Patent Publication Number: US-2006010032-A1

Title: System, method and computer program product for evaluating an asset management business using experiential data, and applications thereof

Description:
CROSS-REFERENCE TO RELATED APPLICATION  
      This application is a continuation-in-part of U.S. patent application Ser. No. 11/005,119, entitled “METHOD FOR EVALUATING A BUSINESS USING EXPERIENTIAL DATA,” filed on Dec. 6, 2004, which claims priority from U.S. Patent Application No. 60/527,688, entitled “METHOD FOR ASSESSING, MEASURING AND RATING OPERATIONAL PERFORMANCE OF HEDGE FUND BUSINESS,” filed on Dec. 5, 2003, all of which are herein incorporated by reference in their entireties. 
    
    
     BACKGROUND  
      1. Field of the Invention  
      The present invention relates to a method of measuring enterprise risk, performance, and potential, and more particularly, measuring the enterprise risk, performance, and potential of asset management businesses.  
      2. Background  
      Businesses generally strive to improve financial performance through the traditional means of lowering costs and finding new revenue sources. Most businesses can be evaluated by examining the financial statements of the firm, and understanding basic dynamics of customers, suppliers, and competitors.  
      Asset management businesses, however, are different from businesses in general because their success is tied to their ability to identify, measure, and manage risk.  
      The asset management industry has experienced significant change in recent years due to disappointing and volatile stock market returns combined with cash and bond yields reaching historic lows. In addition, the migration of investment talent from mutual funds and traditional asset managers to alternative asset management companies (also called “hedge funds”) has prompted institutional investors to start making large allocations to alternative investment funds. The hedge fund sector has grown dramatically in recent years, as evidenced by both the number of firms and the amount of assets managed. The United States Securities and Exchange Commission estimates that the asset management will grow dramatically over the next 5 or 10 years. Others estimate the hedge fund industry will grow from a current level of $1 trillion of assets under management to more than $2 trillion by 2010.  
      With the growth in the asset management industry, institutional investors and government regulators have become concerned about business risk (or enterprise risk) as well as the underlying strength and stability of asset management businesses. Regulators are generally concerned with eliminating fraud and preventing systemic risk to the market. Institutional investors are concerned about meeting their fiduciary duties to their pension plans, endowments, or other asset pools. While investment performance is still important, there is increased concern and scrutiny on asset management businesses relating to risk, including investment and operational risk. There are concerns that asset management businesses may have grown too quickly to effectively manage their businesses, placing the assets under management at risk. Asset management businesses are now being evaluated across several dimensions that do not exist for most businesses in general.  
      While there is a desire to evaluate asset management businesses from an enterprise risk perspective and across operational performance dimensions, it has not been possible due to the shortcomings and limitations of conventional approaches to measuring and managing risk. As a result, asset management businesses have scant and fragmented information about their business risk, particularly information related to performance and potential.  
      To evaluate the enterprise risk and/or operational risk of an asset management business, prior art methods use loss event narrative summaries as source data, usually excerpted from loss event statistics purchased from third party sources in industries other than asset management (primarily the banking industry). This information summarizes past loss events in an effort to raise awareness and to document the type, frequency and magnitude of loss events. While instructive in understanding what happened and the resulting consequences, this approach does not lend itself to pro-active loss prevention, thus calling into question the efficacy of traditional source data and enterprise risk methods.  
      In the asset management industry, the data, information and systems available to manage investment portfolios and risk are highly sophisticated and comprehensive, however, there is little in the way of data, information or systems to manage investment businesses and risk. Prior art methods use portfolio performance data as the measure of how well an asset management business is performing and make no attempt to access or employ business operating data. This results in a lack of understanding about the business supporting the investment portfolio and, therefore, the risk of the asset management firm.  
      Lacking data, information and systems, the various constituencies of asset management businesses operate with an isolated view and do not have the means to understand the interdependencies across business lines, nor the impact of those interdependencies on the business. In addition, they do not have a quantitative framework to evaluate the business or its risk exposures as a whole. Thus, there is no enterprise, or “big picture,” view to work from either to measure business performance or to root out operational problems or inefficiencies. Further, there is no effective way to measure business risk or identify and address risk exposure issues.  
      To understand the ability of a asset management business to perform well in the future, prior art methods rely on past performance history despite the conventional wisdom that past performance is not indicative of future results. What&#39;s more, this data concerns only investment results and not the people, processes and technology having generated the results. Consequently, this approach does not provide a forward-looking perspective and provides little insight to understanding an asset management business.  
      An asset management business may use third party service providers to perform functions that may previously have been performed by the business. These activities may include fund administration, pricing of securities, a measurement of investment risk and performance. Prior art approaches do not incorporate the functions of third party service providers into the evaluation of the asset management business.  
      An asset management business is best analyzed by its business processes. Understanding the business processes of the individual functions and activities within an asset management business can enhance the understanding of the asset management business. This is important for individuals and organizations that select and oversee asset managers in the context of performing their fiduciary responsibilities. Prior art approaches do not operate in this manner.  
      Many constituencies seek data to understand the strength and risk levels of an asset management firm. These include auditors that seek data on which to base management opinions, and credit providers, that seek to understand the stability of an asset management firm and the potential risks of default on a loan, among others.  
      Institutional investors and their intermediaries (consultants and funds-of-funds) resort to issuing long due diligence questionnaires to potential and existing managers that they hire. These questionnaires are generally provided in typed documents, and asset managers either type responses or write in the responses in long hand. The primary limitation of due diligence questionnaires relates to the self-assessment nature of the majority of the questions. As a result, the information supplied does not lend itself well to verification. Additionally, managers respond to the questionnaires on an ad hoc basis and typically delegate their completion to non-investment personnel outside of the business areas being examined.  
     SUMMARY OF THE INVENTION  
      The present invention provides additional methods for evaluating an asset management business, such as but not limited to an asset management business or hedge fund, by using experiential data, i.e., data produced in the course of operating the business, to measure business risk, performance, and potential (other methods are described in U.S. patent application Ser. No. 11/005,119, entitled “METHOD FOR EVALUATING AN ASSET MANAGEMENT BUSINESS USING EXPERIENTIAL DATA,” filed on Dec. 6, 2004, referenced above). The methods include (1) the identification of previously untapped data from various disparate computerized systems supporting asset management businesses and (2) automating the collection of this experiential data for the evaluation of components of an asset management business (such as a traditional asset manager, mutual fund, or hedge fund) by using the experiential data, i.e., data produced in the course of operating the business, to measure business risk, performance, and potential. The business is broken down into functions, each function being carried out through a number of activities (i.e., business processes). Each activity produces experiential data, i.e., data produced by performing the activity. Experiential data includes both qualitative and quantitative information compiled from operating systems, databases, applications, workflows, interviews, paper-based files and financial records. Activities and functions are measured individually and then collectively to understand the business as a whole. Component measures compute risk levels, confidence levels, efficiency ratings, and operational effectiveness levels.  
      A specific set of mathematical functions, referred to as metrics and algorithms, are applied to the collected experiential data to measure enterprise risk, performance, and potential related to component parts of the asset management business. The measures generated are expressed as values, Key Business Indicators (KBIs), Operational Performance Benchmarks (OPBs), enterprise risk, business drivers and consistency potential. These measures provide an asset management business quantitative framework for each function, activity, and the business (or enterprise) as a whole.  
      The inventive method measures the potential of an asset management business to perform consistently over time by assessing how effective the business processes are operating by using experiential data extracted from the full spectrum of the business&#39; functions and activities. The extracted data is fed into a series of metrics and algorithms that measure how well an asset management business is performing from a business perspective. The information generated by the metrics is combined with a comparison of the business process workflows to applicable quality control checks, all of which is then ultimately combined, or rolled up, to assess enterprise risk, performance, and potential.  
      Two levels of analysis and perspective are provided, one at the functional level of the business (i.e., how well are the individual business processes performing) and the other, at the enterprise level (i.e., rolling up the business functions and activities to understand the business as a whole). In this way, the present invention provides 1) an understanding of the interdependency of the individual business functions and their impact on the business as a whole, and 2) the ability to quantify the effect and impact those interdependent relationships have on the business as a whole.  
      In addition, the present invention may be applied to examine specific aspects of an asset management business, and thus hone in on a particular area of concern. For example, the inventive method can be used to measure:  
      the level of operational risk;  
      the likelihood the business will perform consistently;  
      the effectiveness of the decision-making process;  
      alpha generation (growth in excess of market appreciation);  
      the short and long-term scalability of the business infrastructure;  
      the appropriateness of policies and procedures;  
      the adequacy of oversight and controls;  
      whether the business is being run responsibly; and  
      current practices as compared to quality control checks.  
      The method enables asset management businesses to utilize their own operational information to more effectively manage their businesses. Instead of having to rely on anecdotal information, they are able to manage their businesses as effectively as they manage their portfolios. The method also allows asset management businesses to provide quantitative information about their businesses to the financial institutions employing them as a supplement to traditional investment results and qualitative survey information.  
      In further detail, the method comprises the steps of gathering data across the activities of the business and applying a set of metrics to the data to produce measures to determine how well the business processes are performing as expressed in values and other measures such as key business indicators; comparing the business processes (activities) workflows to best practices (i.e., applicable quality control checks) to identify risk exposures; rolling up the activity measures for each function to understand how well the functions are performing as expressed in values and other measures such as key business indicators; combining all of the data generated to compute operational performance measures as expressed in terms of the business drivers (productivity, profitability, scalability, alpha generation and risk); combining all of the data generated to calculate enterprise risk and potential as expressed as a value.  
      In embodiments of the invention, the functions described herein are performed automatically using one or more computers. In other embodiments, some manual intervention is involved in some of the functions described herein. Implementation of these embodiments via software and hardware will be apparent to persons skilled in the art based on the teachings contained herein.  
      These and other advantages and features will become readily apparent in view of the following detailed description of the invention. Note that the Summary and Abstract sections may set forth one or more, but not all exemplary embodiments of the present invention as contemplated by the inventor(s). 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
      The accompanying drawings, which are incorporated herein and form a part of the specification, illustrate the present invention and, together with the description, further serve to explain the principles of the invention and to enable a person skilled in the pertinent art to make and use the invention.  
       FIG. 1  illustrates the broad steps of the method in accordance with an embodiment of the present invention.  
       FIG. 2  illustrates an exemplary set of metrics, KBIs and their corresponding Business Drivers for the trade capture activity (process) in accordance with the method of the present invention.  
       FIG. 3  is a flow chart illustrating the steps of the method in accordance with an embodiment of the present invention.  
       FIG. 4  is an exemplary set of experiential data for a trade capture activity in accordance with an embodiment of the present invention.  
       FIG. 5  is a mapped workflow of the trade capture process according to an embodiment of the present invention.  
       FIG. 6  is a comparison of the mapped workflow from  FIG. 5  with applicable quality control checks (also called industry best practices) according to an embodiment of the present invention.  
       FIGS. 7A-7G  are data flow diagrams illustrating the operation of an embodiment of the invention.  
       FIG. 8  illustrates the configuration of measures according to an embodiment of the invention.  
       FIG. 9  illustrates the configuration of activity KBIs according to an embodiment of the invention.  
       FIG. 10  is a data flow diagram illustrating the Operation Function and its associated activities according to an embodiment of the invention.  
       FIG. 11  illustrates an example method for the analysis of the trade processing activity of the Operation Function of an asset management firm.  
       FIG. 12  illustrates an example method for the analysis of the corporate action processing activity of the Operation Function of an asset management firm.  
       FIG. 13  illustrates an example method for the analysis of the pricing of the Treasury Function of an asset management firm.  
       FIG. 14  illustrates an example method for the analysis of the data management activity of the Information Technology Function of an asset management firm.  
       FIG. 15  is an example computer system used to implement embodiments of the present invention.  
       FIG. 16  is an asset management evaluation system according to an embodiment of the invention. 
    
    
      The present invention will now be described with reference to the accompanying drawings. In the drawings, like reference numbers can indicate identical or functionally similar elements. Additionally, the left-most digit(s) of a reference number may identify the drawing in which the reference number first appears.  
     DETAILED DESCRIPTION  
      1. Operational Description: Overview  
      The present invention provides a method whereby the experiential data of an asset management business is used to measure enterprise risk, performance, and the potential of the business to perform consistently over time. To do so, the method uses the experiential data of the business to fuel specific, predetermined mathematical functions, or metrics and algorithms, to measure the business in terms of the specific drivers of an asset management business: productivity, scalability, profitability, alpha generation and enterprise risk.  
      The first step is gathering data. The data is compiled from the business processes supporting the functions and activities of the business being evaluated. Businesses are often thought of in terms of departments, however, the inventive method organizes an asset management business by function and activity for greater specificity. Within each function is a sub-set of activities that make up the function. This is illustrated in the example data flow diagram of  FIGS. 7A-7G . In particular,  FIG. 7A  illustrates an example business  702 . The business  702  is organized according to the functions that it performs. These functions are illustrated as functions  704  (specified, for example, in Table 1). Each function  704  includes a number of activities  706 . For ease of illustration, only the activities  706 A- 706 N for function  704 A are shown in  FIG. 7A . An exemplary set of functions and their associated activities are listed in Table 1.  
               TABLE 1                          Business Organization by Function and Activity.                             Function   Activities           (such as functions   (such as activities           704 in  FIG. 7A )   706 in  FIG. 7A )                       Research   Idea generation               Implementation strategy           Portfolio Management   Investment due diligence               Strategy &amp; execution               Investment risk management           Sales   Market research               New business               Prospective development           Client Service   Communication               Retention           Management/General   Alpha generation           Partners   Business strategy &amp; execution               Compensation               Governance/ownership           Treasury   Cash management               Human resources               Margin/financing               Securities lending           Compliance   Business risk management               External compliance               Internal oversight           Controller   Audit/tax               Corporate finance               Portfolio (Partnership) accounting &amp;               reconciliation               Pricing           Operations   Corporate actions               Portfolio recordkeeping               Proxy voting               Trade capture               Trade error resolution               Trade settlement           Information Technology   Business continuity               Data management               Security               System administration               System development               Web presence                      
 
       FIG. 1  illustrates the major, broad steps and information involved with an embodiment of the inventive method, working from the bottom of the diagram to the top. The left column  100  represents a broad description of the general steps for the present invention. The middle column  200  represents the data associated with each step at its respective horizontal position. For example, the Extraction step (in the left column  100 ) is on the same line as “Summary Data,” “Workflows,” “Operating Data,” and Transaction Data.” The step of Extraction produces the data groups listed next to it. The column  300  on the right is a list of the libraries, or databases in which information is organized and maintained. The operation depicted in  FIG. 1  shall be described with reference to the example data flow diagram of  FIGS. 7A-7G .  
      As previously mentioned, the first step is to collect source (or experiential) data (as noted at the bottom of the left column  100 ). The systems and areas of an asset management business generating the source data are listed next to “Source Data”. Above “Source Data” is “Extraction” with the extraction results listed next to it. Referring to the example of  FIG. 7A , source data (or experiential data  708 ), is generated when activities  706  corresponding to the functions  704  of the business  702  are performed. Such experiential data  708  is collected in this first step.  
      In the next (“Business Functions”) step, metrics are applied to source data  200  to measure the performance of the functions and activities supporting the business. These measures, or criteria, may be expressed as values or as key business indicators (KBIs).  
      For example, the Research Function of  FIG. 1  includes two activities, Idea Generation and Implementation Strategy (as detailed in Table 1). In other words, the Research function generates ideas and formulates strategies for implementing those ideas. The Research function&#39;s business processes are scrutinized and subjected to specific statistical and mathematical analysis represented by a set of metrics designed to evaluate the specific activities of the Research function.  
      Assume a set of metrics for the Research function measures the productivity of the activities. For example, to measure the productivity of “Idea Generation,” the frequency with which new ideas are generated, the quality of the ideas, and whether they are generated on a timely basis, would all be considered. Each of these criteria can be measured by metrics. Metrics may also be a certain statistical analysis, for example, the percentage of ideas generated that increase investment results. The metrics for “Implementation Strategy” might then include the frequency with which strategies are formulated, the quality of the strategy, and whether they are implemented on a timely basis.  
      These metrics establish a baseline of current operational performance that comprises the foundation to understanding how well the business is performing. In this example, the metrics are designed to gauge how well the people, processes and technology involved in the activities within the Research function are performing. By using the experiential data of the business activities to establish a baseline of performance, the business owner can evaluate the effectiveness of the activities over time as well as measure the impact of business dynamics on the activities.  
      In the example of  FIG. 7A , such metrics and algorithms  710  are applied to the experiential data  708  to generate measures  713  (see  FIG. 7B ).  
      Next in the Business Functions step, Key Business Indicators (KBIs) are calculated for each activity to measure how well the specific activity is performing. For example, a KBI for Idea Generation might be “the number of new ideas generated in the period that were presented to the investment committee and given a green light for further analysis.” A KBI for the Implementation Strategy might be “the number of new ideas approved for implementation into the portfolio”. A KBI is a definitive, quantitative measure of each activity&#39;s performance. In the example of  FIG. 7B , such KBIs are shown as activity KBIs  714 . They are called “activity” KBIs  714  because each corresponds to an activity  706 , and each represents a key business indicator for that corresponding activity  706 . In  FIG. 7B , the activity KBIs  714  are shown as being generated by selective combination  715  of the measures  713 .  
      In the next step, to quantify and determine the performance of the Research function as a whole, the activities within the function are weighted by their importance to the function. That is to say, the Research function KBI score is calculated by averaging the weighted KBIs of Idea Generation and Implementation Strategy, the two activities that comprise the Research function. Referring to  FIGS. 7A and 7B , for example, note that the activity KBIs  714 A- 714 N all correspond to function  704 A. More particularly, the activity KBIs  714 A- 714 N shown in  FIG. 7B  all correspond to activities  706 A- 706 N that are part of function  704 A. In this step, the activity KBIs  714 A- 714 N for function  704 A are averaged and weighted  716  to produce function key business indicator(s)  720 A for function  704 A. In a similar manner, function KBIs  720 B- 720 N for the other functions  704 B- 704 N of the business  702  are generated. The KBIs  720  generated in this step are called “function” KBIs  720  because each corresponds to a function  704 , and each represents a key business indicator for that corresponding function  704 .  
      The analysis continues by shifting focus from the functional level of the business to the enterprise level. From the point of view of  FIGS. 7A and 7B , the analysis up to this point has focused on evaluating each function (such as function  704 A) independently of other functions  704  (such as functions  704 B- 704 N). The analysis now turns to evaluating the business  702  as a whole, which will involve (but not limited to) analysis of the functions  704  in combination. As part of the steps described above, the activity KBIs  714  for all the functions  704  are calculated in the same way as the example set out above for the Research Function. In the example of  FIG. 7B , the activity KBIs for functions  704 B- 704 N are collectively represented by activity KBIs  714 X.  
      The shift to the enterprise level begins by first noting that the metrics and KBIs are associated with business drivers. As discussed previously, and shown in  FIG. 2 , each metric and KBI is linked at the outset to one of five business drivers: productivity, scalability, profitability, risk or alpha generation. An example of a productivity metric has already been used in the discussion of Research activities and functions (“the number of new ideas generated in the period that were presented to the investment committee and were approved by the committee for further analysis.”). Additionally,  FIG. 2  provides more examples of activities metrics and their corresponding business drivers as will be discussed below.  
      In the next step, business process workflows of the activities  706  are analyzed to determine if they include applicable control checks. This is represented in the example of  FIG. 7C  by analysis modules  726  receiving as input activity work flows  723  and control checks  728 . The analysis modules  726  determine whether the activity work flows  723  include the control checks  728 . Prior to performance of this step, the business process workflows associated with the activities  706  are determined. Such workflows are part of the experiential data  708 . For example,  FIG. 5  illustrates an example workflow  500  for the trade capture activity, and includes the steps that are taken in capturing a trade in the business  702  being evaluated. Workflow  500  operates as follows. In steps  502  and  504 , the trades executed during the day are imported from the trading system into the portfolio accounting system. In step  506 , the head trader confirms the number of trades posted in the portfolio accounting system. For example, if 10 trades were executed by the trading system, then 10 trades should have been imported into the portfolio accounting system in step  504 . If there is a difference (step  508 ), then adjustments are made (steps  510 ,  512 , and  514 ).  
      Activity work flows  723  typically include a number of control checks  728 . A control check is a point in the work flow  723  where the accuracy of the performance of the work flow  723  is determined. In the example of  FIG. 5 , step  506  constitutes a control check  728 . There is a set of control checks  728  associated with each activity  706 . The control checks  728  for a given activity  706  depend on the nature of such activity  706 , and are thus implementation dependent.  
      Difference businesses may have different workflows  723  for a given activity  706 . Different workflows  723  for a given activity  706  may have different sets of control checks  728 . Omission of a control check  728 , however, represents a potential risk with the particular work flow  723 . Accordingly, in this step, the activity workflows  723  are analyzed  726  to determine if they have the applicable control checks  728 . The result of this analysis  726  are represented by control scores  730  (described in more detail below).  
      Example control checks  728  for the Idea Generation activity include: 1) documenting the inspiration source for the new idea; 2) documenting the source data used in formulating the new idea; and 3) dating, documenting and signing all the steps in the formulating of the new idea. Enhancements to, or deviations from, these control checks are scored. In this way, the inventive method provides a quantitative framework to easily identify and quantify performance contributors or detractors.  
      In the next step, the functions  704  are weighted by their importance to the business. These weightings are determined by a proprietary series of algorithms designed to account for the interdependence of the functions  704 . In the example embodiment of  FIGS. 7B and 7D , this step is achieved by averaging/weighting  734  the previously determined function KBIs  720  to generate function weighting(s)  736 .  
      In the next step, the collective information generated in the prior steps—metrics (represented by measures  713  in  FIG. 7B ), KBIs (activity KBIs  714  and function KBIs  720 ), control check scores  730  and function weightings  736 —is then used to calculate the productivity, scalability, profitability and alpha generation levels of the business  702 . These measures are intermediate calculations and referred to as the Operational Performance Benchmarks (OPBs) for the purposes of this explanation as they do not yet include an operational risk perspective. The OPBs provide an understanding of the interdependence of the business activities and functions and their relation to the business as a whole. This step is depicted in the example of  FIG. 7E , where productivity OPBs  740 A, scalability OPBs  740 B, profitability OPBs  740 C, and alpha generation OPBs  740 D are generated  738  from activity KBIs  714 , function KBIs  720 , control check scores  730  and/or function weightings  736 .  
      After the Operational Performance Benchmarks  740  have been computed, a risk assessment is performed. The risk assessment is based on a number of factors, typically those that create a risk. For example, people, processes, technology and external factors. These risk factors are detailed in Table 2.  
               TABLE 2                          Risk Factors.                     Risk Factors   Drivers               People   Appropriateness of skills &amp; experience           Adequacy of resources           Stability of staff           Commitment to ethics           Level of oversight       Processes   Effectiveness of control checks           Prevalence of manual processes           Awareness of risk exposures           Accuracy &amp; timeliness of data access, handling,           processing &amp; delivery           Separation of responsibilities, control checks &amp;           oversight           Clarity of policies and procedures       Technology   Reliability           Redundancy           Security           Contingency       External Factors   Awareness of external factors (physical           environment, counterparty, regulatory)           Preparedness to respond to external factors                  
 
      Risk assessment is accomplished by applying a risk assessment algorithm to the collective data to measure the operational risk of the activities, functions and the business as a whole. The KBIs and OPBs are then adjusted and weighted for operational risk resulting in final measures of productivity, scalability, profitability, alpha generation and operational risk for the business, or enterprise, as a whole.  FIG. 7F  illustrates an example data flow diagram of the risk assessment function.  FIG. 7F  is described in detail below.  
      The method culminates in computing a measure of the consistency potential of the business by factoring the pre-selected business drivers together, the process of which is described below. Staying with our previous example for consistency, there are five business drivers: productivity, profitability, scalability, alpha generation and enterprise risk.  FIG. 7G  illustrates an example data flow diagram illustrating how the consistency potential of the business is measured.  FIG. 7G  is described in detail below.  
      2. Operational Description: Detailed Description  
      Operation of embodiments of the invention shall now be described in greater detail with reference to  FIG. 3 , which is a flowchart illustrating the method according to a preferred embodiment of the present invention. The operation depicted in the embodiment of  FIG. 3  shall be described with reference to the example data flow diagram of  FIGS. 7A-7G . For further illustration, the inventive method is applied to the Trade Capture activity of the Operations function to provide a detailed example.  
      The first step is to collect relevant data (step  302 ). For this example, since the Trade Capture Activity is being evaluated, the relevant data includes the experiential data (data from experience) produced by the trade capture activity. This data may be obtained from interviews, operating systems, applications, workflows, databases, paper-based files and financial records.  
      An exemplary set of relevant experiential data is listed in  FIG. 4 . In this example, the experiential data includes operational data  400 , processes (mapped workflows)  410 , people (census information)  420  and technology systems  430 .  
      Referring to the example of  FIG. 7A , source data, or experiential data  708 , is generated when activities  706  corresponding to the functions  704  of the business  702  are performed. Such experiential data  708  is collected in step  302 .  
      Referring back to  FIG. 3 , once the relevant data is collected (step  302 ), a specific set of metrics is applied to the collected operating data (step  304 ). These metrics translate the raw operating data into measures that correspond to a specific driver. For this example, there are five business drivers: productivity, scalability, profitability, alpha generation and risk. The first three are familiar in the business world and self-explanatory. The fourth, Alpha Generation, is the contribution by the people of the asset management business in excess of industry standards. For example, the alpha generated by the portfolio managers of the asset management business could be defined as their contribution to financial growth or profit realized, in excess of market appreciation. So, if the relevant market grew by 10% and the asset management business realized a profit of 15%, its&#39; alpha contribution was 5%, i.e., the excess realized profit over market appreciation.  
      In this example, the inventive method uses five business drivers as the criteria by which the method measures a asset management organization. They are: productivity, scalability, profitability, alpha generation and enterprise risk. All metrics are linked to one of the business drivers.  
      For each activity, there is a specific, corresponding set of metrics to be applied to the activity&#39;s relevant experiential data. The number of metrics and the ones used will depend on the activity being evaluated. Each activity is measured by a set of metrics linked to an asset management business driver: productivity, scalability, profitability, alpha generation and enterprise risk.  
      In this example using trade capture productivity metrics for productivity, the metrics are applied to the relevant experiential data and scored using simple math as shown in Table 3. The calculation results of Table 3 are only examples and the values have been arbitrarily chosen.  
               TABLE 3                       Trade Capture Metrics for Productivity                                    Percent of trades captured on trade date = (trades captured on trade       date/total number of trades = 88%)       Percent of trades captured electronically = (trades captured electronically/       total number of trades captured = 87%)       Percent of trades captured error-free = (trades captured error-free/total       number of trades captured = 82%)                  
 
      Step  304 , as just described, is represented in the example of  FIG. 7A  by metrics and algorithms  710  being applied to the experiential data  708  to generate measures  713  (see  FIG. 7B ). As shown in  FIG. 8 , each measure  713  may include measures corresponding to the business drivers (see  FIG. 2 ). Thus, any given measure  713  may include productivity measure(s)  802 , scalability measure(s)  804 , profitability measure(s)  806 , alpha generation measure(s)  808  and/or risk measures  810 . For example, measures  713 A may include productivity measure(s)  802 , scalability measure(s)  804 , profitability measure(s)  806 , alpha generation measure(s)  808  and/or risk measures  810 , all of which are generated by metrics/algorithms  710 A from experiential data  708 A.  
      After the metrics are scored, an activity KBI  714  measuring trade capture productivity is calculated using, for example, simple math, such as a simple or weighted average (step  306 ). The activity KBI  714  is a predetermined measure of how well the particular activity  706  is being performed. The activity KBI  714 , like the set of metrics  710 , is different for every activity  706  and for each business driver. Thus, there are five different activity KBIs  714  for the Trade Capture  706  activity, one relating to each business driver. This is generally shown in  FIG. 9 . An exemplary set of KBIs for the Trade Capture Activity is listed in Table 4.  
               TABLE 4                          Trade Capture Activity KBIs.                         Activity KBI       Business Driver   (such as activity KBIs 714 in  FIG. 7B )               Productivity   Percent of trades captured on trade date, electronically           and error-free.       Profitability   Percent of maximum profitability target.       Scalability   Excess capacity.       Alpha   Percent of trade capture resources that add to the           firm&#39;s competitiveness.                  
 
      In this example, the Trade Capture Activity as it affects the driver Productivity, or, more simply put, trade capture productivity, is being evaluated. So the activity KBI corresponding to trade capture productivity is selected and applied to the metrics in Table 3 (step  306 ). In this example, for the Trade Capture Activity, the percentage of trades that are captured on trade date, electronically, and error free is the strongest indicator of operational performance of the Trade Capture Activity with respect to Productivity.  
      Step  306  shall now be further described. Values  713  obtained by the metrics  710  can be combined to generate new information  714 . The combination can be via a simple average or a weighted average, for example. Weights are implementation dependent and can be set, for example, based on the relative impact of the factors on the business, functions and/or activities (this is generally the case for all weights described herein). In the present example, looking at the values obtained by the metrics, the percentage of trades captured on the trade date, 88%, the trades captured electronically, 87%, and trades captured error-free, 82%, are extracted and their average obtained. In this case, the average of the three metrics is 85.6% or 86%. So, 86% (out of a possible 100%) of the trades captured are done so electronically, on the trade date and error-free. This value serves as the indicator of productivity for the Trade Capture Activity.  
      As indicated above, step  306  operates to calculate, for the Trade Capture activity, activity KBIs  714  for the three other business drivers as well, following the same steps as described above for the trade capture productivity KBI (see again  FIG. 9 ).  FIG. 2  lists the Productivity Metrics  200 , the Profitability Metrics  210 , the Scalability Metrics  220 , the Alpha Generation Metrics  230 , and the Risk Metrics  240  along with their respective KBIs  250 . Averages (simple or weighted) and percentages are obtained in the ordinary manner as is commonly known and practiced in the field of mathematics.  
      Step  306  is represented in the example of  FIG. 7B  by measures  713  being combined  715  to generate activity KBIs  714 . Step  306  could alternatively be viewed as combinations of metrics  710  being selected and applied to experiential data  708  to generate activity KBIs  714 , without the intermediate step of generating measures  713 .  
      Steps  302 ,  304  and  306  operate to generate measures  713  (in the form shown in  FIG. 8 ) for all the activities  706  of all the functions  704 , in the manner described above. Also, steps  302 ,  304  and  306  operate to generate activity KBIs  714  (in the form shown in  FIG. 9 ) for all the activities  706  of all the functions  704 , in the manner described above. Reference number  714 X represents activity KBIs for the other functions  704 B- 704 N.  
      In step  310 , function KBIs  720  are calculated for each function  704 , by taking a simple or weighted average of the activity KBIs  714  associated with that function  704 . For example, the Trade Capture activity KBI, along with the five other activity KBIs  714  within the Operations Function listed in Table 1, are averaged by their productivity activity KBIs  902 , which is selected as the Primary Business Driver of the Operations Function to provide a quantified analysis of the Operations Function, also called the function KBI  720  or Performance Rating  312 .  
      An exemplary set of Primary Business Drivers for each function  704  of an asset management business  702  is detailed in Table 5. The Primary Business Driver for each Function  704  is the Driver that is most effected by the Function  704 . So, for example, Sales affects Profitability more than the other Drivers.  
               TABLE 5                          Primary Business Drivers.                             Function 704   Primary Business Driver                       Research   Alpha Generation           Portfolio Management   Alpha Generation           GP/Management   Alpha Generation           Client Service   Profitability           Sales   Profitability           Treasury   Profitability           Compliance   Operational Risk           Controller   Operational Risk           Operations   Productivity           IT   Scalability                      
 
      Operation of step  310  is represented in the example of  FIG. 7B  by the generation of function KBIs  720 . Function KBIs  720  are generated for each function  704 . For example, in this step  310 , the activity KBIs  714 A- 714 N for function  704 A are averaged and weighted  716  to produce function key business indicator(s)  720 A for function  704 A. In a similar manner, function KBIs  720 B- 720 N for the other functions  704 B- 704 N of the business  702  are generated using their respective activity KBIs  714 X.  
      To summarize so far, the measures  713 , activity KBIs  714  and function KBIs  720  represent quantitative measures of how well the activities  706  and functions  704  of the business  702  are performing.  
      According to embodiments of the invention, the focus shifts to overall business, or enterprise, performance. The inventive method begins this next phase by utilizing the activity workflows  723  gathered as experiential data ( 410  of  FIG. 4 ). The trade capture activity workflow  500  is illustrated in the example of trade capture in  FIG. 5 , generally indicated by reference numeral  500 . The mapped workflow  500  is a flowchart for the steps that are taken in capturing a trade in the business being evaluated. The mapped workflow identifies the steps in the trade capture process. The workflow includes internal control checks (see Table 6, for example), which the invention in step  314  (below) compares to control checks  728  applicable to each activity  706 .  
      The mapped workflow  723  of each activity  706  is compared to the applicable set of control checks  728  (step  314 ). Table 6 lists example control checks  728  for the trade capture activity.  
               TABLE 6                       Industry Best Practices (Quality Control Checks) for Trade Capture.                                            Verification of number of trades (inbound)           Verification of number of trades (outbound)           Holdings check before trade capture           Authorization check before trade capture           Use of standard trade format           Time stamp (inbound)           Time stamp (outbound)           Assigned batch numbers to processed trades                      
 
      A comparison between the mapped workflow  500  and the control checks  728  is made. The comparison between the Trade Capture Activity workflow and the control checks  728  is shown graphically in  FIG. 6 .  
      In the comparison process, enhancements to and deviations from the control checks  728  are generated by automated workflow comparison software technology. In the example of  FIG. 6 , the deviations from the control checks  728  include (1) executing trades manually, (2) not holding checks before a trade capture, and (3) not authorizing a check before trade capture  602 . A link  606  shows where in the evaluated process  500  the deviation occurs. Another deviation occurs when the number of outbound trades is not verified  604 . A corresponding link  608  indicates where, in the process, the number of outbound trades should be verified.  
      The impact of each enhancement and/or deviation is linked to one of the five business drivers: productivity, scalability, profitability, alpha generation or enterprise risk. For example, variances between the Trade Capture Activity workflow and applicable control checks  728  are shown in Table 7. An “X” indicates that the variance has an impact on the particular driver.  
               TABLE 7                          Variances From Industry Best Practices (Quality Control Checks)                                         Productivity   Scalability   Profitability   Alpha    Risk       Variance   Impact   Impact   Impact   Impact   Impact               Manual trade   X   X   X       X       verification       No holdings                   X       check before       trade capture       No                   X       authorization       check before       trade capture       No outbound                   X       trade       verification                  
 
      The variances ( 602  and  604  in  FIG. 6 ) found in the comparison of the mapped workflow activity to control checks  728  are then scored beginning with an impressed base score of 50.  
      Each variance has a pre-determined weight depending on the business driver impacted by the variance. The pre-determined weights are configured prior to the analysis either by (1) accepting default settings established by the author of the analytical application, or (2) allowing the user of the application to set customized weights. In some embodiments, the control checks  728  represent industry best practices. Variances that raise best practice standards are scored positively, variances that deviate from industry best practices are scored negatively. Example best practice variance scores are shown in Table 8.  
               TABLE 8                          Best Practice Assigned Variance Scores.                             Business Driver   Assigned Variance Score                                         Alpha Generation   2.0           Operational Risk   1.5           Scalabilty   1.0           Productivity   .5           Profitability   .25                      
 
      To illustrate the scoring of an activity workflow comparison to industry best practices, an example of the scored trade capture activity comparison to applicable control checks  728  is shown in Table 9.  
               TABLE 9                          Trade Capture Comparison                                         Productivity   Scalability   Profitability   Alpha   Risk       Variance   Impact   Impact   Impact   Impact   Impact               Manual trade   −0.5   −1.0   −.25       −1.5       verification       No holdings                   −1.5       check before       trade capture       No                   −1.5       authorization       check before       trade capture       No outbound                   −1.5       trade       verification                  
 
      To compute the score  730  for the trade capture activity comparison to industry best practices, or the Trade Capture Activity Control Check Score  730 , the impressed base score of 50 is reduced by the sum of the scored variances. In this example, the sum of the variances is −7.75, resulting in a score of 42.25.  
      Step  314  is represented in the example workflow of  FIG. 7C , where activity workflows  723  are compared to applicable control checks  728  to generate control scores  730 .  
      Following the analysis of step  314 , the functions  704  are then weighted vis-à-vis their importance to the business  702  using an algorithm designed to account for the interdependence of the functions  704  (step  316 ). In the example embodiment of  FIGS. 7B and 7D , this step  316  is achieved by averaging/weighting  734  the previously determined function KBIs  720  to generate function weighting(s)  736 . Different functions as detailed in Table 1 have a different importance to the business based on their relationship to drivers  740 . Every function  704  impacts productivity  740 A, scalability  740 B, profitability  740 C, and alpha  740 D. However, in embodiments, because alpha is considered to be more important than profitability in evaluating an asset management business, functions  704  that are most important to alpha  740 D have a higher weighting than functions  704  that are most important to profitability  740 C.  
      Following step  316 , the collective information generated in the prior steps—metrics (represented by measures  713  in  FIG. 7B ), KBIs (activity KBIs  714  and function KBIs  720 ), process workflow control check comparisons (control check scores  730 ) and function weightings  736 —is then used to calculate the actual productivity, scalability, profitability and alpha generation levels of the business (step  318 ). These measures are intermediate calculations and expressed as the Operational Performance Benchmarks (OPBs) as they do not yet include an operational risk perspective. The OPBs provide an understanding of the interdependence of the business activities and functions and their relation to the business as a whole. This step  318  is depicted in the example of  FIG. 7E , where productivity OPBs  740 A, scalability OPBs  740 B, profitability OPBs  740 C, and alpha generation OPBs  740 D are generated  738  from activity KBIs  714 , function KBIs  720 , control check scores  730  and/or function weightings  736 . Different functions as detailed in Table 1 have a different importance to the business based on their relationship to drivers  740 . Every function  704  impacts productivity  740 A, scalability  740 B, profitability  740 C, and alpha  740 D. However, in embodiments, because alpha is considered to be more important than profitability in evaluating an asset management business, functions  704  that are most important to alpha  740 D have a higher weighting than functions  704  that are most important to profitability  740 C.  
      After the Operational Performance Benchmarks have been computed, a risk assessment is performed (step  320 ). For this example, the risk assessment is based on four risk factors: people, processes, technology and external factors. Examples of risk factors are detailed in Table 10.  
               TABLE 10                          Risk Factors.                     Risk Factors           (such as 754 in         FIG. 7F )   Drivers               People   Appropriateness of skills &amp; experience           Adequacy of resources           Stability of staff           Commitment to ethics           Level of oversight       Processes   Effectiveness of control checks           Prevalence of manual processes           Awareness of risk exposures           Accuracy &amp; timeliness of data access, handling,           processing &amp; delivery           Separation of responsibilities, control checks &amp;           oversight           Clarity of policies and procedures       Technology   Reliability           Redundancy           Security           Contingency       External Factors   Awareness of external factors (physical           environment, counterparty, regulatory)           Preparedness to respond to external factors                  
 
      Referring to the example data flow diagram of  FIG. 7F , the risk assessment is accomplished by applying a risk assessment algorithm  752  to the collective data (metrics (represented by measures  713  in  FIG. 7B ), KBIs (activity KBIs  714  and function KBIs  720 ), best practice comparisons (control check scores  730 ), function weightings  736  and Operational Performance Benchmarks  740 ) as it effects the drivers for each Risk Factor. Such collective data is assigned reference number  750  in  FIG. 7F . The risk assessment algorithm  752  includes risk metrics for each of the factors  754  in Table 10. That is, each Driver in Table 10 is assigned a grade  756 , or score from 1-100 indicating how well the driver is performing. Such grade/score  756  is determined by applying that part of collective data  750  relevant to a given driver to a metric or algorithm applicable to that driver (similar in concept to metrics  710 ). For example, the Technology Risk Factor may have a 90 in Reliability, 80 in Redundancy, 67 in Security and 75 in Contingency. In this example these are the Risk Driver Ratings  756  for the Technology risk factor, and the Security driver is creating the highest risk for this risk factor.  
      The weighted average (see functional block  757  in  FIG. 7F ) of the Risk Driver Ratings  756  for each Risk Factor  754  is calculated to obtain the Risk Factor Weightings  758 . The weighted average (see functional block  760 ) of the Risk Factor Weightings  758  is then calculated to obtain an overall Risk Assessment  762 . Different Risk Factors as detailed in Table 10 have a different importance to the business based on their relationship to functions  704 . For instance, people risk is more significant to skill based functions such as research and portfolio management and less significant in functions such as information technology and operations. Where a Risk Factor is more significant, it will have a higher weighting in the average functional block  760 .  
      The inventive method calculates the enterprise risk level  324  of the business using an enterprise risk algorithm (similar to functional block  752 ) in the same fashion using the Risk Ratings for each Function (step  322 ). The enterprise risk score  324  measures the level of risk in the business  702  by using metrics to link Risk Factor Weighting  758  against business drivers. For example, a high level of people risk would have a higher impact on a business driver depending heavily on human skill (such as Alpha Generation  808 ), whereas a high level of technology risk would have a higher impact on a business driver depending heavily on technology (such as Productivity  802 ).  
      The inventive method culminates in computing a measure of the consistency potential of the business (step  328 ) by factoring the business drivers together using a consistency potential algorithm that measures the potential of the business to perform consistently in the future. Referring to the example data flow diagram in  FIG. 7G , the consistency potential algorithm  772  is a combined weighted average of any one or more of the Performance Ratings, Risk Ratings, KBIs and OPBs of each Function (represented in  FIG. 7G  as collective data  770 ) to obtain final measures of productivity, scalability, profitability, alpha generation and operational risk for the business, or enterprise, as a whole (step  326 ). For example, step  326  may be performed by taking a weighted average of those portions of the collective data  770  applicable to a given business driver (where the weights are assigned based on the importance of the data to the business driver), where that weighted average represents the overall score for that business driver. This is repeated for each business driver (i.e., productivity, scalability, profitability, alpha generation and operational risk). In step  328 , the assessment  330  of the consistency potential measures the potential of the business to perform consistently over time, thus providing a forward-looking perspective. For example, step  328  can be performed by taking a weighted average of the business driver scores (calculated in step  326 ), where the weights are based on the perceived importance of each business driver to the particular business  702 .  
      3. Operational Examples  
      For further illustration of activity metrics  710  and their corresponding activity KBIs  714 , as well as other processing of the invention, a number of exemplary metric sets and activity KBIs for specific activities are provided. Table 11 shows a simplified exemplary set of data with its corresponding metrics and KBI for Trade Error Resolution Activity, and is similar to the steps of  FIG. 3  for Trade Capture Activity. Once experiential data is gathered (step  302 —this step may be manual, automatic, or partially automatic), all of the subsequent steps in  FIG. 3  are performed by a computer.  
               TABLE 11                          Trade Error Resolution Activity.                         Step   Action   Example Data/Metrics/KBI/Logic               Collect Data   Compile experiential data on   Example Data 708:           activities   No. of trade errors               No. of trade errors remedied               electronically               No. of trade errors caused by               counterparty error       Apply Metrics   Establish baseline of current   Metrics 710:           operational performance, i.e., how   Avg. no. of daily trade errors           well people, processes and technology   Avg. time to remedy trade errors           are performing.   % of trade errors remedied               electronically       Generate KBI   Combine metrics using computed   Activity KBI 714:           ratios, averages and percentages to   % of trade errors remedied before           produce KBIs.   T + 2 electronically       Compute OPB   Compute productivity, scalability,   Weighting logic 738:           profitability, and alpha generation by   Timely identification and quick           aggregating, factoring and weighting   remediation of trade errors is key           metrics, KBIs &amp; best comparison   to controlling costs and risk.           results.                  
 
      Table 12 shows a simplified exemplary set of data with its corresponding metrics and KBI for Pricing Activity.  
               TABLE 12                          Pricing Activity.                         Step   Action   Example Data/Metrics/KBI/Logic               Collect Data   Compile experiential data on   Example data 708:           activities   No. of securities with missing prices               No. of manually priced securities               No. of securities with price               overrides               No. of unsupervised and non-               priced securities       Apply Metrics   Establish baseline of current   Metrics 710:           operational performance, i.e.,   Avg. %. of securities priced daily           how well people, processes and   Avg. % of securities priced           technology are performing.   manually               Avg. % of unsupervised and non-               priced securities.               Avg. % of securities priced               manually       Generate KBI   Combine metrics using   Activity KBI 716:           computed ratios, averages and   % of securities with manual price           percentages to produce KBIs.   overrides       Compute OPB   Compute productivity,   Weighting logic 738:           scalability, profitability, and   As the frequency and number of           alpha generation by aggregating,   manually priced securities           factoring and weighting metrics,   increases, so do costs and risk.           KBIs &amp; best comparison results.                  
 
      Table 13 shows a simplified exemplary set of data with its corresponding metrics and KBI for Reconciliation Activity.  
               TABLE 13                          Reconciliation Activity                         Step   Action   Example Data/Metrics/KBI/Logic               Collect Data   Compile experiential data on activities   Example data 708:               Total no. of position breaks               No. of positions in portfolio               No. of position breaks found               through automated comparison       Apply Metrics   Establish baseline of current   Metrics 710:           operational performance, i.e.,   Avg. no. of position breaks           how well people, processes and   % of total no. of positions with breaks           technology are performing.   Avg. time to remedy position breaks               % of breaks identified electronically       Generate KBI   Combine metrics using computed ratios,   Activity KBI 714:           averages and percentages to produce KBIs.   % of position breaks remedied in               less than 2 days and via 1 notification       Compute OPB   Compute productivity,   Weighting logic 738:           scalability, profitability, and   Position breaks increase cost and risk           alpha generation by aggregating,           factoring and weighting metrics,           KBIs &amp; best comparison results.                  
 
      Table 14 shows a simplified exemplary set of data with its corresponding metrics and KBI for Proxy Voting Activity.  
               TABLE 14                          Proxy Voting Activity                         Step   Action   Example Data/Metrics/KBI/Logic               Collect Data   Compile experiential data on activities   Example data 708:               Total no. of votes to vote               No. of votes not voted               No. of votes voted manually               No. of votes archived       Apply Metrics   Establish baseline of current   Metrics 710:           operational performance, i.e., how   % of votes voted manually           well people, processes and   % of votes not voted           technology are performing.   % of vote overrides       Generate KBI   Combine metrics using computed   Activity KBI 714:           ratios, averages and percentages to   % of votes, voted error-free &amp; archived           produce KBIs.       Compute OPB   Compute productivity, scalability,   Weighting logic 738:           profitability, and alpha generation   Automation provides increased           by aggregating, factoring and   efficiency with lower cost and risk.           weighting metrics, KBIs &amp; best           comparison results.                  
 
      4. Example Components of an Asset Management Business  
      Example components of an asset management business according to embodiments of the invention are described in this section.  
      One inventive method relating to a component part of an asset management business measures the counterparty risk, counterparty effectiveness, settlement risk, and operational efficiency for the trade processing activity within the Operations Function of an asset management business and/or outsourced service provider. This method is represented, for example, in  FIG. 11 . Trade (or transaction) processing is part of the Operations business function as represented in Table 1. Measurements are created by assessing experiential data extracted from transaction processing systems such as trade order management systems, DTC systems, and portfolio accounting systems. The extracted data is fed into an application comprised of a series of metrics and algorithms that measure how well the component part of the asset management business is performing. The measurements provide levels of risk that particular type of security transactions will not settle. The measurements provide a quantitative framework and insights on the effectiveness of individual counterparties as well as measures of operational risk and efficiency within the trade processing function.  
      Two levels of analysis and perspective are provided, one at the functional level of the trade processing function (i.e., how well the activities are being performed) and the other, at the enterprise level (i.e., rolling up the various activity based measures) to understand the trade processing function overall and its risk exposure impact on the business. In this way, this embodiment of the present invention provides 1) an understanding of the interdependency of the various trade processing activities and their impact on the business as a whole, 2) the ability to quantify the effect and impact those interdependent relationships have on the business as a whole, and 3) the ability for an asset manager to detect and pinpoint the risk exposures within the trade processing function.  
      The method enables asset management businesses to utilize their own operational information to more effectively manage their businesses. Instead of having to rely on anecdotal information, they are able to manage their businesses as effectively as they manage their portfolios. The method also allows asset management businesses to provide quantitative information about their businesses to the financial institutions employing them as a supplement to traditional investment results and qualitative survey information.  
      To describe in further detail, the method for the trade processing function shown in  FIG. 11  (1) identifies relevant and previously not utilized data elements contained within various computer systems and databases for extraction ( 1101 ); (2) aggregates the data for analysis ( 1102 ); (3) applies metrics to the data to produce specific measures ( 1103  ( a - i )), and (4) combines the measures to evaluate risk, efficiency, and operational performance ( 1104  ( a - e )).  
      The method in  FIG. 11  relies on computers to compute metrics pertaining to trade processing activities. Counterparty settlement rate  1103   a  is computed by measuring the number of trades that settle as a percentage of all trades, and calculating the percentage for each counterparty that has traded with the asset management business over the defined period of time. Counterparty error rate  1103   b  is computed for each counterparty by measuring the number of transactions with errors as a percentage of total transactions. Counterparty correction time  1103   c  is computed for each counterparty by comparing the length of time it takes for an error in a transaction to be corrected. Exposure to counterparties  1103   d  is computed for each counterparty by calculating the number of trades that have been traded but not settled. System reliability  1103   e  is computed by calculating the percentage of days that transactions were processed on against total days available to process transactions. System overrides  1103   f  is computed by calculating the number of times a system administrator (such as a portfolio manager, operations manager, or information technology specialist) overrides previously installed controls within a portfolio accounting or trade order management system. Counterparty credit rating  1103   g  is computed by assigning third party (such as Standard &amp; Poor&#39;s or Moody&#39;s) credit ratings to each counterparty. Operational performance  1103   h  is computed by calculating the percentage of transactions that settle on time. Exposure to open positions  1103   i  is computed by calculating the number of trades that have been made but have not yet settled (open positions) as a percentage of overall transactions, and the understanding the dollar amount of those open positions relative to the overall investment portfolio.  
      The method in  FIG. 11  relies on computers to compute algorithms that incorporate previously calculated metrics. Transaction complexity weighting  1104   a  is calculated by assigning a score to individual securities based on the difficulty of settling a transaction. For instance, settling a trade of a common stock on the NASDAQ stock exchange is easier than settling an option traded on the CBOE. Settling an option on the CBOE is easier, however, than settling a privately negotiated interest-rate swap. The score can be assigned based on a user&#39;s understanding of security complexity or based on calculations of prior performance in settling securities. Counterparty confidence level  1104   b  is calculated by averaging (either as a straight average or weighted average) the results for counterparty settlement rate  1103   a,  counterparty error rate  1103   b,  transaction complexity weighting  1104   a,  counterparty correction time  1103   c,  and exposure to counterparties  1103   d.  Counterparty risk level  1104   d  is calculated by averaging the counterparty credit rating  1103   g  and the counterparty confidence level  1104   b.  System risk  1104   c  is calculated by averaging system reliability  1103   e  and system overrides  1103   f.    
      An overall operational risk level for transaction processing  1104   e  is calculated by averaging (either as a straight average or weighted average) the results for system risk  1104   c,  operational performance  1103   h,  counterparty risk level  1104   d,  and exposure to open positions  1103   i.    
      A second inventive method relating to a component part of an asset management business measures the risk, performance, and potential of the corporate action processing function of an asset management business and/or outsourced service provider. This method is represented in  FIG. 12 . Corporate actions processing is part of the Operations business function as represented in Table 1. Measurements are created by assessing experiential data extracted from the systems associated with corporate action processing, such as portfolio accounting systems. The extracted data is fed into an application comprised of a series of metrics and algorithms that measure how well the corporate action processing component part of the asset management business is performing. The measurements provide a quantitative framework and insights on the effectiveness and risk level associated with the corporate action processing activity.  
      Two levels of analysis and perspective are provided, one at the functional level of the corporate action processing function (i.e., how well the activities are being performed) and the other, at the enterprise level (i.e., rolling up the various activity based measures) to understand the trade processing function overall and its risk exposure impact on the business. In this way, this embodiment of the present invention provides 1) an understanding of the interdependency of the various corporate action processing activities and their impact on the business as a whole, 2) the ability to quantify the effect and impact those interdependent relationships have on the business as a whole, and 3) the ability for an asset manager to detect and pinpoint the risk exposures within the corporate action processing function.  
      To describe in further detail, the method for the corporate action processing function shown in  FIG. 12  (1) identifies relevant and previously not utilized data elements contained within various computer systems and databases for extraction ( 1201 ); (2) aggregates the data for analysis ( 1202 ); (3) applies metrics to the data to produce specific measures ( 1203   a - g ), and (4) combines the measures to evaluate risk, efficiency, and operational performance ( 1204   a - d ).  
      The method in  FIG. 12  relies on computers to compute metrics pertaining to corporate action processing activities. System reliability  1203   a  is computed by calculating the number of days the system processes corporate actions as a percentage of total days. System overrides  1203   b  are computed by calculating the number of times a system administrator or other professional overrides internal control checks. Timeliness of information  1203   c  is calculated by determining when information relating to corporate actions is received from counterparties. Accuracy of information  1203   d  is calculated by determining if information received from counterparties has been subsequently revised or corrected. Corporate action activity level  1203   e  is calculated by determining the number of corporate actions processed. Corporate action consistency  1203   f  is calculated by determining the ways corporate actions are incorporated into the investment portfolio. Operational performance  1203   g  is calculated by determining the number of error free corporate actions processed as a percentage of total corporate actions.  
      The method in  FIG. 12  relies on computers to compute algorithms that incorporate previously calculated metrics. System risk  1204   a  is calculated by averaging system reliability  1203   a  and system overrides  1203   b.  Counterparty risk  1204   b  level is computed for each counterparty by averaging (as a weighted average or simple average) timeliness of information  1203   c  and accuracy of information  1203   d.  Process complexity  1204   c  is computed by averaging (as a weighted average or simple average) corporate action activity level  1203   e  and corporate action consistency  1203   f.    
      An overall operational risk level for corporate action processing  1204   d  is calculated by averaging (either as a straight average or weighted average) the results for system risk  1204   a,  operational performance  1203   g,  counterparty risk level  1204   b,  and process complexity  1204   c.    
      A third inventive method of one component part of an asset management business measures the risk, performance, and potential of the security pricing function. This method is represented in  FIG. 13 . Security pricing is part of the Controller business function as represented in  FIG. 1 . Measurements are created by assessing experiential data extracted from the systems associated with security pricing, such as portfolio accounting systems. The extracted data is fed into an application comprised of a series of metrics and algorithms that measure how well the security pricing component part of the asset management business is performing. The measurements provide a quantitative framework and insights on the effectiveness and risk level associated with the security pricing activity.  
      Two levels of analysis and perspective are provided, one at the functional level of the security pricing function (i.e., how well the activities are being performed) and the other, at the enterprise level (i.e., rolling up the various activity based measures) to understand the security pricing function overall and its risk exposure impact on the business. In this way, this embodiment of the present invention provides 1) an understanding of the interdependency of the various security pricing activities and their impact on the business as a whole, 2) the ability to quantify the effect and impact those interdependent relationships have on the business as a whole, and 3) the ability for an asset manager to detect and pinpoint the risk exposures within the security pricing function.  
      To describe in further detail, the method for the security pricing function of  FIG. 13  (1) identifies relevant and previously not utilized data elements contained within various computer systems and databases for extraction ( 1301 ); (2) aggregates the data for analysis ( 1302 ); (3) applies metrics to the data to produce specific measures ( 1303   a - g ), and (4) combines the measures to evaluate risk, efficiency, and operational performance ( 1304   a - d ).  
      The method in  FIG. 13  relies on computers to compute metrics pertaining to pricing activities. System reliability  1303   a  is computed by determining the days pricing is performed relative to possible days pricing could be performed. System overrides  1303   b  is calculated by determining the number of instances an administrator or portfolio manager overrides the established pricing process for a security and inserts a new price for the security. Timeliness of information  1303   c  is calculated by determining when pricing data is received. Accuracy of information  1303   d  is calculated by determining if data is subsequently corrected or revised. Process complexity  1303   e  is calculated by determining how difficult a security is to price. This can be self-scored or determined based on historical difficulties in pricing securities (pricing a common stock is easy, while pricing a highly illiquid security is more difficult). Pricing complexity  1303   f  is calculated by determining the number of pricing elements that are involved in a security pricing. This can be self-scored or determined base do historical performance in pricing securities. Some securities are easy to price because they have a single market price on an exchange. Other illiquid fixed income securities, however, may have a dozen different data elements required to price the security. Operational performance  1303   g  is calculated by determining the level at which securities are priced error-free.  
      The method in  FIG. 13  relies on computers to compute algorithms that incorporate previously calculated metrics. System risk  1304   a  is computed by averaging system reliability  1303   a  and system overrides  1303   b.  Data risk  1304   b  is determined by averaging (as a weighted average or simple average) timeliness of information  1303   c  and accuracy of information  1303   d.  Process risk  1304   c  is calculated by averaging (as a weighted average or simple average) process complexity  1303   e  and price complexity  1303   f.    
      An overall operational risk level for pricing  1304   d  is calculated by averaging (either as a straight average or weighted average) the results for system risk  1304   a,  operational performance  1303   g,  data risk  1304   b,  and process risk  1304   c.    
      A fourth inventive method of one component part of an asset management business measures the risk, performance, and potential of the data management function of an asset management business and/or outsourced service provider. This method is represented in  FIG. 14 . Data management is part of the information technology business function in Table 1. Measurements are created by assessing experiential data extracted from the systems associated with data management, such as portfolio accounting systems. The extracted data is fed into an application comprised of a series of metrics and algorithms that measure how well the data management component part of the asset management business is performing. The measurements provide a quantitative framework and insights on the effectiveness and risk level associated with the data management activity.  
      Two levels of analysis and perspective are provided, one at the functional level of the data management function (i.e., how well the activities are being performed) and the other, at the enterprise level (i.e., rolling up the various activity based measures) to understand the data management function overall and its risk exposure impact on the business. In this way, this embodiment of the present invention provides 1) an understanding of the interdependency of the various data management activities and their impact on the business as a whole, 2) the ability to quantify the effect and impact those interdependent relationships have on the business as a whole, and 3) the ability for an asset manager to detect and pinpoint the risk exposures within the data management function.  
      To describe in further detail, the method for the data management function of  FIG. 14  (1) identifies relevant and previously not utilized data elements contained within various computer systems and databases for extraction ( 1401 ); (2) aggregates the data for analysis ( 1402 ); (3) applies metrics to the data to produce specific measures ( 1403   a - f ), and (4) combines the measures to evaluate risk, efficiency, and operational performance ( 1404   a - c ).  
      The method in  FIG. 14  relies on computers to compute metrics pertaining to data management activities. Timeliness of information  1403   a  is calculated by determining when information is received. Accuracy of information  1403   b  is calculated by determining if information received is subsequently revised or corrected. Data complexity  1403   c  is computed by determining the level of complexity in data received. This is self-scored or determined based on a comparison of the data received relative to other data. Model complexity  1403   d  is calculated by determining the data requirements of various computer systems in the firm (such as portfolio accounting systems, risk management systems, etc.). Operational performance  1403   f  is determined by determining the level at which data managed in the firm is without error. This can be determined over a period of time or as a percentage of overall data managed.  
      The method in  FIG. 14  relies on computers to compute algorithms that incorporate previously calculated metrics. Data risk  1404   a  is computed by averaging (as a weighted average or simple average) timeliness of information  1403   a  and accuracy of information  1403   b.  Model risk  1404   b  is calculated by averaging (as a weighted average or simple average) data complexity  1403   c  and model complexity  1403   d.    
      An overall operational risk level for data management  1404   c  is calculated by averaging (either as a straight average or weighted average) the results for system reliability  1403   e,  operational performance  1403   f,  data risk  1404   a,  and model risk  1404   b.    
      The methods described herein enable asset management businesses to utilize their own operational information to more effectively manage their businesses. Instead of having to rely on anecdotal information, they are able to manage their businesses as effectively as they manage their portfolios. The method also allows asset management businesses to provide quantitative information about their businesses to the financial institutions employing them as a supplement to traditional investment results and qualitative survey information.  
      The methods described herein enable institutional investors to quantitatively evaluate the business infrastructures and operational risk levels of the asset managers they employ or are considering for employment. The method also allows investors to quantitatively understand the stability and scalability of an asset management business. Further, financial institutions can understand an investment strategy within the context of the business supporting it rather than looking at the strategy in isolation.  
      The methods described herein enable government regulators to understand operational risk levels within asset management business. By understanding the risks within firms regulators can better understand how to minimize systemic risk within markets.  
      The methods described herein enable asset management industry participants and government regulators to better understand counterparty risk levels and their potential impact on systemic risk.  
      5. Structural Description of Embodiments of the Invention  
      Certain embodiments of the present invention are implemented using well known computers, such as a computer  1502  shown in  FIG. 15 . The computer  1502  can be any commercially available and well known computer capable of performing the functions described herein, such as computers available from IBM, Apple, Sun, HP, Dell, Compaq, etc.  
      The computer  1502  includes one or more processors (also called central processing units, or CPUs), such as a processor  1506 . The processor  1506  is connected to a communication bus  1504 . The computer  1502  also includes a main or primary memory  1508 , preferably random access memory (RAM). The primary memory  1508  has stored therein control logic  1528 A (also herein called computer software), and data.  
      The computer  1502  also includes one or more secondary storage devices  1510 . The secondary storage devices  1510  include, for example, a hard disk drive  1512  and/or a removable storage device or drive  1514 . The removable storage drive  1514  represents a DVD drive, a compact disk drive, a magnetic storage device, an optical storage device, tape backup, etc.  
      The removable storage drive  1514  interacts with a removable storage unit  1516  (in some embodiments the storage unit  1516  is not removable from the device  1514 ). As will be appreciated, the removable storage unit  1516  includes a computer usable or readable storage medium  1524  having stored therein computer software (control logic  1528 B) and/or data. The removable storage drive  1514  reads from and/or writes to the removable storage unit  1516  in a well known manner.  
      Removable storage unit  1516 , also called a program storage device or a computer program product, represents a floppy disk, magnetic tape, compact disk, DVD, optical storage disk, or any other computer data storage device. Program storage devices or computer program products also include any device in which computer programs can be stored, such as hard drives, ROM or memory cards, etc.  
      Control logic (or computer software)  1528 , when executed, enable the computer  1502  to perform the functions of embodiments of the present invention as described herein. In particular, the computer programs  1528 , when executed, enable the processor  1506  to perform the functions described herein. Accordingly, such computer programs  1528  represent controllers of the computer  1502 .  
      The computer  1502  also input/output/display devices  1522 , such as monitors, keyboards, pointing devices, etc.  
      The computer  1502  further includes a communication or network interface  1518 . The network interface  1518  enables the computer  1502  to communicate with remote devices over one or more communication mediums  1526 . For example, the network interface  1518  may allow the computer  1502  to communicate over communication networks, such as LANs, WANs, the Internet, etc. Communication mediums  1526  include wired or wireless mediums. Software  1528 C is transmitted over such communication mediums  1526 . The electrical/magnetic signals having contained therein computer programs  1528 C also represent computer program product(s).  
      The invention can work with software, hardware, and operating system implementations other than those described herein. Any software, hardware, and operating system implementations suitable for performing the functions described herein can be used.  
      In an embodiment, the present invention is directed to computer program products or program storage devices having software that enables the computer  1502  to perform any combination of the functions described herein.  
       FIG. 16  illustrates an asset management evaluation system  1602  according to an embodiment of the invention. The asset management evaluation system  1602  performs the functions described herein. In particular, the asset management evaluation system  1602  includes a functional level module  1604  that evaluates a business from an activity level and a functional level using experiential data of the business, as well as an enterprise level module  1606  that evaluates the business from an enterprise level, again using experiential data of the business, in accordance with the teachings contained herein. In embodiments, the management evaluation system  1602  is implemented using one or more computers, such as that shown in  FIG. 15 .  
      The present invention has been described above with the aid of functional building blocks illustrating the performance of specified functions and relationships thereof. The boundaries of these functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternate boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Any such alternate boundaries are thus within the scope and spirit of the claimed invention. One skilled in the art will recognize that these functional building blocks can be implemented by discrete components, application specific integrated circuits, processors executing appropriate software and the like and combinations thereof.  
      While various embodiments of the present invention have been described above, it should be understood that they have been presented by way of example only, and not limitation. Thus, the breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.