Patent Publication Number: US-2015081483-A1

Title: Intraday cash flow optimization

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This is a continuation application that claims the benefit of U.S. patent application Ser. No. 14/027,411 filed Sep. 16, 2013, the contents of which are incorporated by reference herein in their entirety. 
    
    
     BACKGROUND 
     The present invention relates to bank management systems and, more specifically, to intraday cash flow optimization systems and methods for banking organizations. 
     BASEL III is a set of worldwide banking standards to regulate the amount of capital that banks need to keep on hand for intraday transactions. The intent of BASEL III is to increase bank liquidity and to reduce the amount of leverage. BASEL III requires banks to monitor transaction operations in a shorter time window with broader context as compared to earlier standards known as BASEL II. Monitoring current liquidity is particularly challenging for large banking organizations which include relatively isolated departments that operate using substantially independent systems and processes. As banking organizations are merged or acquired and assimilated, there is a greater tendency to operate various departments or divisions separately. 
     Bank transactions often involve a period of latency that can span multiple days for the transactions to complete. For example, a transfer of funds between accounts can take two or more days to clear. Backend systems typically resolve transactions in batches within a day or two of initiating the transactions. Transaction latency increases uncertainty in estimating current liquidity at any given point in time. Operational costs for banks increase as a larger amount of reserves are maintained to buffer for uncertainty and latency. 
     SUMMARY 
     According to one embodiment of the present invention, a method for intraday cash flow optimization is provided. The method includes accessing, by a processor, transactions on a business-to-business integration network from a plurality of sources linked with payment delivery system data from a financial service system. The transactions are associated with two or more compartmentalized entities. The transactions are characterizes based on the payment delivery system data and an analysis of customer profile data. The transactions associated with two or more compartmentalized entities are linked as integrated information based on the characterizing of the transactions. An intraday receivables prediction engine and an intraday payables prediction engine are applied to the integrated information to produce an estimation of intraday cash flow. The estimation of intraday cash flow is monitored relative to intraday operations optimization conditions. An alert is generated based on determining that at least one of the intraday operations optimization conditions is met. 
     According to another embodiment of the present invention, a system for intraday cash flow optimization is provided. The system includes a processor communicatively coupled to a business-to-business integration network and a financial service system. An intraday cash flow optimization tool is executable by the processor. The intraday cash flow optimization tool is configured to implement a method. The method includes accessing, by the processor, transactions on the business-to-business integration network from a plurality of sources linked with payment delivery system data from a financial service system. The transactions are associated with two or more compartmentalized entities. The transactions are characterizes based on the payment delivery system data and an analysis of customer profile data. The transactions associated with two or more compartmentalized entities are linked as integrated information based on the characterizing of the transactions. An intraday receivables prediction engine and an intraday payables prediction engine are applied to the integrated information to produce an estimation of intraday cash flow. The estimation of intraday cash flow is monitored relative to intraday operations optimization conditions. An alert is generated based on determining that at least one of the intraday operations optimization conditions is met. 
     According to a further embodiment of the present invention, a computer program product for intraday cash flow optimization is provided. The computer program product includes a storage medium embodied with machine-readable program instructions, which when executed by a computer causes the computer to implement a method. The method includes accessing transactions on the business-to-business integration network from a plurality of sources linked with payment delivery system data from a financial service system. The transactions are associated with two or more compartmentalized entities. The transactions are characterizes based on the payment delivery system data and an analysis of customer profile data. The transactions associated with two or more compartmentalized entities are linked as integrated information based on the characterizing of the transactions. An intraday receivables prediction engine and an intraday payables prediction engine are applied to the integrated information to produce an estimation of intraday cash flow. The estimation of intraday cash flow is monitored relative to intraday operations optimization conditions. An alert is generated based on determining that at least one of the intraday operations optimization conditions is met. 
     Additional features and advantages are realized through the techniques of the present invention. Other embodiments and aspects of the invention are described in detail herein and are considered a part of the claimed invention. For a better understanding of the invention with the advantages and the features, refer to the description and to the drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       The subject matter which is regarded as the invention is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The forgoing and other features, and advantages of the invention are apparent from the following detailed description taken in conjunction with the accompanying drawings in which: 
         FIG. 1  depicts a block diagram of a system upon which intraday cash flow optimization may be implemented according to an embodiment of the present invention; 
         FIG. 2  depicts a high-level data flow diagram for an intraday cash flow optimization according to an embodiment; 
         FIG. 3  depicts a low-level data flow diagram for intraday cash flow optimization according to an embodiment; 
         FIG. 4  depicts a process for intraday cash flow optimization according to an embodiment; and 
         FIG. 5  depicts a computer system for intraday cash flow optimization according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     Exemplary embodiments provide intraday cash flow optimization for banking or financial organizations. Embodiments leverage a business-to-business integration network to access transactions from multiple sources to assist in determining an estimate of intraday cash flow. The transactions are associated with two or more compartmentalized entities, also referred to as “silos”, which can be effectively isolated from each other and observed without direct modification. The transactions link transaction data from multiple sources to payment delivery system data, where the payment delivery system data can be used to establish a domain business process for a banking or financial organization. The transactions can be characterized based on the payment delivery system data and an analysis of customer profile data. The transactions associated with two or more compartmentalized entities are linked as integrated information based on the characterizing of the transactions. 
     Historical transaction data can be analyzed offline to search for patterns and develop model parameters. The model parameters can be applied for real-time analytics in conjunction with external data to predict intraday receivables and intraday payables. Reconciling the intraday receivables prediction and the intraday payables prediction at different levels of hierarchy can produce an overall estimation of intraday cash flow as well as an estimation of intraday cash flow on a customer and account basis. Once estimation of intraday cash flow is performed, the estimate can be used for real-time alerts, reinvestment suggestions, and/or other monitoring purposes for intraday cash flow optimization. 
     Turning now to  FIG. 1 , a bank management system  100  upon which intraday cash flow optimization may be implemented will now be described in an exemplary embodiment. The bank management system  100  includes a plurality of electronic access points  102  in communication with gateways  104 . Each of the gateways  104  may be coupled to a department computer system  106 . Each department computer system  106  is coupled to a regional banking computer system  108 . The regional banking computer system  108  may also be accessed via gateway  110  by bank branches  112  that provide physical access to customers  114 . The bank management system  100  is partitioned into regional banking networks  116  that are joined by a business-to-business integration network  118 . The regional banking networks  116  may be geographically distributed in different locations, such as California, New York, etc. 
     Other systems may also be coupled to the business-to-business integration network  118 . In one example, an intraday cash flow optimization computer system  120  is coupled to the business-to-business integration network  118 , where the intraday cash flow optimization computer system  120  is configured to provide estimates of the intraday cash flow and optimizations based on the estimates. The intraday cash flow optimization computer system  120  can also access external data sources  122  in real-time through a network  124 . The external data sources  122  may be third-party generated data, such as credit reports, new reports, stock market data, bond market data, and the like. The network  124  may be any type of network known in the art. In one example, the network  124  is the Internet. 
     Although the bank management system  100  is depicted in  FIG. 1  as including two substantially similar regional banking networks  116  joined by the business-to-business integration network  118 , the scope of embodiments is not so limited. There may be any number of instances of the electronic access points  102 , gateways  104 , department computer system  106 , regional banking computer system  108 , gateway  110 , bank branches  112 , and regional banking networks  116  with various topologies. Additional elements can be added, removed, or combined in the regional banking networks  116 . Moreover, the intraday cash flow optimization computer system  120  can be distributed in multiple computer systems and can access other networks and/or data sources (not depicted). In exemplary embodiments, the business-to-business integration network  118  provides a generic communication interface between a number of elements that may otherwise be isolated from each other. For example, instances of the department computer system  106  can be separate compartmentalized entities or silos, where a trust department may not have direct access to data in a treasury department even within the same regional banking network  116 . 
       FIG. 2  depicts a high-level data flow diagram  200  for an intraday cash flow optimization according to an embodiment. An intraday cash flow optimization tool  202  may be executed on the intraday cash flow optimization computer system  120  of  FIG. 1 . The intraday cash flow optimization tool  202  can access transactions  204  on the business-to-business integration network  118  of  FIG. 1  from a number of sources  210  linked with payment delivery system data  206  from a financial service system  208 . The business-to-business integration network  118  can interface to a number of protocols  209  to receive transaction data  205  from the sources  210 . For example, the sources  210  can communicate the transaction data  205  via protocols  209  such as Simple Mail Transfer Protocol (SMTP)  212 , Electronic Data Interchange-Internet Integration (EDIINT)  214 , File Transfer Protocol (FTP)  216 , Hypertext Transfer Protocol (HTTP)  218 , Secure File Transfer Protocol (SFTP)  220 , Simple Object Access Protocol (SOAP)  222 , Web Distributed Authoring and Versioning (WebDAV)  224 , Electronic Data Interchange (EDI)/eXtensible Markup Language (XML)  226 , and various file systems  228 . 
     The sources  210  of the transaction data  205  can include a variety of inputs from the electronic access points  102  of  FIG. 1 , bank branches  112  of  FIG. 1 , or other elements of the regional banking networks  116  of  FIG. 1 , such as requests from a department computer system  106  of  FIG. 1  or regional banking computer system  108  of  FIG. 1  as compartmentalized entities. For example, the sources  210  can include e-mail  230 , phone/interactive voice response  232 , bank branches  112 , internet cash management software  234 , and bulk files/Enterprise Resource Planning (ERP)  236  to provide the transaction data  205  for the transactions  204 . The business-to-business integration network  118  provides a common format for the transaction data  205  to be processed from the sources  210  using any of the protocols  209 . The transaction data  205  can be configured in a generalized format that is linked to the payment delivery system data  206  in the transactions  204 . In this way, the relative isolation or compartmentalization of each source  210  of the transaction data  205  and payment delivery systems  238  providing the payment delivery system data  206  can be maintained while the transactions  204  are examined by the intraday cash flow optimization tool  202 . 
     The financial service system  208  may be supported by various components of the bank management system  100  of  FIG. 1  according to the payment delivery systems  238 . For example, a department computer system  106  of FIG. may support a subset of the payment delivery systems  238 , while a regional banking computer system  108  of  FIG. 1  supports another subset of the payment delivery systems  238 . Examples of the payment delivery systems  238  include Automated Clearing House (ACH)  240 , Electronic Data Interchange (EDI)  242 , wire  244 , Society for Worldwide Interbank Financial Telecommunication (SWIFT)  246 , and check  248 . The financial service system  208  can interface with the different payment delivery systems  238  providing the payment delivery system data  206 , where the payment delivery system data  206  provide business process detail and correlate to the transaction data  205  in the transactions  204 . For example, a transaction  204  can be made by e-mail  230 , sent using SMTP  212 , and include payment delivery system data  206  using a payment delivery system  238  of ACH  240 . Transaction data  205  may include customer identifiers, account information, routing information, and other constraints associated with the transactions  204 . The payment delivery systems  238  can also be treated as separate compartmentalized entities or silos, where each payment delivery system  238  is independently managed relative to each other. 
     The intraday cash flow optimization tool  202  includes one or more offline model learning engines  250  that can access historical values of the transactions  204  including transaction data  205  and payment delivery system data  206  as historical transaction data  262  for identifying patterns to produce model parameters  252 . The model parameters  252  may be formatted as coefficients to be applied by an online transaction analytics engine  254 . Pattern analysis can include looking for repeating sequences of the transactions  204  based on a particular customer or account. The patterns may also include tracking time between posting and completion of repeated transactions  204  based on a particular source  210 , customer, account, and/or payment delivery system  238 . Failed transactions  204 , for instance, due to insufficient funds, may also be tracked on a customer and/or account basis to determine a risk factor or likelihood of repetition of a similar pattern. The one or more offline model learning engines  250  may operate on data spanning several years to improve a level of confidence associated with identified patterns used to create the model parameters  252 . The one or more offline model learning engines  250  may also access external information  256  from the external data sources  122  in developing patterns for the model parameters  252 . For example, accessing a customer credit report can increase confidence in a likelihood of repetition of successful or failed transactions  204 . 
     The online transaction analytics engine  254  can apply the model parameters  252  to the transactions  204  in real-time in combination with customer profile data  258  from customer profiles  260  and external information  256  from external data sources  122 . For example, accessing Bloomberg reports as the external information  256  for a business account can provide further insight as to the likelihood of the transactions  204  following previous patterns or an increased risk of failing to repeat previous patterns, e.g., based on a recent negative report associated with customer profile data  258  for a particular customer involved in a transaction  204 . The online transaction analytics engine  254  may be comprised of a separate intraday receivables prediction engine to produce an intraday receivables prediction and an intraday payables prediction engine to produce an intraday payables prediction as further described in reference to  FIG. 3 . 
       FIG. 3  depicts a low-level data flow diagram  300  for intraday cash flow optimization according to an embodiment. The data flow diagram  300  depicts three stages including information integration  302 , prediction and monitoring  304 , and intraday operations optimization  306 . The information integration  302  includes a first silo  308  and a second silo  310  in this example, where the first and second silos  308  and  310  are examples of compartmentalized entities. The first and second silos  308  and  310  may be associated with separate bank departments, organizations, or systems, such as different instances of the department computer system  106  or regional banking computer system  108  of  FIG. 1 . 
     The first silo  308  provides transactions  312  and customer profile data  314  to linked data analytics  315 . The second silo  310  provides transactions  316  and customer profile data  318  to the linked data analytics  315 . The transactions  312  and  316  may be instances of the transactions  204  of  FIG. 2 , and the customer profile data  314  and  318  may be instances of the customer profile data  258  of  FIG. 2 . The linked data analytics  315  also receives the external information  256  that may include news reports  320 , stock market data  322 , as well as other sources (not depicted). The linked data analytics  315  combines data from various sources such as the first silo  308 , the second silo  310 , and the external information  256  to produce integrated information  324 . The transactions  312  and  316  can be characterized based on the payment delivery system data  206  of  FIG. 2  and an analysis of the customer profile data  314  and  318 . The characterized transactions  312  and  316  may have common dates, account numbers, and customer data that enable grouping and integration of data even though they originated from different compartmentalized entities, such as the first and second silos  308  and  310 . 
     The integrated information  324  is provided to an intraday receivables prediction engine  326  and an intraday payables prediction engine  328 . As previously described, the intraday receivables prediction engine  326  and the intraday payables prediction engine  328  may be components of the online transaction analytics engine  254  of  FIG. 2 . The intraday receivables prediction engine  326  is configured to produce an intraday receivables prediction  330 , and the intraday payables prediction engine  328  is configured to produce an intraday payables prediction  332 . The intraday receivables prediction engine  326  can extract and operate on receivable data  334  from the integrated information  324 , while the intraday payables prediction engine  328  can extract and operate on payable data  336  from the integrated information  324 . 
     An example of a prediction model that be applied by the intraday receivables prediction engine  326  and/or the intraday payables prediction engine  328  is provided in equation 1 as follows. 
         y   —   t=a 1 *y _( t− 1)+ a 2 *y _( t− 2)+ . . . + ak*y _( t−k )+ b 1 *y _( t− 24)+ b 2 *y _( t− 30)+ c 1 *x 1 —   t+c 2 *x 2 —   t+ . . . +cp*xp   —   t +white noise,  (Eq. 1)
 
     where y_t is an hourly transaction amount at hour t, t=1, . . . , 24 and hourly transaction amount at hour t, t=1, . . . , 24. Accordingly, y_(t−24) indicates a transaction at the same hour one day before to capture a daily pattern, and y_(t−168) indicates a transaction at the same hour one week before to capture the weekly pattern. Values x1_t, x2_t, . . . , xp_t are other contributing factors, and a1, a2, . . . , cp are parameters which indicate factor impacts. The parameters a1, a2, . . . , cp may be derived from the model parameters  252  of  FIG. 1 . Once the model parameters  252  of  FIG. 1  are estimated, y_t can be predicted for a transaction amount at future time t. When applied to the receivable data  334  and the payable data  336 , the intraday receivables prediction engine  326  and the intraday payables prediction engine  328  can respectively produce the intraday receivables prediction  330  and the intraday payables prediction  332 . 
     A hierarchical liquidity estimation  338  is performed to reconcile the intraday receivables prediction  330  and the intraday payables prediction  332  in a hierarchical format to produce an estimation of intraday cash flow  340 . The intraday receivables prediction  330  and the intraday payables prediction  332  may be produced on a customer and account basis. Accordingly, the hierarchical liquidity estimation  338  can perform liquidity analysis on a customer or account basis, as well as at different levels of bank organization, such as a branch level, department level, regional level, and the like. The estimation of intraday cash flow  340  may be provided to a real-time alert engine  342 , a reinvestment engine  344 , and/or to a visualization dashboard  346 . 
     The real-time alert engine  342  can monitor the estimation of intraday cash flow  340  relative to intraday operations optimization conditions  348 . The intraday operations optimization conditions  348  can be defined as near threshold limits to trigger an alert prior to violating one or more of the intraday operations optimization conditions  348 . The intraday operations optimization conditions  348  may include one or more of: known issues  350  for account management, mitigation rules  352  of accounts, and regulations  354  for maintaining liquidity. Examples of known issues  350  for account management can be defined as alert limits for constraints on an account, location/time based issues, minimum balance rules, and the like. Examples of mitigation rules  352  of accounts can be defined as alert limits for keeping money in an account for a certain period of time, limits for triggering specific actions, account closure rules, and the like. The regulations  354  for maintaining liquidity can be defined as alert limits for liquidity and higher level rules defined according to, for example, BASEL III regulations. The real-time alert engine  342  can generate an alert  356  based on determining that at least one of the intraday operations optimization conditions  348  is met. The alert  356  may be in the form of an electronic message, audio or video output, and/or data provided to the visualization dashboard  346  for further processing. 
     The reinvestment engine  344  can monitor the estimation of intraday cash flow  340  relative to reinvestment conditions  358 . The reinvestment engine  344  can analyze the estimation of intraday cash flow  340  to determine where excess intraday cash flow exists and provide one or more reinvestment options  366  based on determining that at least one of the reinvestment conditions  358  is met by the estimation of intraday cash flow  340 . The one or more reinvestment options  366  may be in the form of an electronic message, audio or video output, and/or data provided to the visualization dashboard  346  for further processing. Similar to the intraday operations optimization conditions  348 , the reinvestment conditions  358  may include one or more of: known issues  360  for account management, mitigation rules  362  of accounts, and regulations  364  for maintaining liquidity. Rather than comparing the reinvestment conditions  358  to minimum threshold limits, the reinvestment conditions  358  may define safe maximum values where liquidity above the maximum threshold limits can be reinvested without a likely risk of failing to meet intraday liquidity requirements. The reinvestment engine  344  may access the external information  256  in making recommendations based on current market conditions, where a larger excess liquidity can support consideration of incrementally greater investment risk. For example, a tiered risk strategy in investment options can be applied as a greater amount of liquidity is identified. 
     The visualization dashboard  346  can summarize the estimation of intraday cash flow  340 , any alert  356 , and/or reinvestment options  366 . The visualization dashboard  346  may be a collection of static data or can be interactive, allowing a user to drilldown into different organization, department, customer, and account level data. There can be multiple instances of the visualization dashboard  346 , where different users can access underlying data but apply different views or filters to the data. Interaction with the visualization dashboard  346  can also trigger other actions, such as initiating one of the reinvestment options  366  suggested by the reinvestment engine  344 . 
       FIG. 4  depicts a process  400  for intraday cash flow optimization in accordance with an embodiment. The process  400  is described in reference to  FIGS. 1-4  and need not be performed in the precise order as depicted in  FIG. 4 . In this example, a processor of the intraday cash flow optimization computer system  120  of  FIG. 1  executes the intraday cash flow optimization tool  202  to perform the process  400 . At block  402 , the intraday cash flow optimization tool  202  accesses transactions  204  on the business-to-business integration network  118  from a plurality of sources  210  linked with payment delivery system data  206  from the financial service system  208 . The transactions  204  can be the transactions  312  and  316  associated with two or more compartmentalized entities, such as silos  308  and  310 . 
     At block  404 , the intraday cash flow optimization tool  202  characterizes the transactions  204  based on the payment delivery system data  206  and an analysis of customer profile data  258 . With respect to the transactions  312  and  316 , the customer profile data  258  is comprised of customer profile data  314  and  318 . The analysis of the customer profile data  314  and  318  can include determining customer and account information associated with the transactions  312  and  316  on a compartmentalized entity basis, i.e., per silo  308 ,  310 . 
     At block  406 , the intraday cash flow optimization tool  202  links the transactions  312  and  316  associated with the silos  308  and  310  as two or more compartmentalized entities to form integrated information  324  based on the characterizing of the transactions  312  and  316 . Linking can be performed by the linked data analytics  315 . External information  256  can be accessed in real-time to link with the transactions  312  and  316  and form the integrated information  324 . The external information  256  may relate to one or more of: the transactions  312 ,  316  and the customer profile data  314 ,  318 . 
     At block  408 , the intraday cash flow optimization tool  202  applies the intraday receivables prediction engine  326  and the intraday payables prediction engine  328  to the integrated information  324  to produce an estimation of intraday cash flow  340 . One or more offline model learning engines  250  can be applied to produce model parameters  252  based on identifying patterns in historical transaction data  262 . The model parameters  252  are applied to the transactions  312 ,  316  in real-time in combination with the customer profile data  314 ,  318  and external information  256  from external data sources  122  by the online transaction analytics engine  254 . The online transaction analytics engine  254  can include the intraday receivables prediction engine  326  and the intraday payables prediction engine  328  to produce an intraday receivables prediction  330  and an intraday payables prediction  332 . The intraday receivables prediction  330  and the intraday payables prediction  332  may be produced on a customer and account basis. The hierarchical liquidity estimation  338  may reconcile the intraday receivables prediction  330  and the intraday payables prediction  332  in a hierarchical format to produce the estimation of intraday cash flow  340 . 
     At block  410 , the intraday cash flow optimization tool  202  monitors the estimation of intraday cash flow  340  relative to intraday operations optimization conditions  348 . The intraday operations optimization conditions  348  can include one or more of: known issues  350  for account management, mitigation rules  352  of accounts, and regulations  354  for maintaining liquidity. Monitoring can be performed by the real-time alert engine  342 . The monitoring can also be performed by the reinvestment engine  344  relative to the reinvestment conditions  358 . 
     At block  412 , the intraday cash flow optimization tool  202  generates an alert  356  based on determining that at least one of the intraday operations optimization conditions  348  is met. The intraday cash flow optimization tool  202  may also output one or more reinvestment options  366  based on determining that at least one of the reinvestment conditions  358  is met by the estimation of intraday cash flow  340 . 
     Referring now to  FIG. 5 , a schematic of an example of a computer system  554  in an environment  510  is shown. The computer system  554  is only one example of a suitable computer system and is not intended to suggest any limitation as to the scope of use or functionality of embodiments described herein. Regardless, computer system  554  is capable of being implemented and/or performing any of the functionality set forth hereinabove. The computer system  554  is an embodiment of the intraday cash flow optimization computer system  120  of  FIG. 1 . 
     In the environment  510 , the computer system  554  is operational with numerous other general purpose or special purpose computing systems or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable as embodiments of the computer system  554  include, but are not limited to, personal computer systems, server computer systems, cellular telephones, thin clients, thick clients, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network personal computer (PCs), minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or devices, and the like. 
     Computer system  554  may be described in the general context of computer system-executable instructions, such as program modules, being executed by one or more processors of the computer system  554 . Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. Computer system  554  may be practiced in distributed computing environments, such as cloud computing environments, where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices. 
     As shown in  FIG. 5 , computer system  554  is shown in the form of a general-purpose computing device. The components of computer system  554  may include, but are not limited to, one or more computer processing circuits (e.g., processors) or processing units  516 , a system memory  528 , and a bus  518  that couples various system components including system memory  528  to processor  516 . When embodied as the intraday cash flow optimization computer system  120  of  FIG. 1 , the processor  516  is communicatively coupled to the business-to-business integration network  118  and the financial service system  208  of  FIG. 2 . 
     Bus  518  represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus. 
     Computer system  554  typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system  554 , and it includes both volatile and non-volatile media, removable and non-removable media. 
     System memory  528  can include computer system readable media in the form of volatile memory, such as random access memory (RAM)  530  and/or cache memory  532 . Computer system  554  may further include other removable/non-removable, volatile/non-volatile computer system storage media. By way of example only, storage system  534  can be provided for reading from and writing to a non-removable, non-volatile magnetic media (not shown and typically called a “hard drive”). Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM or other optical media can be provided. In such instances, each can be connected to bus  518  by one or more data media interfaces. As will be further depicted and described below, memory  528  may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention. 
     Program/utility  540 , having a set (at least one) of program modules  542 , may be stored in memory  528  by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data. Each of the operating system, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules  542  generally carry out the functions and/or methodologies of embodiments of the invention as described herein. An example application program or module is depicted in  FIG. 5  as intraday cash flow optimization tool  202  of  FIG. 2 . Although the intraday cash flow optimization tool  202  is depicted separately, it can be incorporated in any application or module. The intraday cash flow optimization tool  202  can be stored directly in the memory  528  or can be accessible by the processor  516  from a location external to the computer system  554 . 
     Computer system  554  may also communicate with one or more external devices  514  such as a keyboard, a pointing device, a display device  524 , etc.; one or more devices that enable a user to interact with computer system  554 ; and/or any devices (e.g., network card, modem, etc.) that enable computer system  554  to communicate with one or more other computing devices. Such communication can occur via input/output (I/O) interfaces  522 . Still yet, computer system  554  can communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter  520 . As depicted, network adapter  520  communicates with the other components of computer system  554  via bus  518 . It should be understood that although not shown, other hardware and/or software components could be used in conjunction with computer system  554 . Examples, include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, redundant array of independent disk (RAID) systems, tape drives, and data archival storage systems, etc. 
     It is understood in advance that although this disclosure includes a detailed description on a particular computing environment, implementation of the teachings recited herein are not limited to the depicted computing environment. Rather, embodiments are capable of being implemented in conjunction with any other type of computing environment now known or later developed (e.g., any client-server model, cloud-computing model, etc.). 
     Technical effects and benefits include integration of a plurality of systems or compartmentalized entities that do not otherwise directly share information. Accessing a business-to-business integration network for transactions enables linking of the transactions with payment delivery system data and data from external sources without modifying the data sources to produce integrated information from which predictive modeling can be developed and applied. Predictive models for intraday receivables and intraday payables applied to real-time data can result in generation of real-time alerts and reinvestment options. 
     As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or computer program product. Accordingly, aspects of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a “circuit,” “module” or “system.” Furthermore, aspects of the present invention may take the form of a computer program product embodied in one or more computer readable medium(s) having computer readable program code embodied thereon. 
     Any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. 
     A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. 
     Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. 
     Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The program code may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). 
     Aspects of the present invention are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one more other features, integers, steps, operations, element components, and/or groups thereof. 
     The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present invention has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated 
     The flow diagrams depicted herein are just one example. There may be many variations to this diagram or the steps (or operations) described therein without departing from the spirit of the invention. For instance, the steps may be performed in a differing order or steps may be added, deleted or modified. All of these variations are considered a part of the claimed invention. 
     While the preferred embodiment to the invention had been described, it will be understood that those skilled in the art, both now and in the future, may make various improvements and enhancements which fall within the scope of the claims which follow. These claims should be construed to maintain the proper protection for the invention first described.