Electronic data processing system and method of using an electronic data processing system for automatically determining a risk indicator value

• US 20060100957A1
• Filed: 04/10/2003
• Published: 05/11/2006
• Est. Priority Date: 05/08/2002
• Status: Abandoned Application

• Current Assignee: Accenture Global Services GmbH (Accenture PLC)
• Original Assignee: Accenture Global Services GmbH (Accenture PLC)
• Inventors: Buttler, Michael, Jasic, Teo
• Application Number:
  US10/513,638
• Publication Number:
  US 20060100957A1
• Time in Patent Office: Days
• US Class Current: 705/38
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

An electronic data processing system for automatically determining a risk indicator value based on a number of risk parameters, for evaluating a risk involved with performing a transaction between a client and a transaction provider in said data processing system.

Claims

1. An electronic data processing system for automatically determining a risk indicator value based on a number of risk parameters, for evaluating a risk involved with performing a transaction between a client and a transaction provider in said data processing system, the data processing system comprising:

a first input for inputting client input data relating to said client;

a first database for storing said client input data;

a computing processing module;

an output for outputting said risk indicator value;

said first database for assembling and storing said client input data in a predetermined number of client input data files in said first database;

said computing processing module for reading client input data from at least one of said client input data files of said first database into said computing processing module;

a first calculating unit for determining risk parameter values for a number of predefined risk parameters from said client input data, for evaluating each risk parameter value using an associated evaluation function to determine an evaluation value of said risk parameter and for comparing each evaluation value with at least one threshold value to determine whether the associated risk parameter is critical, uncritical, or neutral for the risk involved with performing said transaction;

a second calculating unit for calculating a risk density for each non-neutral risk parameter, for aggregating the risk densities of non-neutral risk parameters of at least one redefined set of risk parameters to determine a common risk density of said set of risk parameters, for generating an overall risk probability, based on predetermined weights and partial risk probabilities for said sets of risk parameters, for calculating a net utilisation as the difference between an utilisation in said transaction and a value of collaterals available for said transaction at a certain point in time; and

for calculating said risk indicator value as the product of said net utilisation and said overall risk probability.

2. The system of claim 1 wherein said second calculating means unit integrates a risk density function using said evaluation value of the respective non-neutral risk parameter.

3. The system of claim 1 wherein said second calculating unit generates a partial risk probability from the common risk density, for each of a number of risk categories, and said multiples partial risk probability with an assigned category weight, for each of said categories, to determine the overall risk probability as a weighted average value of said partial risk probabilities.

4. The system of one of claim 1, further comprising a second input for reading threshold values a, b and stretching parameters c, d of the evaluation function;

and a first combiner for deriving said evaluation function for each of said risk parameters based on said threshold values and stretching parameters.

5. The system of claim 4, further comprising a second combiner for deriving said risk density function for each risk parameter which has been determined not to be neutral.

6. The system of claim 1, comprising a second database for storing said risk indicator value.

7. The system of claim 1, comprising a second input for inputting predetermined threshold values a, b and stretching parameters c, d from an external source for determining said evaluation functions.

8. The system of claim 4, comprising a third database for storing said threshold values a, b and stretching parameters c, d.

9. The system of claim 1, wherein said client input data comprise personal client data, historical account data and historical credit data of the client, wherein the historical data is assembled in said first database in a number of files corresponding to a certain number of past months for each of a number of data types.

10. A method of using an electronic data processing system for automatically determining a risk indicator value based on a number of risk parameters, for evaluating a risk involved with performing a transaction between a client and a transaction provider in said data processing system, the data processing system including a first input for inputting client input data relating to said client;

a first database for storing said client input data;

a computing processing module for reading said client input data from said first database, and calculating said risk indicator value;

an output for outputting said risk indicator value;

the method comprising the steps of;

collecting and inputting into said first database client input data;

assembling and storing said client input data in a predetermined number of client input data files in said first database;

reading client input data from at least one of said client input data files of said first database into said computing processing module;

determining risk parameter values (x) for a predetermined number of redefined risk parameters from said client input data;

determining an evaluation function for each of said risk parameters;

evaluating each risk parameter using the associated evaluation function to determine an evaluation value of said risk parameter;

comparing each evaluation value with at least one threshold value to determine whether the associated risk parameter is critical, uncritical, or neutral for the risk involved with performing said transaction;

determining a risk density function for each risk parameter which has been determined to be not neutral;

aggregating the risk densities of at least one redefined set of risk parameters to a common risk density to determine a partial risk probability for said set of risk parameters;

determining a weight for said set of risk parameters;

generating an overall risk probability, based on said determined risk weights and partial risk probabilities for each set of risk parameters;

calculating a net utilisation as the difference between a utilisation in said transaction and a value of collaterals available for said transaction at a certain point in time; and

calculating said risk indicator value as the product of said net utilisation and said overall risk probability.

11. The method of claim 10, wherein said step of aggregating comprises separately aggregating the risk densities of non-neutral risk parameters of a number of sets of risk parameters to determine a partial risk probability for each of said sets.

12. The method of claim 11, wherein a weighted average value of all said partial risk probabilities is generated to determine an overall risk probability for calculating said risk indicator value.

13. The method of claim 10, wherein the risk indicator value is output to a second database for storing said risk indicator value.

14. The method of claim 10, wherein said evaluation function for each of said risk parameters is specifically determined.

15. The method of claim 14, wherein said threshold values and stretching parameters are input into the data processing system from an external source.

16. The method of claim 10, wherein said client input data comprise personal client data, historical account data and historical credit data of the client, wherein the historical data is assembled in a number of files corresponding to up to 36 past months for each of a number of data types.

17. The method of claim 10, wherein said amount claimed in said transaction and said value of collaterals relate to respective amounts of monetary units.

18. The method of claim 10, wherein the risk probability is expressed as a real number between 0 and 1, inclusive, and the risk indicator value is a non-negative real number.

19. The method of claim 10, wherein the risk indicator values of a group of clients are aggregated to form a higher level risk indicator value.

20. The method of claim 10 wherein each set of risk parameters corresponds to one category of client-related values.

21. The method of claim 10 wherein client input data are read from each client input data file by a single access to said file.

22. Computer program product including program code for performing the method of claim 10 when run on a data processing device.