CN113283977A - Credit scoring decision analysis method, device, equipment and storage medium - Google Patents
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Abstract
The invention provides a credit scoring decision analysis method, a device, equipment and a readable storage medium, wherein the method comprises the steps of obtaining a loan request, and obtaining the school information and the family information of an applicant according to the loan request; generating credibility vectors and credibility losing vectors of the applicant according to the on-going information and the family information; and generating a credit scoring decision model according to the credibility vector and the loss credibility vector, wherein the credit scoring decision model is used for generating a loan decision result. The problem of the existing loan decision objectivity is solved.
Description
Technical Field
The invention relates to the technical field of internet finance, in particular to a credit scoring decision analysis method, a credit scoring decision analysis device, credit scoring equipment and a credit scoring storage medium.
Background
In the credit scoring mechanism, the only methods used are statistical discrimination and classification methods, and the statistical method is still the most common method for establishing credit scoring. In the aspect of personal credit scoring, the method of the square Kuangnan and the like [10] provides a credit scoring model based on semi-supervised generalized additive Logistic regression, and the unmarked sample information is fully utilized to evaluate the personal credit default risk. Ginger brightness [11] and the like carry out relevant induction aiming at models and development of personal credit scoring, combine a statistical model and an artificial intelligence algorithm, and carry out optimization and significance weighting on a personal credit scoring index system so as to solve the problems existing in the personal credit scoring. In the aspect of college student credit investigation, Zhang Qiang et al [12] designs a college assistant loan credit rating model based on an improved fuzzy algorithm, and establishes the credit rating of college students by samples collected by questionnaire surveys of relevant colleges. Yuanzhuai et al [13] utilize an initial population and a moving step length of an improved firefly algorithm, optimize initial weight and a threshold parameter of a BP neural network, apply a neural network cooperation ensemble learning algorithm (IGSO-BP), and establish a college student personal credit evaluation model.
The university student venture loan approval process will eventually choose between two behaviors: a new applicant is given a loan or refuses his credit application. Credit scoring attempts to establish an optimal criterion to assist in making such decisions, although optimal here means that such criterion is optimal when applied to previous applicant samples. This has the advantage that the applicant can be aware of their performance to date. If the decision-making behavior is only two-refusal or acceptance, it makes no sense to divide the applicant's performance into more than good and bad, but more than two, because here the "good" behavior is what the loan authority can accept, i.e. how much his credit is. Bad behavior is the behavior of those applicants that the loan institution wishes to refuse. There are some loan institutions where bad behavior is defined as several unreturned loans in succession, while others regard severe default behavior up to a certain amount as bad behavior.
This method has its inherent bias because the sample here includes only those applicants who have approved their loan, and we do not know the information of those applicants who have rejected the application. Thus, the sample is representative only of those applicants that have accepted in the past and not of those that have rejected in the past.
In view of this, the present application is presented.
Disclosure of Invention
The invention discloses a credit scoring decision analysis method, a credit scoring decision analysis device, credit scoring equipment and a credit scoring storage medium, which at least solve the defects of the prior art to a certain extent.
The first embodiment of the present invention provides a credit score decision analysis method, including:
obtaining a loan request, and obtaining the information of the applicant at school and the family information according to the loan request;
generating credibility vectors and credibility losing vectors of the applicant according to the on-going information and the family information;
and generating a credit scoring decision model according to the credibility vector and the loss credibility vector, wherein the credit scoring decision model is used for generating a loan decision result.
Preferably, the generating of the credibility vector and the credibility loss vector of the applicant according to the current-school information and the family information specifically includes:
generating a reliability matrix and a weight distribution vector according to the on-going information and the family information;
calculating the reliability matrix and the weight distribution vector to obtain the reliability vector;
and generating a loss confidence vector according to the false item number of the correction information.
Preferably, the generating a credit score decision model according to the credibility vector and the loss credibility vector specifically includes:
and differencing the credibility vector and the credibility vector to generate a credit scoring decision model.
Preferably, the method further comprises the step of obtaining a confidence negative extreme value of the applicant;
and when the applicant cannot reach the credit granting condition according to the negative extreme value of the credibility, generating a negative decision result.
A second embodiment of the present invention provides a credit scoring decision analysis apparatus, including:
the loan request acquisition unit is used for acquiring a loan request and acquiring the in-school information and the family information of an applicant according to the loan request;
a vector generating unit for generating credibility vectors and credibility loss vectors of the applicant according to the current information and the family information;
and the credit scoring decision model generating unit is used for generating a credit scoring decision model according to the credibility vector and the loss credibility vector, wherein the credit scoring decision model is used for generating a loan decision result.
Preferably, the vector generation unit is specifically configured to:
generating a reliability matrix and a weight distribution vector according to the on-going information and the family information;
calculating the reliability matrix and the weight distribution vector to obtain the reliability vector;
and generating a loss confidence vector according to the false item number of the correction information.
Preferably, the credit score decision model generation unit is specifically configured to:
and differencing the credibility vector and the credibility vector to generate a credit scoring decision model.
Preferably, the system further comprises a credibility negative extreme value acquiring unit, which is used for acquiring the credibility negative extreme value of the applicant;
and when the applicant cannot reach the credit granting condition according to the negative extreme value of the credibility, generating a negative decision result.
A third embodiment of the present invention provides a credit scoring decision analysis device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a credit scoring decision analysis method as described in any one of the above.
A fourth embodiment of the present invention provides a readable storage medium, where a computer program is stored, where the computer program is executable by a processor of a device where the computer readable storage medium is located, so as to implement a credit score decision analysis method as described in any one of the above.
Based on the credit scoring decision analysis method, the credit scoring decision analysis device, the credit scoring decision analysis equipment and the credit scoring decision analysis storage medium, when a loan request is received, the current school information and the family information of an applicant are obtained, the credibility vector and the loss credibility vector of the applicant are obtained according to the current school information and the family information of the applicant, and a credit scoring decision model is established, and the credit scoring decision model can generate a decision whether to give a loan or not according to the current school information and the family information of the applicant.
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FIG. 1 is a flowchart illustrating a method for decision analysis of credit scoring according to a first embodiment of the present invention;
fig. 2 is a schematic diagram of a credit scoring decision analysis device according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be understood that the term "and/or" as used herein is merely one type of association that describes an associated object, meaning that three relationships may exist, e.g., a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context. Similarly, the phrases "if determined" or "if detected (a stated condition or event)" may be interpreted as "when determined" or "in response to a determination" or "when detected (a stated condition or event)" or "in response to a detection (a stated condition or event)", depending on the context.
In the embodiments, the references to "first \ second" are merely to distinguish similar objects and do not represent a specific ordering for the objects, and it is to be understood that "first \ second" may be interchanged with a specific order or sequence, where permitted. It should be understood that "first \ second" distinct objects may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced in sequences other than those illustrated or described herein.
The following detailed description of specific embodiments of the invention refers to the accompanying drawings.
The invention discloses a credit scoring decision analysis method, a credit scoring decision analysis device, credit scoring equipment and a credit scoring storage medium, which at least solve the defects of the prior art to a certain extent.
Referring to fig. 1, a first embodiment of the present invention provides a credit scoring decision analysis method, which can be executed by a credit scoring decision analysis device (hereinafter, referred to as an analysis device), and in particular, executed by one or more processors in the analysis device, to implement the following steps:
s101, obtaining a loan request, and obtaining the school information and the family information of an applicant according to the loan request;
in this embodiment, the analysis device may be a server located at a cloud end, and the server at the cloud end may establish a communication connection with a user terminal (such as a smart phone, a smart printer, or other smart devices) to implement data interaction.
In particular, in this embodiment, data for analysis may be stored in the server, and the user terminal may obtain corresponding data by issuing a loan request instruction to the server to analyze the uploaded school information and family information.
S102, generating credibility vectors and credibility losing vectors of the applicant according to the on-going information and the family information;
in this embodiment, a reliability matrix and a weight distribution vector may be generated from the current-time information and the family information;
computing the confidence matrix R and the weight distribution vector W to obtain the confidence vector W multiplied by R;
generating a loss confidence vector h according to the false item number of the correction-in-progress information
And S103, generating a credit scoring decision model according to the credibility vector and the loss credibility vector, wherein the credit scoring decision model is used for generating a loan decision result.
In this embodiment, the confidence vector and the loss vector may be differentiated to generate a credit score decision model.
In this embodiment, based on the analysis of the composition of the college student credit rating index, we divide the college student credit rating index into college student credit rating target layer M, AiCredit indicator layer PjAnd establishing the credit index scoring basis and scoring standard of college students as shown in the following table 1;
TABLE 1
Constructing a discrimination matrix according to the evaluation criteria, and dividing the personal credit index system of college students into a target layer M (individuals)Credit integration score), index layers Ai and Pj. In the bank repayment index, the loan institution generally recognizes personal credit (A)1) Compared with the individual performance at school (A)2) More importantly, the repayment capacity is more objective substance expression, and the personal credit mechanism is incorporated into the personal information platform at present. The repayment will shows subjective consciousness and expression, and meanwhile, schools do not form a unified credit standard, compared with A2，A1It appears more authoritative. Therefore, we will M12Value is set to 2, M211/2; the index itself is of the same importance as itself, so M11And M22Are all 1.
According to AiAnd PjThe corresponding index can be obtained
Wherein, Y1And Y2Are respectively A1 and A2The decision matrix of (1).
Separately calculate a decision matrix M, Y1、Y2Maximum eigenvalue λ ofmaxThe consistency index value lCThe index weight value W, and the final weight index result are shown in table 2 below.
TABLE 2 college student Credit evaluation index judgment matrix weight table
According to the index weight analysis of the above judgment matrix, and according to the membership principle of the index andanalysis of various indexes of college students can know A1Weight assignment of 0.6, A2The weight is assigned to 0.4. Due to the index (P) such as the family income level1-P8) Belonging to A1Index (P) such as acquisition condition of prize money9-P15) Belonging to A2With P1For example, its weight w1Is composed of
w1＝0.6×0.2524＝0.1514
By analogy, the index weights respectively calculated by the judgment matrixes of the credit evaluation point indexes are subjected to weight sorting, and finally the weight vector of the index layer is obtained as
In the formula, wiAnd r in the credibility scoring functioniAnd correspond to each other.
According to the calculated index weight value, P is the index with larger weight value1 (0.1514)、P3(0.1087)、P6(0.0939)、P11(0.0699)、P12(0.0782) and P15Item (0.0686). These several indexes are the main factors reflecting the credit risk of college students. For example, personal income is a major source of repayment funds; the good and bad condition of the family affects the repayment of the applicant, and the family is an important support for the repayment when the repayment capacity is insufficient. This is an important basis for the bank to judge the reliability of the applicant. Although it is not emphasized herein that the more money the children in the family have higher credit, the less independent and the more dependent the family is, we emphasize the weight assignment. The smaller of the latter ratio in the weight distribution is P2(0.0232)、P8(0.0355), valence P13(0.0394) and P14(0.0343). This indicates that they have a relatively small impact on the personal credit score, but still reflect some information that the applicant has in terms of credit risk, and that there is value.
The loan institution is not necessarily true and reliable for all information provided by college students and schools, and there may be false information. Then, the weight value of the false information is deducted from the total weight, and we use the confidence level h to represent the checking result, i.e. the checking result is
In the formula (1), f is the index number (term) of data false. When 15 indexes provided by college students contain more than 5 false information, h is 1, and credit granting is refused; when 0< f <5, h is 0.04. The final result is only W-h.
In this embodiment, the method further includes obtaining a negative extremum of the confidence level of the applicant;
and when the applicant cannot reach the credit granting condition according to the negative extreme value of the credibility, generating a negative decision result.
When college students apply for loan, according to the application data submitted by college students, the certification data provided by the payment institution according to schools and the data called by the college students, the credibility vector of each index item is calculated by the index credibility calculation formula in the table 1 and is represented by a credibility matrix R, namely R ═ { R ═ R { (R })1,r2,…,r15In which r isiAnd scoring the credibility of a certain index of the college student credit.
After the accuracy of the materials submitted by the college students is judged, the false information weight value is deducted, namely the obtained personal credit index comprehensive weight distribution vector W is multiplied by the credibility matrix R and then the credibility h is deducted, namely the college student personal credit scoring decision model facing scoring judgment
s＝W×R-h＝{s1,s2,…,s15}＝ {w1,w2,…,w15}{r1,r2,…,r15}-{h1,h2,…,hn} (2)
In the formula (2), siAnd scoring the personal credit risk of the college student. Finally, according to the corresponding threshold value of the calculation result, the credit rating of the university student is determined so as to decide whether to credit the university studentThe student is the applicant.
The credit granting threshold of college students is mainly determined by the social credit investigation condition and the form of the credit degree of the school performance condition. Assume threshold t1Is a base value for which credit is allowed. When P is present1、P3、P7、P9、P15When the credibility of the indexes is 1 and the confidence losing degree h is 0, the college student can be considered to have high total credit level and have the condition and qualification of obtaining loan, and the credit score is
t1＝{0.1514×1+0+0.1087×1+0+0.0885×1++0.0548×1+0.0699×1+0+0.0686×1}＝{0.5419}
Of course, if the university student has a high credit score and has a high credit rating, we can set the threshold of this highest level as t2. While these indices include P7、P3、P5、P7、P8、P9、 P10、P11、P14、P15And credit, etc. When r is 1 and h is 0, the credit score of the credit having the highest rank can be judged
t2＝{0.1514×1+0+0.1087×1+0+0.0679×1+0+0.0565×1 +0.0355×1+0.0699×1+0.0548×1+0+0.0343×1+0 +0.0686×1}＝{0.7476}
Determining the threshold is the key to the ranking. When the credit score of a college student is lower than 60 points, the college student can be said to have poor credit. If the credit score is between 70 and 80 points, the credit score is judged to be better, and if the credit score is higher than 90 points, the credit score is proved to be excellent.
By evaluating score S and threshold t for college student credit1、t2We will be universityThe credit rating of the student is divided into three levels, namely, poor credit, good credit and excellent credit, if the credit rating is 'poor credit', the client is 'bad client', and the decision result is not passed. As in table 3.
TABLE 3 college student Credit rating
In the two-way influence index, the threshold value t1A negative score may be determined. In the liability condition P8For example, even if the credibility of other indexes is 1, the total credit score of the college student still cannot meet the credit granting condition. Then P8X for negative extremum of reliability8Is shown to be
The weight w which has been calculated previously is usediAnd t1Substituting formula (3) to obtain: r is9＝-3.052。
P9And P15The calculation of the negative extreme of (a) is the same. As a result, r10＝-4.047， r15-2.001. Thus, r in Table 29、r10And r15The extreme values of (A) are-3.052, -4.047 and-2.001 respectively.
Specifically, the implementation process of the present invention is embodied in one embodiment;
the application information acquired by the applicant is as follows:
TABLE 4 personal basic credit information table for college students
The corresponding credit comprehensive evaluation and credit granting decision results are shown in the following table 5
TABLE 5
From the final credit evaluation decision result, the university student is finally identified as a good client, and through application, from the weight distribution of 15 indexes, the larger weight is P1(0.1514)、P3 (0.1087)、P6(0.0939)、P11(0.0699)、P12(0.0782) and P15(0.0686) indicating that the quality of a college student's family condition determines the score of his personal credit. Last few P2 (0.0232)、P8(0.0355), valence P13(0.0394) and P14(0.0343) the influence on individual credit scores is relatively small, but still reflects some information of college students on credit risk, such as more diligent college students, later developmental efforts are also over-sufficient, which are all of good reference value to our decision analysis.
A second embodiment of the present invention provides a credit scoring decision analysis apparatus, including:
a loan request acquisition unit 201, configured to acquire a loan request, and acquire, according to the loan request, information of a current school and family of an applicant;
a vector generation unit 202 configured to generate a reliability vector and a loss-of-confidence vector of the applicant from the current-time information and the home information;
and a credit scoring decision model generating unit 203, configured to generate a credit scoring decision model according to the credibility vector and the loss credibility vector, where the credit scoring decision model is used to generate a loan decision result.
Preferably, the vector generation unit is specifically configured to:
generating a reliability matrix and a weight distribution vector according to the on-going information and the family information;
calculating the reliability matrix and the weight distribution vector to obtain the reliability vector;
and generating a loss confidence vector according to the false item number of the correction information.
Preferably, the credit score decision model generation unit is specifically configured to:
and differencing the credibility vector and the credibility vector to generate a credit scoring decision model.
Preferably, the system further comprises a credibility negative extreme value acquiring unit, which is used for acquiring the credibility negative extreme value of the applicant;
and when the applicant cannot reach the credit granting condition according to the negative extreme value of the credibility, generating a negative decision result.
A third embodiment of the present invention provides a credit scoring decision analysis device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a credit scoring decision analysis method as described in any one of the above.
A fourth embodiment of the present invention provides a readable storage medium, where a computer program is stored, where the computer program is executable by a processor of a device where the computer readable storage medium is located, so as to implement a credit score decision analysis method as described in any one of the above.
Based on the credit scoring decision analysis method, the credit scoring decision analysis device, the credit scoring decision analysis equipment and the credit scoring decision analysis storage medium, when a loan request is received, the current school information and the family information of an applicant are obtained, the credibility vector and the loss credibility vector of the applicant are obtained according to the current school information and the family information of the applicant, and a credit scoring decision model is established, and the credit scoring decision model can generate a decision whether to give a loan or not according to the current school information and the family information of the applicant.
Illustratively, the computer programs described in the third and fourth embodiments of the present invention may be partitioned into one or more modules, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program in the decision analysis device implementing a credit score. For example, the device described in the second embodiment of the present invention.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, said processor being the control center of said one credit scoring decision analysis method, using various interfaces and lines connecting the whole of said various parts implementing the one credit scoring decision analysis method.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of a credit scoring decision analysis method by running or executing the computer programs and/or modules stored in the memory and invoking the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, a text conversion function, etc.), and the like; the storage data area may store data (such as audio data, text message data, etc.) created according to the use of the cellular phone, etc. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein the implemented module, if implemented in the form of a software functional unit and sold or used as a stand-alone product, can be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for decision analysis of credit scoring, comprising:
obtaining a loan request, and obtaining the information of the applicant at school and the family information according to the loan request;
generating credibility vectors and credibility losing vectors of the applicant according to the on-going information and the family information;
and generating a credit scoring decision model according to the credibility vector and the loss credibility vector, wherein the credit scoring decision model is used for generating a loan decision result.
2. The method of claim 1, wherein the generating of the credibility vector and the credibility vector of the applicant according to the current school information and the family information specifically comprises:
generating a reliability matrix and a weight distribution vector according to the on-going information and the family information;
calculating the reliability matrix and the weight distribution vector to obtain the reliability vector;
and generating a loss confidence vector according to the false item number of the correction information.
3. The method of claim 1, wherein the generating a credit score decision model according to the credibility vector and the loss credibility vector specifically comprises:
and differencing the credibility vector and the credibility vector to generate a credit scoring decision model.
4. The method of claim 1, further comprising obtaining a negative limit of confidence of the applicant;
and when the applicant cannot reach the credit granting condition according to the negative extreme value of the credibility, generating a negative decision result.
5. A credit scoring decision analysis device, comprising:
the loan request acquisition unit is used for acquiring a loan request and acquiring the in-school information and the family information of an applicant according to the loan request;
a vector generating unit for generating credibility vectors and credibility loss vectors of the applicant according to the current information and the family information;
and the credit scoring decision model generating unit is used for generating a credit scoring decision model according to the credibility vector and the loss credibility vector, wherein the credit scoring decision model is used for generating a loan decision result.
6. The apparatus of claim 5, wherein the vector generation unit is specifically configured to:
generating a reliability matrix and a weight distribution vector according to the on-going information and the family information;
calculating the reliability matrix and the weight distribution vector to obtain the reliability vector;
and generating a loss confidence vector according to the false item number of the correction information.
7. The apparatus for decision analysis on credit score according to claim 5, wherein the credit score decision model generating unit is specifically configured to:
and differencing the credibility vector and the credibility vector to generate a credit scoring decision model.
8. The apparatus of claim 5, further comprising a confidence negative extremum obtaining unit for obtaining the confidence negative extremum of the applicant;
and when the applicant cannot reach the credit granting condition according to the negative extreme value of the credibility, generating a negative decision result.
9. A credit scoring decision analysis device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor executing the computer program to implement a credit scoring decision analysis method as claimed in any one of claims 1 to 4.
10. A storage medium storing a computer program executable by a processor of a device on which the computer-readable storage medium is stored to implement a method of decision analysis of credit scoring as claimed in any one of claims 1 to 4.
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