                 Total top 100 
                 105,000 
               
               
                   
                   
               
             
          
         
       
     
       11. Corporate Banking: Cross Currency Opportunity Identification and Targeting System 
     Pattern: 
     (Based on MT103 fields and the customer information file) 
       [0312]    1. On an outbound payment list top 10% of payment customers by currency. CLEAN 
       2. Is the Company, Parent, or Ultimate Parent a Target_Country based company from the CIF? 
       [0000]    
       
         
           
             If No, go to next record. 
             If yes go to step 3. 
           
         
       
     
       SELECT CURRENCY 
     3. Go to Mapping # “8 Corporate Banking: Competitive Wallet Share Analyzer”. 
     12. Corporate Banking: Network Opportunity Identification and Targeting Systems 
     Pattern: 
     (Based on MT103 fields and the customer information file) 
     1. Is the Beneficiary  108  Customer an existing customer? 
       [0000]    
       
         
           
             Is field  59 A=customer in our CIF? 
             If Yes then go to next record. 
             If No. Go to step 2. 
           
         
       
     
       2. Save record to Database 
       [0000]    
       
         
           
             Fields 
               32 A—Value Date and Amount 
               50 A—Ordering Customer 
               51 A—Sending Institution 
               52 A—Ordering Institution 
               53 A—Senders Correspondent 
               56 A—Intermediary Institution 
               57 A—Account With Institution 
               59 A—Beneficiary  108  Customer 
           
         
       
     
       Analytics: 
       [0327]    Ranking Order for Highest Potential New Customer based on Volume of Payments. Will aggregate the number of payments by Beneficiary  108  Customer. 
       Action: 
       [0000]    
       
         
           
             Sort by  59 A 
             If value date minus the current date is less than 90 days count the number of payments (from field  32 A) 
             Take number of payment by each  59 A and divide by total number of payments from top  100  to derive % of total by each beneficiary  108 . 
             Show cumulative % of total for top  2 ,  3 ,  4 ,  5  etc. 
           
         
       
     
         [0000]    
       
         
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                   
                   
               
               
                   
                 Benificiary 
                 Total number of 
                 % 
                 Cumulative 
               
               
                   
                 Customer 
                 payments 90 days 
                 of Total 
                 % of Total 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                   
                 Propsect A 
                 11,005 
                 5% 
                 5% 
               
               
                   
                 Prospect B 
                 8,025 
                 4% 
                 9% 
               
               
                   
                 Prospect C 
                 7,800 
                 4% 
                 13% 
               
               
                   
                 Prospect D 
                 5,800 
                 3% 
                 16% 
               
               
                   
                 Total top 100 
                 205,000 
               
               
                   
                   
               
             
          
         
       
     
       Sort by  50 A (Ordering Party) to show relationship between Ordering Customer and Beneficiary  108  Customer. 
       [0332]      
         [0000]    
       
         
               
               
               
               
             
               
               
               
               
             
           
               
                   
               
               
                 Benificiary 
                 Total number of payments 90 days 
                 % of 
                 Cumulative 
               
               
                 Customer 
                 from a specfic Ordering Customer 
                 Total 
                 % of Total 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 Propsect A 
                 650 
                 7% 
                 7% 
               
               
                 Prospect B 
                 520 
                 5% 
                 12% 
               
               
                 Prospect C 
                 310 
                 3% 
                 15% 
               
               
                 Prospect D 
                 295 
                 3% 
                 18% 
               
               
                 Total top 100 
                 9,800 
               
               
                   
               
             
          
         
       
     
       13. Compliance: Suspicious Payment Pattern Monitoring and Analysis System 
     Pattern: 
     (Based on MT103 fields and the customer information file) 
     Inbound: 
     Identify High Risk Customers 
       [0000]    
       
         
           
             Does  59 A match against bank provided list? 
             If no go to next record. 
             If yes Save Record to Database. 
           
         
       
     
       Outbound: 
     Identify High Risk Customers 
       [0000]    
       
         
           
             Does  50 A match against bank provided list 
             If no go to next record. 
             If yes Save Record to Database. 
           
         
       
     
       Save record to Database 
       [0000]    
       
         
           
             Fields 
               32 A—Value Date and Amount 
               50 A—Ordering Customer 
               51 A—Sending Institution 
               52 A—Ordering Institution 
               53 A—Senders Correspondent 
               54 A—Receivers Correspondent 
               56 A—Intermediary Institution 
               57 A—Account With Institution 
               59 A—Beneficiary  108  Customer 
           
         
       
     
       Analytics: 
     Identify when transaction is between our high-risk customer and another high-risk customer. 
     Action: 
       [0000]    
       
         
           
             Are  50 A and  59 A both high-risk customers 
           
         
       
     
       Analytics: 
     Do specific incoming payment match within 5% of outbound payments same day. 
     Action: 
       [0000]    
       
         
           
             Look up 32 for value in inbound same day 
             Look up 32 for value in outbound same day 
             Compare to +−5%. 
             If outside 5% no action taken 
             If inside 5% flag and move to Suspicious Database. 
             Match  50 A,  59 A pairs and aggregate number of transactions to identify patterns