WO2007135683A2 - Système et procédé de gestion de crédit - Google Patents

Système et procédé de gestion de crédit Download PDF

Info

Publication number
WO2007135683A2
WO2007135683A2 PCT/IL2007/000626 IL2007000626W WO2007135683A2 WO 2007135683 A2 WO2007135683 A2 WO 2007135683A2 IL 2007000626 W IL2007000626 W IL 2007000626W WO 2007135683 A2 WO2007135683 A2 WO 2007135683A2
Authority
WO
WIPO (PCT)
Prior art keywords
data
credit
customer
providing
pertinent
Prior art date
Application number
PCT/IL2007/000626
Other languages
English (en)
Other versions
WO2007135683A3 (fr
Inventor
Igaal Brumer
Original Assignee
Perspective D.S.S Ltd.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from IL175868A external-priority patent/IL175868A0/en
Priority claimed from IL175869A external-priority patent/IL175869A0/en
Priority claimed from IL177034A external-priority patent/IL177034A0/en
Application filed by Perspective D.S.S Ltd. filed Critical Perspective D.S.S Ltd.
Publication of WO2007135683A2 publication Critical patent/WO2007135683A2/fr
Publication of WO2007135683A3 publication Critical patent/WO2007135683A3/fr

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/02—Banking, e.g. interest calculation or account maintenance

Definitions

  • 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.
  • 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.
  • 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 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.
  • 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 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.
  • At least one credit rating is based upon data relating to at least one of the group comprising:
  • 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.
  • the system additionally comprises a set of criteria relating at least one set of credit ratings to a credit management policy.
  • 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.
  • 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.
  • 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.
  • 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:
  • the pertinent data to be emphasized is selected by the user.
  • the pertinent data appertains to at least one of the group comprising:
  • a transaction based credit management system 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.
  • 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:
  • the pertinent data of the transaction based credit management system to be emphasized is selected by the user.
  • the pertinent data of the transaction based credit management system appertains to at least one of the group comprising:
  • a third aspect of the current invention provides a method for credit management comprising the steps of;
  • n is an integer greater than or equal to one
  • the method further comprises the steps of;
  • 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.
  • the method of credit management produces at least one credit rating based upon data relating to at least one of the group comprising:
  • the method of credit management provides a user interface having at least one data representation emphasizing data pertinent to a user.
  • 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.
  • 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.
  • the term 'organization' refers hereinafter to the body responsible for credit management of funds such as inter alia selling organizations, or credit clearing companies.
  • 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 l n 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.
  • 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.
  • 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.
  • a user interface 15 may provide fields for inputting credit related data to the database 11.
  • credit related data is uploaded using a standard input form.
  • 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.
  • 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
  • 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.
  • a total debt of 12,010,559 NIS is represented spread over all the customers.
  • 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.
  • Fig. 3 represents a second visual data representation according to another embodiment
  • 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.
  • 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.
  • Fig. 6 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.
  • FIG. 8 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.
  • the exemplary customer (company_1087) 850 is tracked in further detail in the later figures.
  • 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.
  • FIG. 11 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • Fig 17 shows a flow diagram representing a method for credit management comprising the following steps:

Landscapes

  • Business, Economics & Management (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Engineering & Computer Science (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Strategic Management (AREA)
  • Technology Law (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Financial Or Insurance-Related Operations Such As Payment And Settlement (AREA)

Abstract

L'invention concerne un système et procédé de gestion de crédit. Dans l'invention, des données sont obtenues à partir de sources données extérieures et intérieures et sont utilisées pour générer une base de données contenant des données associées à des crédits concernant au moins un consommateur. Un processeur est conçu pour produire un ensemble de n cotes de solvabilité en fonction des données associées à ces crédits et pour analyser les cotes de solvabilité afin de produire une cote de solvabilité de n dimensions. Les données de crédit sont présentées dans des représentations de données mettant en valeur les données les plus pertinentes pour l'utilisateur.
PCT/IL2007/000626 2006-05-23 2007-05-24 Système et procédé de gestion de crédit WO2007135683A2 (fr)

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
IL175868 2006-05-23
IL175868A IL175868A0 (en) 2006-05-23 2006-05-23 Means and method of objectively assessing the credit rating of customers
IL175869 2006-05-23
IL175869A IL175869A0 (en) 2006-05-23 2006-05-23 Multidimensional credit rating system and associated credit management method
IL177034 2006-07-23
IL177034A IL177034A0 (en) 2006-07-23 2006-07-23 Means and method of presenting customer related data for use in credit management

Publications (2)

Publication Number Publication Date
WO2007135683A2 true WO2007135683A2 (fr) 2007-11-29
WO2007135683A3 WO2007135683A3 (fr) 2009-04-23

Family

ID=38723698

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/IL2007/000626 WO2007135683A2 (fr) 2006-05-23 2007-05-24 Système et procédé de gestion de crédit

Country Status (1)

Country Link
WO (1) WO2007135683A2 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180197240A1 (en) * 2015-06-26 2018-07-12 Sumitomo Mitsui Banking Corporation Banking system, method and computer-readable storage medium for credit management for structured finance
CN113298639A (zh) * 2021-05-14 2021-08-24 中证鹏元资信评估股份有限公司 一种具有高风险筛选功能的信用评级系统

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6119103A (en) * 1997-05-27 2000-09-12 Visa International Service Association Financial risk prediction systems and methods therefor
US20020011243A1 (en) * 2000-06-14 2002-01-31 Gerard Barbezat Surface layer forming a cylinder barrel surface, a spraying powder suitable therefor and a method of creating such a surface layer
US20020152155A1 (en) * 2001-04-13 2002-10-17 Greenwood James E. Method for automated and integrated lending process
US6546545B1 (en) * 1998-03-05 2003-04-08 American Management Systems, Inc. Versioning in a rules based decision management system

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6119103A (en) * 1997-05-27 2000-09-12 Visa International Service Association Financial risk prediction systems and methods therefor
US6546545B1 (en) * 1998-03-05 2003-04-08 American Management Systems, Inc. Versioning in a rules based decision management system
US20020011243A1 (en) * 2000-06-14 2002-01-31 Gerard Barbezat Surface layer forming a cylinder barrel surface, a spraying powder suitable therefor and a method of creating such a surface layer
US20020152155A1 (en) * 2001-04-13 2002-10-17 Greenwood James E. Method for automated and integrated lending process

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180197240A1 (en) * 2015-06-26 2018-07-12 Sumitomo Mitsui Banking Corporation Banking system, method and computer-readable storage medium for credit management for structured finance
CN113298639A (zh) * 2021-05-14 2021-08-24 中证鹏元资信评估股份有限公司 一种具有高风险筛选功能的信用评级系统

Also Published As

Publication number Publication date
WO2007135683A3 (fr) 2009-04-23

Similar Documents

Publication Publication Date Title
Fecher et al. Productive performance of the French insurance industry
Assagaf et al. Determinants of financial performance of state-owned enterprises with government subsidy as moderator
Ke et al. What insiders know about future earnings and how they use it: Evidence from insider trades
Adair et al. The reporting of risk in real estate appraisal property risk scoring
Orens et al. Determinants of sell‐side financial analysts’ use of non‐financial information
US20140351171A1 (en) Investment Guidance System Which Enables Individuals To Rate And Select Assets Based On Personal Investment Preferences
Danso et al. Market sentiment and firm investment decision-making
US20080288329A1 (en) User interface for identifying trade opportunities
US20030055758A1 (en) Method and apparatus for identifying investor profile
Farj et al. Importance of accounting information to investors in the stock market: a case study of Libya
Gabbi et al. Does face-to-face contact matter? Evidence on loan pricing
Odonkor An assessment of credit risk management practices of Adansi Rural Bank Limited
Njuguna et al. Cashflow Volatility, Corporate Investments and Value of Nonfinancial Firms Listed in Kenya
Ge et al. Do CFOs have styles of their own? An empirical investigation of the effect of individual CFOs on financial reporting practices
Chiekezie et al. Adoption of competitor focused accounting methods in selected manufacturing companies in Nigeria
Black The development of risk based regulation in financial services: Canada, the UK and Australia
Alade Effect of international financial reporting standards adoption on value relevance of accounting information of Nigerian listed firms
AU2006338310B2 (en) Method configured for facilitating financial consulting services
Alqam et al. The relative importance of financial ratios in making investment and credit decisions in Jordan
WO2007135683A2 (fr) Système et procédé de gestion de crédit
Beke International accounting standardization practice in Hungary
Vahdani et al. The role of corporate diversification in tax avoidance in companies listed in the Tehran Stock Exchange
CN113256334A (zh) 一种基于凝聚指标对金融产品价格的预测方法
Gad et al. Employee financial literacy and financial reporting misconduct
Svensson et al. How does IFRS 15 influence Swedish auditors and financial analysts’ understanding of companies’ revenue transactions?

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 07736366

Country of ref document: EP

Kind code of ref document: A2

NENP Non-entry into the national phase in:

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 07736366

Country of ref document: EP

Kind code of ref document: A2