KR20160149990A - Personal credit rating method and computer program combining credit grade and psychometric data - Google Patents

Abstract

The present invention relates to a personal credit rating device, a method thereof, and a computer program based on psychometric data. The present invention includes: a credit grading unit configured to set up a credit rating standard and rate credit rating about personal information; a psychometric data rating unit configured to analyze correlation between personal psychometric property and a credit risk so as to generate a psychometric rating standard and rate the psychometric rating for the psychometric data; and a comprehensive rating unit configured to combine credit rating rated by the credit grading unit and the psychometric rating rated by the psychometric data rating unit so as to calculate the final credit rating. According to the present invention, the present invention can miniaturize personal credit rating and accurately predict default.

Description

[0001] Personal credit rating method and computer program combining credit grade and psychometric data [

The present invention relates to a personal credit evaluation apparatus, method, and computer program based on psychometric measurement data, and more particularly, to a personal credit evaluation apparatus, method, and computer program based on psychometric measurement data, And a computer program.

When an individual wants to take out a loan, the credit rating is generally used as the most important evaluation criterion in loan review. Credit ratings are based on an individual's accumulated credit history. However, in the case of an individual who has a shortage of information on the accumulated credit history due to a short period of credit or a short period of credit, such a credit rating is disadvantageously applied, and the approval rate for such individuals is reduced.

In order to solve such problems, there is an effort to evaluate credit by additionally using information stored in a mobile phone, a Social Network Services (SNS) related record, and an Internet access record in addition to a credit rating. However, such information is difficult to be collected in a short time, and accumulated information is required for a certain period of time as in the case of a credit rating, so that it is difficult to apply the information immediately in an individual's credit evaluation.

In addition, in the credit evaluation, since the personal information such as the credit check history and the education level can not be used under the domestic laws and ordinances, there is a need for a method of evaluating an individual's credit on a variety of criteria in addition to the conventional credit rating.

Published Japanese Patent Application No. 10-2001-0096448 (November 11, 2001)

The present invention evaluates the credit rating based on information input from an individual who wants to receive the loan, evaluates the psychological rating based on the psychometric measurement data obtained through the psychometric questionnaire, and combines the evaluated credit rating and psychological rating A personal credit evaluation device, method and computer program capable of calculating an individual's credit rating.

According to an aspect of the present invention, there is provided a credit rating evaluating unit that sets a credit rating evaluation standard and evaluates a credit rating of individual information, a correlation between an individual's psychological characteristic and a credit risk, And a psychological measurement data evaluation unit for evaluating a psychological grade of the psychometric measurement data and a psychological evaluation level evaluated by the credit rating evaluation unit and the psychological measurement data evaluation unit to obtain a final credit And an overall evaluation section for calculating an evaluation grade.

According to another aspect of the present invention, there is provided a method for evaluating a user's personal information, comprising the steps of: setting a credit rating evaluation standard, evaluating a credit rating for information of an individual, generating a psychological evaluation criterion by analyzing a correlation between an individual's psychological characteristic and a credit risk Evaluating a psychological grade for the psychometric data, and combining the credit grade and the psychological grade to yield a final credit rating.

According to the present invention, since an individual's credit rating can be calculated based on a credit rating of an individual's information and a psychological rating of psychometric measurement data, individuals whose credit ratings are unfavorably applied due to lack of credit information can be easily It is possible to receive a loan audit and receive a loan without a security loan according to the calculated credit rating.

Further, according to the present invention, psychological measurement data can be created within a short period of time, so that the individual can minimize the psychological or temporal burden, and the financial institution can perform various kinds of research such as personal credit transaction, SNS related record, The result of the credit evaluation for the individual can be confirmed quickly regardless of the accumulation period or time.

Further, according to the present invention, since the credit evaluation is calculated by combining the characteristics of the individual, the willingness to repay the debt, and the repayment ability, the financial institution can obtain a credit evaluation result that is more refined and improved in discrimination about the individual. In addition, the financial institution can predict whether a default will occur based on the characteristics of the individual.

Further, according to the present invention, the financial institution can reduce the loan loss ratio while maintaining the loan approval rate or increase the loan approval rate while maintaining the loan loss ratio, based on the result of the individualized credit rating.

1 is a configuration diagram showing a personal credit evaluation apparatus according to an embodiment of the present invention;
Figure 2 is a flow chart illustrating a method for evaluating a credit rating in accordance with one embodiment of the present invention.
Figure 3 is a flow chart illustrating a method for evaluating psychometric data in accordance with an embodiment of the present invention.
Figure 4 - a flow chart illustrating a method of computing an individual's credit rating in accordance with one embodiment of the present invention;

The present invention relates to a psychological measurement data based personal credit evaluation apparatus, a method and a computer program. More specifically, the present invention relates to a personal credit evaluation apparatus, a method and a computer program, The present invention is applicable to all fields in which a credit evaluation for an individual is required. The personal credit evaluation device, method and computer program based on psychometric measurement data according to the present invention may be collectively referred to as Psychometrics-based Scoring System (PSS).

Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings.

FIG. 1 is a configuration diagram illustrating a personal credit evaluation apparatus according to an embodiment of the present invention.

1, the personal credit evaluation apparatus 10 according to the present invention may include an input unit 20, a credit evaluation calculation unit 30, and an output unit 40. [ The personal credit evaluation apparatus 10 according to the present invention may be a device operating in a financial institution to calculate a credit rating for an individual or may be a company or a public entity for providing a credit rating calculated for an individual as a financial institution Lt; / RTI > may be an apparatus included in a system operated by a user.

The input unit 20 receives predetermined information from an individual and receives information for evaluating a credit rating and psychological measurement data. The input unit 20 may be a keyboard or a touch pad that can be directly input by an individual. The input unit 20 may be a device that is connected to a computer, a tablet PC, a portable terminal, A financial institution or the like.

The output unit 40 is a device for requesting an individual to input certain information, and a list of psychological measurement questionnaires or a list of information to be collected for credit evaluation is output to the individual via the output unit 40. The output unit 40 may be a display unit that provides a screen directly to an individual, or may be a device that is connected to a computer, a tablet PC, a portable terminal, or the like used by an individual through an Internet network to transmit a list of information or a psychological measurement questionnaire, A list of information provided to an individual who visits the institution or the like, or a device in which a psychometric questionnaire is stored.

The credit evaluation calculating unit 30 may include a credit rating evaluating unit 31, a psychometric measurement data evaluating unit 32 and an overall evaluation unit 33. [ The credit evaluation calculator 30 can request the user to input predetermined information through the output unit 40 and calculate the individual credit rating according to the information input through the input unit 20. [ The credit rating calculated by the credit rating calculator 30 may be provided to a financial institution.

In more detail, the credit rating evaluating unit 31 sets a credit rating evaluation criterion, requests the information related to the credit rating evaluation criterion through the output unit 40, and, based on the credit rating information input from the individual Evaluate the credit rating.

The credit rating evaluating unit 31 can receive basic information such as occupation and residence type from the individual, income information such as disposable income per month, and can receive credit information provided by companies such as NICE / KCB Can be received.

The credit rating evaluating unit 31 does not use a conventionally used logistic regression model in setting a credit rating evaluation criterion and uses a statistically more advanced methodology such as a decision tree, A support vector machine, a neural network model, and the like, and can use a meta algorithm such as bootstrapping or bagging, bootstrap aggregating, and the like. Therefore, even when the number of evaluation items increases, the credit rating evaluator 31 can maintain a high predictive power and can improve the accuracy and stability in the regression analysis.

As described above, the credit rating evaluating unit 31 collects basic information, income information, or credit information of an individual and stores it in a decision tree, a random forest, a support vector machine ), A neural network model, bootstrapping, or bagging, to evaluate the individual's credit rating as a new evaluation standard.

The psychometric data evaluation unit 32 sets evaluation criteria for the psychometric data, provides the psychometric measurement questionnaire to the individual via the output unit 40, and outputs the psychometric measurement data, which is input as a response to the psychometric questionnaire, Evaluate the psychological grade accordingly.

The psychometric measurement data evaluating unit 32 can classify the characteristics of the individual into four categories: personality, attitude, mental health, and basic capability, Generate a psychometric questionnaire that can measure features. In order to measure the personality, sub-items include not only the Big 5 Personality Theory but also the delay of gratification, impulsiveness, sensation-seeking, overconfidence, self- control, locus of control, self-efficacy, optimism, antisocial personality, moral judgment, and altruism can be analyzed, Attitude to money, attitude to debt, attitude to credit transaction, and materialism can be analyzed as sub items to measure, and to measure mental health, In order to measure basic stress, stress, depression, anxiety, and emotional well-being can be analyzed, and the management of money, coping strategy, cognitive ability ntellectual capacity, and financial knowledge can be analyzed. The intelligence to supplement the analysis of the psychological variables may be the Digit Span Recall test or the Ravens Progressive Matrix test.

Since the psychometric measurement data evaluation unit 32 is not limited to the psychometric measurement based on the Riccart scale, a picture or a game can be used for the psychometric questionnaire. Therefore, the psychometric questionnaire can be composed of a multiple choice, simple sentence or image as an option.

For example, the psychometric survey questionnaire can be used to analyze the psychological measurement questionnaire, such as personal value pattern, work life type, negotiation pattern, saving / spending pattern, rest pattern, consumption pattern, decision pattern, interpersonal pattern, The psychological measurement data evaluating unit 32 may include a psychological measurement questionnaire as shown in Table 1 below to the individual. The psychological measurement data may include a question about the individual's psychological change, the situation recognition pattern, the problem solving pattern, can do.

One Which of the following behaviors could be considered an adult? 2 How do you want to be recognized at work? 3 How do you start negotiating? 4 Is it a saving or consuming type? 5 Under what circumstances does the spending increase? 6 What do you do when you want to rest? 7 How often do you buy good stuff? 8 How much information do you need to change your mind? 9 What do you do after a big fight with a friendly person? 10 Which of the following images is the most annoying? 11 How did you feel when you heard that you were going to the moon? 12 What if I can not choose? 13 What does the first thing happen when something breaks in the house? 14 What type of party is it? 15 Do you know how much money you have in your account? 16 How does someone respond if you insult you? 17 What do you do when you have change? 18 What is the mood when things go wrong? 19 How serious do you play in the game?

The psychometric measurement data evaluating unit 32 quantifies inputted psychometric measurement data and analyzes a correlation between psychological characteristics of an individual and psychological characteristics of the individual based on the psychometric data.

Two methods can be used to analyze the correlation between individual psychological characteristics and credit risk. First, a retrospective analysis method that confirms the correlation between psychological characteristics and credit risk by inputting psychological measurement data from a good loaner and a bad loaner among individuals who have received a conventional loan can be used. Second, A prospective method of analyzing the correlation between psychological characteristics and credit risk can be used by tracking the borrower's redemption records based on psychometric data obtained from new borrowers. In the former case, the accuracy of the correlation analysis decreases, but the correlation can be grasped quickly. In the latter case, it takes a long time to grasp the correlation, but the accuracy of the correlation analysis can be improved. The psychological measurement data evaluating unit 32 can confirm the correlation between the psychological characteristic and the credit risk by using at least one of a retrospective analysis method and a prospective analysis method. If the two methods are used in a complementary manner The correlation can be confirmed more accurately.

The psychometric measurement data evaluation unit 32 can use a Bayesian hierarchical logit model or a machine learning technique in analyzing a correlation between an individual's psychological characteristics and credit risk, It is possible to prevent overfitting problems that may arise due to shortage, and to create a safer psychological evaluation standard by preventing the effect of the evaluation item from being exposed.

A correlation between the psychological characteristics of the individual and the credit risk analyzed by the psychometric measurement data evaluation unit 32 is generated as a psychological evaluation standard so that the psychological rating of the individual is evaluated. The evaluation criteria can be verified through discriminant power, stability, and sequencing based on indicators such as K-S statistics, Population Stability Index (PSI), and Score Distribution Report (SDR).

The psychometric measurement data evaluator 32 accumulates psychometric data for a plurality of individuals and statistically analyzes the correlation between the individual's psychological characteristics and credit risk to provide a more reliable psychological rating .

The comprehensive evaluation unit 33 combines the credit rating evaluated by the credit rating evaluation unit 31 and the psychological rating evaluated by the psycho measurement data evaluation unit 32 to calculate the final credit rating. The comprehensive evaluation unit 33 can calculate the credit rating more precisely by combining additional conditions with the final credit rating calculated by combining the credit rating and the psychological rating. For example, it is possible to combine conditions that can be overturned for a class corresponding to automatic approval of a loan or conditions for which a decision can be reversed for a class corresponding to an automatic denial of a loan. And the like may be used as additional conditions.

Therefore, according to the present invention, individuals having the same credit rating can be further subdivided, so that it is possible to more accurately predict whether an individual defaults on default.

2 is a flow diagram illustrating a method for evaluating a credit rating in accordance with an embodiment of the present invention.

As shown in FIG. 2, the credit rating evaluating unit 31 receives information necessary for evaluating the credit rating through the input unit 20 (S50). For example, basic information such as occupation, type of housing, income information such as monthly disposable income, and credit information provided by a company such as NICE / KCB can be input.

The credit rating evaluation unit 31 applies a credit rating evaluation standard to the input information (S51). For example, methods such as a decision tree, a random forest, a support vector machine, a neural network model, bootstrapping, or bagging may be applied. The credit rating evaluating unit 31 according to the present invention does not use a logistic regression model.

Next, the credit rating evaluating unit 31 evaluates the individual's credit rating according to the input information (S52). The evaluated credit rating may not be disclosed to the individual. In addition, the credit rating evaluating unit 31 may use various filtering to evaluate an individual's credit rating. For example, the credit rating evaluating unit 31 may use the age, the provided credit rating, and the repayment rate as criteria for calculating the credit rating .

3 is a flow chart illustrating a method for evaluating psychometric data in accordance with an embodiment of the present invention.

3, the psychometric measurement data evaluator 32 provides the psychological measurement questionnaire to the individual, and inputs the psychometric measurement data through the input unit 20 (S60). It primarily receives psychometric data related to an individual's personality, intelligence, or honesty.

For example, the psychological measurement questionnaire is composed of the Big 5 Personality Theory, delay of gratification, impulsiveness, sensation-seeking, overconfidence, self control ), Control (locus of control), self-efficacy, optimism, antisocial personality, moral judgment, altruism, attitude to money, debt Attitude to credit, attitude to credit transaction, materialism, stress, depression, anxiety, emotional well-being, money management management of money, coping strategy, intellectual capacity, and financial knowledge.

In addition, the psychometric questionnaire can be generated based on a Digit Span Recall test or a Ravens Progressive Matrix test, and the psychometric data can be collected based on the individual's consent. have.

Next, the psychometric measurement data evaluating unit 32 converts the input psychometric measurement data into a numerical value (S61), and evaluates the psychological grade of the individual according to the psychological evaluation standard (S62). The psychometric evaluation criterion is generated on the basis of a correlation between the psychological characteristic of the individual and the credit risk analyzed by the psychometric data evaluation unit 32.

4 is a flow diagram illustrating a method for calculating an individual's credit rating in accordance with one embodiment of the present invention.

The overall evaluation unit 33 combines the credit rating evaluated in step S52 of FIG. 2 and the psychological rating evaluated in step S62 of FIG. 3 to calculate the final credit rating of the individual (S70).

The comprehensive evaluation unit 33 may calculate the final credit rating by combining additional conditions on the result of combining the credit rating and the psychological rating. For example, the final credit rating calculated by the comprehensive evaluation unit 33 may be classified into 1 to 10 grades. If the credit rating is calculated as 1 to 4 grades, , It can be set as the subject of precision examination and automatic rejection in the case of 8 to 10 grades. In the case of customers who are subject to close scrutiny, they can only be approved for loan if they pass the pre-determined examination criteria. If they meet the conditions for automatic approval high-side override , The credit rating may change, and if the auto-rejection target meets the Low-side Override criteria, the credit rating will be changed so that the rating corresponding to the automatic approval or the close examination And may be finally calculated.

The personal credit evaluation method according to the present invention is implemented as a processor-readable code on a processor-readable recording medium provided in a machine, a computer, or an integrated control device used by a financial institution, a credit evaluation company or a public institution It is possible. The processor-readable recording medium includes all kinds of recording apparatuses in which data that can be read by the processor is stored. Examples of the recording medium readable by the processor include a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like, and also a carrier wave such as transmission over the Internet. In addition, the processor readable recording medium may be distributed over networked computer systems so that code readable by the processor in a distributed manner can be stored and executed.

As described above, the present invention relates to a personal credit evaluation apparatus, a method, and a computer program for calculating an individual's credit rating. By calculating a personal credit rating based on a new credit rating standard and psychological measurement data, Financial institutions can reduce the loan loss ratio to individuals or increase the loan approval rate because it is possible to classify the individual who wants to borrow more finely and predict more accurately whether an individual defaults.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the inventive concept as defined by the appended claims and their equivalents. But is not limited thereto.

Claims (2)

Establishing a credit rating evaluation standard by the credit rating evaluation unit and evaluating a credit rating for information of an individual including at least one of a job, a housing type, a monthly disposable income, and personal credit information;
By the psychometric measurement data evaluation section,
Using the Bayesian Hierarchical Logit Model or Machine Learning method, the psychological evaluation criterion is generated by analyzing the correlation between psychological characteristics of individuals and credit risk,
The psychological evaluation criterion is verified based on a KS statistic, a Population Stability Index (PSI) or a Score Distribution Report (SDR)
The results of this study are as follows. First, the psychological change of the individual according to the change of situation, the preference or psychological change for the predetermined image, the personal value pattern, the work life style, the negotiation pattern, the saving / Wherein the psychometric measurement questionnaire is a Digit Span Recall test or a Ravens Progressive Matrix test, the psychological measurement questionnaire comprising at least one of a Situation Awareness Pattern, a Problem Solving Pattern and an Emotion Control Pattern, ) Is supplemented by an analysis of intelligence,
Evaluating the psychological class using psychometric measurement data obtained from an individual's response to the psychometric questionnaire; And
And a step of calculating a final credit rating by combining the credit rating evaluated by the credit rating evaluation unit and the psychological rating evaluated by the psychological measurement data evaluation unit, Based personal credit rating method. A computer-readable recording medium having recorded thereon a computer program for performing the personal rating method based on the psychometric measurement data set forth in claim 1.

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