WO2007135683A2

WO2007135683A2 - Credit management system and method

Info

Publication number: WO2007135683A2
Application number: PCT/IL2007/000626
Authority: WO
Inventors: Igaal Brumer
Original assignee: Perspective D.S.S Ltd.
Priority date: 2006-05-23
Filing date: 2007-05-24
Publication date: 2007-11-29
Also published as: WO2007135683A3

Abstract

A credit management system and method is described. Data obtained from internal and external sources and are used to generate a database containing credit related data appertaining to at least one customer. A processor is configured to produce a set of n credit ratings based upon the credit related data and to analyze the credit ratings to produce a credit rating of n dimensions. The credit data is presented in data representations emphasizing those data most pertinent to the user.

Description

CREDIT MANAGEMENT SYSTEM AND METHOD

FIELD OF THE INVENTION

The present invention generally relates to a system and method for credit management of customers. More specifically this invention relates to a system for generating and visually representing multidimensional credit ratings based upon data gathered from a plurality of sources.

BACKGROUND OF THE INVENTION

Numerous organizations perform business transactions with other organizations or individuals involving credit, such organizations need effective systems to manage their credit. On the sale-side, efficient credit management is essential to collectors including organizations selling goods and services on credit to their customers, banks selling credit services to their customers, insurance companies selling insurance services to their customers, factoring companies collecting debt from customers who buy on credit, clearing companies collecting funds owed to their clients and other such organizations. On the purchase-side, credit management can be important to customers who may be interested in improving their own credit rating. Organizations that sell on credit or sell credit related services require effective credit management and control allowing them to optimise profits whilst minimising risk. Granting too much credit to a given risky customer may cause big losses if this customer defaults on his loans, while granting too little credit to a good and reliable customer may limit the amount of business performed with this customer and hurt the organization's financial performance. To this end it is necessary to quantify, monitor and adjust the credit risk of each customer or group of customers in order to assess the customer's ability to repay his loans thereby increasing the reliability of its cash-flow. Each customer has to be allocated suitable credit limits or credit terms so as to limit his credit risk. In addition, after credit is granted it is effective collection of the funds due is essential.

The current success rate seems statistically very high, with a global default rate presently ranging from about 0.5% to about 1.5%. Nevertheless these percentages can translate into around 25% of the organization's profits, for a large organization this might entail the loss of millions of dollars. Moreover maintenance of such a low default rate demands extremely intensive work that places economic strain upon the organisation. In addition evaluation of the default rate is notoriously difficult presenting additional management problems. As the number of customers increases problems associated with effective credit management also intensify. A customer pool containing a few hundred customers can become highly complex and difficult to manage. Increases in the number of transactions with each customer, accounts with each customer or any other variable associated with credit management, all increase the strain put upon any system designed to assist in the management of credit. This strain is noticeable particularly where the nature of the inter-business relationship is ongoing or continuous such as in the case of regular payments for example of business credit or the like.

To cope with the complexity of credit management organizations use a variety of methods. The credit management process involves a few aspects, including: credit risk analysis, survival analysis, credit granting, credit monitoring and control, credit- related policies, collection, recovery and more. Organizations often use systems to help them in credit management, and they may have several systems operating simultaneously to cover the various aspects of credit management.

One of the most crucial tasks in credit management is credit analysis that enables a credit rating to be established for each customer or group of customers. To get information about credit risk, organizations often purchase credit ratings for a subset of their customer base from outside sources including rating agencies, banks, credit insurance agencies, financial reports, publications and through industry gossip.

Another important source for credit information is internal organization sources, such as inter-business transactions between the selling and buying organisation, but also including the personnel in credit department and the collection department, who typically know the customers well.

The information gathered through the above mentioned sources, whether internal or external, has a few important limitations. The information is typically slow-reacting, being based on data that changes slowly, like corporate financial reports. It is usually in a fixed format, meant to be used by a wide range of users, and not adapted, modified or calibrated to the needs of the specific organization or the specific user. In particular, knowledge gathered through such sources is not generally normalized.

It is possible to learn a great deal about the customer through inter-business data gathered within the organization, such as patterns of purchase, payments, returning merchandize and more. Furthermore, in many cases it may be possible to gauge some aspects of the credit risk of the customer by inspecting his inter-business activities with the organization, such as per transaction or per day.

Systems have been developed for rating scoring based upon internal customer information. Such systems inspect the customer activity over time, and use various parameters to predict the customer's behaviour into the future. A "credit score" is generated for each customer reflecting in some way the customer's credit risk. Typically, systems of this type are used to predict the likelihood of collection from a given customer, and estimate the likelihood of recovery from a customer who already has a collection problem. A credit manager can be faced with a number of credit rating scores gathered from a variety of sources. None of these is necessarily tailored to the needs of that specific organization nevertheless these are the indicators used when deciding on a particular credit limit for a given customer. The way in which such information is presented for use by the credit manager is typically in the form of tables and spreadsheets which display historical data with equal weighting.

The presentation of important information in this manner is problematic as the very information required by the credit manager may be lost within the background noise of less important information. There is no easy way for a credit manager to distinguish the most relevant information from the less relevant information. When faced with all this data an inexperienced credit manager, or a credit manager who is presented with data from a particular customer for the first time, will therefore spend much time trying to organise what data is most relevant. It is economically unviable in many cases for a credit manager to invest a great deal of time on each customer. As a result experienced credit managers learn to take short cuts, they know what information is typically relevant and therefore tend to concentrate only on this information whilst ignoring data which is not as relevant. Whilst this allows the experienced credit manager to analyse a significantly larger amount of data in a given time, there is an intrinsic risk associated with discounting data available even where that data is considered less relevant.

Systems to help the credit manager are known such as that described in the international patent application number WO 0,048,053 which discloses a commercial transaction management system that suggests supplementing a standard external credit rating with a credit score based upon inter-business transactions. Nevertheless, subjective decisions are based upon not only the credit scores but also the credit manager's own experience and judgement. Such lack of objectivity is inherent in any system which processes multiple variables without an integrated approach. The problems associated with this subjective judgement approach become more significant when dealing with large organizations with large numbers of clients or large credit management teams. The lack of objective standards creates a number of difficulties for example it becomes problematic to transfer the management of a particular group of customers between different credit managers. Automation of the credit rating procedure is also made much more difficult without any objective standards by which to compare customers or their recorded characteristics.

Credit rating systems do exist which combine various credit rating scores using weighted averaging so as to produce a single compound credit rating score. So for example, international patent application number WO 2004 107,117, presents a rating system for identifying desirable customers that produces a prediction index calculated by combining a value with a biasing weight for each of a plurality of scores representing customer data. Similarly United States patent application number 2002/059,283 presents a method for managing customer relations which produces an overall customer value from an aggregate of values representing responses to a customer questionnaire.

However with any averaging of a plurality of figures there is an associated loss of information. It is possible that one of the credit rating scores used to make up the average does signal a risk but by relying upon the average alone, this warning is lost when viewing only the processed single compound credit rating score. There remains therefore a need for the current invention relating to a system and method for credit management of customers based upon data gathered from a plurality of sources and the effective presentation of such data to a credit manager. SUMMARY OF THE INVENTION

In accordance with a first embodiment, the present invention is directed to providing a credit management system comprising at least one database containing credit related data appertaining to at least one customer said data being obtained from at least one source selected from sources internal and external to a collector; and at least one processor configured to produce a set of n credit ratings based upon said credit related data where n is an integer greater than or equal to one; and analyze said set of credit ratings to produce a credit rating of n dimensions.

Typically at least one credit rating is based upon data relating to at least one of the group comprising:

• degree of exposure to risk,

• payment frequency,

• late payment,

• size of loan,

• collaterals

• liabilities

• returned checks,

• length of association with customer,

• changes in payment behavior,

• changes in payment frequency, and

• changes in customer details.

In preferred embodiments the set of n credit ratings represents a set of n coordinates defining the location of the at least one customer in n dimensional space.

Optionally the system additionally comprises a set of criteria relating at least one set of credit ratings to a credit management policy. Preferably the system additionally comprises an n dimensional guiding template in which specific regions of n dimensional space are attributed guiding values used to indicate credit management policy related to customers whose coordinates lie within said regions.

Typically the processor compares the set of credit ratings to the set of criteria thereby automatically determining the credit management policy to be applied to each customer.

Optionally the system additionally comprises at least one user interface having at least one of the group comprising: at least one input field for providing data to the database; and a plurality of data representations emphasizing data which is pertinent to the user.

In preferred embodiments at least one data representation is selected from the group comprising tables, graphs, histograms, charts and combinations thereof wherein pertinent data is emphasized by at least one of:

• non-linear scales having short intervals providing high resolution for highly pertinent data and long intervals providing low resolution for background data,

• multiple representations providing at least one high resolution representation for highly pertinent data and providing at least one low resolution representation for background data, and

• visual indications, selected from the group comprising column width, text color, text style, text size, background color, use of icons, shading, animation and combinations thereof.

Optionally the pertinent data to be emphasized is selected by the user.

Alternatively the pertinent data appertains to at least one of the group comprising:

• transactions associated with a set of customers,

• credit risk associated with a set of transactions,

• credit management, • financial management, and

• general management.

In a second aspect of the current invention a transaction based credit management system is provided comprising at least one user interface configured for at least one of the group comprising: providing data to the database; and emphasizing data pertinent to a user.

In preferred embodiments at least one data representation of the transaction based credit management system is selected from the group comprising tables, graphs, histograms, charts and combinations thereof wherein pertinent data is emphasized by at least one of:

Optionally the pertinent data of the transaction based credit management system to be emphasized is selected by the user.

Alternately the pertinent data of the transaction based credit management system appertains to at least one of the group comprising:

• transactions associated with a set of customers,

• credit risk associated with a set of transactions,

• credit management,

• financial management, and

• general management. A third aspect of the current invention provides a method for credit management comprising the steps of;

a. obtaining credit related data appertaining to at least one customer said data being obtained from a plurality of sources both internal and external to a collector;

b. producing a set of n credit ratings based upon said credit related data where n is an integer greater than or equal to one; and

c. analyzing said set of credit ratings simultaneously to produce an n-dimensional credit rating.

Optionally the method further comprises the steps of;

d. producing a set of n coordinates for each customer and thereby assigning said customer a location in n-dimensional space; and

e. producing an n-dimensional guiding template in which specific regions of n- dimensional space are attributed guiding values used to indicate credit management policy related to customers whose coordinates lie within said regions.

Typically the method of credit management produces at least one credit rating based upon data relating to at least one of the group comprising:

• degree of exposure to risk,

• payment frequency,

• late payment,

• size of loan,

• collaterals

• liabilities

• returned checks,

• length of association with customer, • changes in payment behavior,

• changes in payment frequency, and

• changes in customer details.

Typically the method of credit management provides a user interface having at least one data representation emphasizing data pertinent to a user.

In preferred embodiments the user interface of the method emphasizes data pertinent to the user by at least one of:

• providing non-linear scales having short intervals providing high resolution for highly pertinent data and long intervals providing low resolution for background data,

• presenting multiple representations providing at least one high resolution representation for highly pertinent data and providing at least one low resolution representation for background data, and

• using visual indications, selected from the group comprising column width, text color, text style, text size, background color, use of icons, shading, animation and combinations thereof.

Alternatively the method provides at least one data representation of the history of the ^-dimensional credit rating said data representation being selected from the group comprising tables, graphs, histograms, charts and combinations thereof.

BRIEF DESCRIPTION OF THE FIGURES

For a better understanding of the invention and to show how it may be carried into effect, reference will now be made, purely by way of example, to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of the preferred embodiments of the present invention only, and are presented in the cause of providing what is believed to be the most useful and readily understood description of the principles and conceptual aspects of the invention. In this regard, no attempt is made to show structural details of the invention in more detail than is necessary for a fundamental understanding of the invention; the description taken with the drawings making apparent to those skilled in the art how the several forms of the invention may be embodied in practice. In the accompanying drawings: Fig. 1 schematically represents the credit management system according to a first embodiment of the current invention;

Fig. 2 shows a two dimensional table displaying data relating to an exemplary customer set according to one embodiment of the user interface;

Fig. 3 shows a histogram displaying data relating to an exemplary customer set according to a second embodiment of the user interface;

Fig. 4 shows a list displaying data relating to an exemplary customer set according to a third embodiment of the user interface;

Fig. 5 shows a tabular representation of the data in Fig. 4;

Fig. 6 shows charts displaying data relating to an exemplary customer according to further embodiments of the user interface;

Fig. 7 shows another visual data representation of the user interface showing the parameter averages for the exemplary customer set;

Fig. 8 represents another visual data representation displaying data relating to a sub-set of customers; Fig. 9 shows the key to the colours used in the table of Fig. 8;

Fig. 10 shows a set of visual data representations displaying data relating to the risk and parameter averages of the exemplary customer;

Fig. 11 shows a visual data representation displaying data summarizing the history of transactions for the exemplary customer; Fig. 12 shows a set of visual data representations displaying data relating to the transactions of the exemplary customer in graphical form;

Fig. 13 shows the same data as Fig. 12 in the form of a table;

Fig. 14 shows a representation of a set of customers alongside their credit scores and exposure histories; Fig. 15 shows a visual data representation of a three dimensional credit rating for a single customer;

Fig. 16 shows the credit history for the customer of Fig. 15 in graphic form; and Fig. 17 shows a flow diagram representing a method for credit management.

DETAILED DESCRIPTION OF THE EMBODIMENTS

The following description is provided, alongside all chapters of the present invention, so as to enable any person skilled in the art to make use of said invention and sets forth the best modes contemplated by the inventor of carrying out this invention. Various modifications, however, will remain apparent to those skilled in the art, since the generic principles of the present invention have been defined specifically to provide a credit rating system and associated method.

The term 'organization' refers hereinafter to the body responsible for credit management of funds such as inter alia selling organizations, or credit clearing companies.

The term 'customer' refers hereinafter to the body in debt to the organization and to whom it is in the interest of the organization to extend credit.

The term 'credit rating' refers hereinafter to any index that presents a measure of the risk that a given customer will default on a payment due. The term ^ln dimensional space' refers hereinafter to a theoretical mathematical construct defined by n axes all of which are parallel to all the others. For example a two dimensional space can be represented, as a simple x-y graph on a piece of paper. It is noted that said space can be defined, geometrically, algebraically, numerically or in any other format. The term 'credit management' refers hereinafter to the management, guidance, control, responsibility or the overseeing of credit extended to customers.

The term 'inter-business' refers hereinafter to any transaction, communication, transfer of funds or any other action involving two or more parties. The term 'transaction' refers hereinafter to an action or set of actions occurring between two or more parties, for example in the conduct of social, political, business, commercial, financial, governmental or any other affairs.

Reference is now made to Fig. 1 schematically representing the credit management system 1 according to a first embodiment of the current invention. The credit management system 1 comprises a database 11 containing credit related data appertaining to at least one customer said data being obtained from a plurality of sources both internal 12 and external 13 to the organization.

Internal data sources 12 include inter alia the internal financial system of the organization, internal transaction processing system, experience of financial, credit, sales and collection departments of the organization for example. Data is also obtained from sources external to the organization 13, such as from credit rating companies, credit bureau data, banks, insurance companies and the like.

It will be appreciated that a user interface 15 may provide fields for inputting credit related data to the database 11. Typically credit related data is uploaded using a standard input form. Alternatively the credit related data may be uploaded in a manner determined by the user.

The database 11 is coupled to a processor 14 configured and operable to produce a set of n credit ratings based upon said credit related data where n is an integer greater than or equal to one; and analyze said set of credit ratings simultaneously to produce a credit rating of n dimensions. Optionally the processor 14 is coupled to a user interface 15 which having a plurality of data representations (not shown in Fig. 1) emphasizing data which is pertinent to the user

Fig. 2 shows a first visual data representation according to one embodiment of the user interface 15 which is accessible from an internet browser. A two dimensional table displays data relating to an exemplary customer set comprising two-hundred and thirty customers. The table is bounded by two axes, a vertical axis 210 representing a

Signal Score, ranging from -10 to +10, and a horizontal axis 220 representing a Risk

Score, ranging from A to E. The Risk Score is based upon credit axioms and the credit history of a customer and the Signal Score is based upon variations in patterns of payment behaviour. In this example, a total debt of 12,010,559 NIS is represented spread over all the customers. Although each individual customer has a precise value for both Risk Score and Signal Score and therefore can be assigned exact coordinates on a continuous two dimensional array, the matrix has been divided into forty cells 260 each representing a two dimensional range of credit ratings. Two numbers are presented in each sector; the number outside the brackets 230 shows the total amount of debt currently held within that two dimensional range of credit ratings, the number inside the brackets 240 in each sector shows the number of customers falling in each sector. The amount of debt is also represented visually by a bar 250 whose width is proportional to the level of debt for that segment. Although this example is two dimensional it will be appreciated that the table could be extended to three dimensions or more, thereby representing more data.

Fig. 3 represents a second visual data representation according to another embodiment

^• of the user interface 15. A histogram 300 displays data relating to the exemplary customer set. A horizontal axis 310 is labelled with the risk groups of the customers and the vertical axis 320 represents the number of customers in each risk group. So, for example, it can be easily seen that 85 customers fall in the B+ risk group 330. The user is able here to select alternative parameters for the axes, such as total expenditure, total debt, external knowledge or other such information.

Fig. 4 shows a third visual data representation displaying data relating to the first 23 of the exemplary customer set in the form of a simple list 400. The obligo value 430 and credit ceiling 440 is displayed for each customer 420. Note that an exemplary customer (company_1087) 450, is tracked in further detail in the later figures.

Fig. 5 shows an alternative visual data representation displaying the same data as the table in Fig. 4, related to changes in credit ceiling of the customers, in the form of a histogram 500. Note that with this form of visual representation it is much easier to spot anomalous data such as the spike indicated 510.

Reference is now made to Fig. 6, which represents two further visual data representations displaying data relating to exemplary customer set in the form of charts 600 and a table 610 providing higher resolution to current data, according to another embodiment of the present invention. The two pie charts 601 and 602 show a break down of the customers according to obligo value 601 and Risk Score 602. The divisions of the table are not linear. The central value 611 is the current value and displays data applicable to a single day, close data either in the future 612 or in the past 613 is displayed with a lower resolution, data applicable to a portion of the current month is represented in each case. More distant data is given in even lower resolution 614 and 615 where data for a whole month is presented in each cell. The most peripheral cells 616 and 617 show yet more distant past 617 and future 616 data at still lower resolution. This representation 610 provides a visual aid to understanding a given value in the context of the recent as well as the distant past and future. Parameter values for the set of customers can be viewed if the user clicks upon the button marked, 630.

Fig. 7 shows still another visual data representation of the user interface, showing the parameter averages for the exemplary customer set in the form of a table 700 providing higher resolution to current data. This is the panel which is opened when the user clicks on the button marked 630 in Fig. 6. The current value of each parameter is given in the highest resolution 710 recent data is normalised over a month 720 providing lower resolution, more distant data 730 is normalised over two months. Still more distant data is normalised over three months 740, 750 and the most distant data is normalised over a year 760. This representation provides a visual aid to understanding a given value in the context of the recent as well as the distant past and future.

Reference is now made to Fig. 8 which represents still another visual data representation displaying data relating to a sub-set of 7 customers in the form of a table 800 according to another embodiment of the present invention. This table 800 summarizes data appertaining to the first seven customers out of the total of eleven represented in the cell indicated 260 in Fig. 2. The names of the customers is presented 810 alongside their Signal Score 820 and Risk Score 830 values which are highlighted using colour coding, as well as further credit data 840. Note that the exemplary customer (company_1087) 850, is tracked in further detail in the later figures.

Reference is now made to Fig. 9 showing a data representation displaying data, relating to a sub-set of the customers represented in Fig. 7, in the form of a table with the addition of a key 910 to the colour coding, according to another embodiment of the present invention. The exemplary customer 950 is tracked in further detail in the later figures. Fig. 10 shows a set of visual data representations 1000 displaying data relating to the risk and parameter averages of the exemplary customer according to another embodiment of the present invention. The company details are displayed in the top table 1010. The current status of the Signalling Score 1020, Risk Score 1030 and further risk related grades 1040 are presented in the lower tables. The parameter averages for the exemplary customer are presented here 1050 with higher resolution for the more recent data 1052 than for more distant data 1054. Note that the parameter averages are presented here for the individual customer whereas in Fig. 7, they are presented for the whole customer population. The exposure history for this individual customer is also displayed, 1060, as it is in the table, 610, in Fig. 6, for the whole customer population.

Reference is now made to Fig. 11, which shows a visual data representation displaying data summarizing the history of transactions for the exemplary customer, according to another embodiment of the present invention. This is a linear table 1110 with equal resolution for all the months and summary table 1120 showing the number of all transactions pertaining to the individual customer.

Fig. 12 shows a set of visual data representations 1200 displaying data relating to the transactions of the exemplary customer in graphical form, according to another embodiment of the present invention. The top graph 1210 displays two line graphs showing the history of the customers Signal Score 1212 and Risk Score 1214. The histograms below this 1220, 1230 and 1240 represent the transaction history of the customer. By comparing the history of debt, 1220, and the history of payment, 1230, on adjacent histograms sharing a horizontal axis 1225, a visual pattern is created of the customer's payment habits. Colour coding on these histograms enhances further the nature of these transactions.

Fig. 13 shows still another visual data representation displaying the same data relating to the transactions of the exemplary customer in the form of a table 1300 according to another embodiment of the present invention. The different data representations for the same data shown in Figs. 12 and 13 demonstrate the advantages of having multiple representations for the same data. A credit manager can typically recognise patterns and trends more readily in a histogram 1200 than a data table 1300. However it is usually easier to identify individual statistics from a data table 1300. Reference is now made to Fig. 14, showing a further visual data representation according to another embodiment of the user interface. The parameter averages of a set of 8 customers are displayed as a table 1400. Here a list of companies 1410 is presented alongside their credit scores 1420, which are colour coded, and their exposure histories 1430. The exposure histories 1430 are presented in a non-linear form providing higher resolution to most current data 1435 which is further emphasized by means of background shading.

Fig. 15 shows a visual data representation of a three dimensional credit rating for a single customer according to another embodiment of the user interface. There are four separate tables represented here. The top table 1510 contains customer information, the second 1520 presents current 1522 and historical 1524 values of the Signal Score, the third table 1530 presents the current 1532 and historical 1534 values of the Risk Score and the bottom table 1540 presents the current 1542 and historical 1544 values of an external credit rating score. The three score values can represent three coordinates in a three dimensional array.

Fig. 16 shows the credit history for the customer of Fig. 15 in graphic form according to another embodiment of the current invention. The top graph 1610 presents historical variation in two scores - the risk score (in blue) 1611 and the Signal Score (in red) 1612. The second graph 1620 shows the transaction history of the customer, each spike on this graph shows an order placed its height representing the size of the order, a green spike 1622 is used where the debt has been paid, a yellow spike 1624 is used where deferred payment has been received and red spikes 1626 represent outstanding debt. The third graph 1630 shows the payment history for the same customer over the same time period, each spike here represents payment received. Note that initially, over the period marked by bracket 1631, the customer pays debts as they arise, whereas the over the period marked by bracket 1632, the customer continues to pay debts but not immediately rather before a subsequent order. This change in behaviour is picked up by the two dimensional credit rating. Note that at point 1613 on the top graph 1610 the Risk Score continues to remain high and in fact rises as the customer is still paying his debts, however the Signal Score drops indicating a change in payment behaviour. This sensitivity to customer behaviour is a feature of multidimensional credit rating that is not possible when using a simple credit rating technique. Fig 17 shows a flow diagram representing a method for credit management comprising the following steps:

Step (a): obtaining credit related data appertaining to at least one customer said data being obtained from a plurality of sources both internal and external to a collector; Step (b): producing a set of n credit ratings based upon said credit related data where n is an integer greater than or equal to one;

Step (c): analyzing said set of credit ratings simultaneously to produce an n- dimensional credit rating.

Step (d): producing a set of n coordinates for each customer and thereby assigning said customer a location in n-dimensional space; and

Step (e): producing an n-dimensional guiding template in which specific regions of n- dimensional space are attributed guiding values used to indicate credit management policy related to customers whose coordinates lie within said regions.

Step (f): providing a user interface having at least one data representation emphasizing data pertinent to a user

Step (g): providing non-linear scales having short intervals providing high resolution for highly pertinent data and long intervals providing low resolution for background data,

Step (h): presenting multiple representations providing at least one high resolution representation for highly pertinent data and providing at least one low resolution representation for background data, and

Step (i): using visual indications, selected from the group comprising column width, text color, text style, text size, background color, use of icons, shading, animation and combinations thereof.

Claims

1. A credit management system comprising;

a. at least one database containing credit related data appertaining to at least one customer said data being obtained from at least one source selected from sources internal and external to a collector; and

b. at least one processor configured to:

i. produce a set of n credit ratings based upon said credit related data where n is an integer greater than or equal to one; and

ii. analyze said set of credit ratings to produce a credit rating of n dimensions.

2. The system of Claim 1, wherein at least one credit rating is based upon data relating to at least one of the group comprising:

i. degree of exposure to risk,

ii. payment frequency,

iii. late payment,

iv. size of loan,

v. collaterals

vi. liabilities

vii. returned checks,

viii. length of association with customer,

ix. changes in payment behavior,

x. changes in payment frequency, and

xi. changes in customer details.

3. The system of Claim 1, wherein said set of n credit ratings represents a set of n coordinates defining the location of the at least one customer in n dimensional space.

4. The system of Claim 1, additionally comprising a set of criteria relating at least one set of credit ratings to a credit management policy.

5. The system of Claim 1, additionally comprising an n dimensional guiding template in which specific regions of n dimensional space are attributed guiding values used to indicate credit management policy related to customers whose coordinates lie within said regions.

6. The system of Claim 4, wherein said processor compares said set of credit ratings to said set of criteria thereby automatically determining the credit management policy to be applied to each customer.

7. The system of Claim 1, additionally comprising at least one user interface having at least one of the group comprising:

a. at least one input field for providing data to the database; and

b. a plurality of data representations emphasizing data which is pertinent to the user.

8. The system of Claim 7, said at least one data representation being selected from the group comprising tables, graphs, histograms, charts and combinations thereof wherein pertinent data is emphasized by at least one of:

a. non-linear scales having short intervals providing high resolution for highly pertinent data and long intervals providing low resolution for background data,

b. multiple representations providing at least one high resolution representation for highly pertinent data and providing at least one low resolution representation for background data, and

c. visual indications, selected from the group comprising column width, text color, text style, text size, background color, use of icons, shading, animation and combinations thereof.

9. The system according to Claim 7 wherein the pertinent data to be emphasized is selected by the user.

10. The system according to Claim 7 wherein said pertinent data appertains to at least one of the group comprising:

i. transactions associated with a set of customers,

ii. credit risk associated with a set of transactions,

iii. credit management,

iv. financial management, and

v. general management.

11. A transaction based credit management system comprising at least one user interface having a plurality of data representations emphasizing data which is pertinent to the user.

12. The system of Claim 11, said at least one data representation being selected from the group comprising tables, graphs, histograms, charts and combinations thereof wherein pertinent data is emphasized by at least one of:

13. The system according to Claim 11 wherein the pertinent data to be emphasized is selected by the user.

14. The system according to Claim 11 wherein said pertinent data appertains to at least one of the group comprising:

i. transactions associated with a set of customers,

ii. credit risk associated with a set of transactions,

iii. credit management,

iv. financial management , and

v. general management.

15. A method for credit management comprising the steps of;

c. analyzing said set of credit ratings simultaneously to produce an n- dimensional credit rating.

16. The method of Claim 15 comprising the further steps of;

d. producing a set of n coordinates for each customer and thereby assigning said customer a location in ^-dimensional space; and

e. producing an ^-dimensional guiding template in which specific regions of ^-dimensional space are attributed guiding values used to indicate credit management policy related to customers whose coordinates lie within said regions.

17. The method of Claim 15, wherein at least one credit rating is based upon data relating to at least one of the group comprising:

i. degree of exposure to risk,

ii. payment frequency, iii. late payment,

iv. size of loan,

v. collaterals

vi. liabilities

vii. returned checks,

viii. length of association with customer,

ix. changes in payment behavior,

x. changes in payment frequency, and

xi. changes in customer details.

18. The method of Claim 15 providing a user interface configured for at least one of the group comprising:

a. providing data to the database; and

b. emphasizing data pertinent to a user.

19. The method of Claim 18 emphasizing said data pertinent to the user by at least one of:

a. providing non-linear scales having short intervals providing high resolution for highly pertinent data and long intervals providing low resolution for background data,

b. presenting multiple representations providing at least one high resolution representation for highly pertinent data and providing at least one low resolution representation for background data, and

c. using visual indications, selected from the group comprising column width, text color, text style, text size, background color, use of icons, shading, animation and combinations thereof.

20. The method of Claim 17 producing at least one data representation of the history of the ^-dimensional credit rating said data representation being selected from the group comprising tables, graphs, histograms, charts and combinations thereof.