Patent Publication Number: US-2007124236-A1

Title: Credit risk profiling method and system

Description:
TECHNICAL FIELD  
      This disclosure relates generally to computer based credit risk profiling techniques and, more particularly, to methods and systems for process model approach to profiling credit risks.  
     BACKGROUND  
      Credits or loans, such as mortgages, credit cards, business loans, etc., are provided by financial institutions to individuals or other institutions in return for principal and interest payments. The credits or loans may have risk of being defaulted, which may cause certain losses for the financial institutions. To minimize the risk of defaulting, credit risk profiling may be used to analyze such risk based on a collection of a large amount of information on credit users.  
      Credit risk profiling may be performed by various techniques, such as empirical techniques, data mining techniques, or decision tree techniques, etc. For example, U.S. Pat. No. 6,513,018 issued to Culhane on Jan. 28, 2003, describes a statistical strategy for generating a credit score predictive of the likelihood of a desired performance result for a selected credit user. However, such conventional techniques often fail to address inter-correlation between various variables within the collected credit user information, especially at the time of generation and/or optimization of process models, to correlate certain credit user information to certain credit risks simultaneously.  
      Methods and systems consistent with certain features of the disclosed systems are directed to solving one or more of the problems set forth above.  
     SUMMARY OF THE INVENTION  
      One aspect of the present disclosure includes a method for a credit risk profiling system. The method may include establishing a credit risk process model indicative of interrelationships between one or more credit risks and a plurality of financial parameters and obtaining a set of values corresponding to the plurality of financial parameters. The method may also include calculating the values of the one or more credit risks simultaneously based upon the set of values corresponding to the plurality of financial parameters and the credit risk process model, presenting the values of the one or more credit risks, and simultaneously presenting financial return information.  
      Another aspect of the present disclosure includes a computer system. The computer may include a database containing data records associating one or more credit risks and a plurality of financial parameters and a processor. The processor may be configured to establish a credit risk process model indicative of interrelationships between the one or more credit risks and the plurality of financial parameters and to obtain a set of values corresponding to the plurality of financial parameters. The processor may also be configured to calculate the values of the one or more credit risks simultaneously based upon the set of values corresponding to the plurality of financial parameters and the credit risk process model, to present the values of the one or more credit risks, and to simultaneously present financial return information.  
      Another aspect of the present disclosure includes a computer-readable medium for use on a computer system configured to perform a credit risk profiling procedure, the computer-readable medium having computer-executable instructions for performing a method. The method may include establishing a credit risk process model indicative of interrelationships between one or more credit risks and a plurality of financial parameters and obtaining a set of values corresponding to the plurality of financial parameters. The method may also include calculating the values of the one or more credit risks simultaneously based upon the set of values corresponding to the plurality of financial parameters and the credit risk process model, presenting the values of the one or more credit risks, and simultaneously presenting financial return information. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       FIG. 1  is a block diagram of an exemplary credit risk profiling process environment consistent with certain disclosed embodiments;  
       FIG. 2  illustrates a block diagram of a computer system consistent with certain disclosed embodiments;  
       FIG. 3  illustrates a flowchart of an exemplary credit risk profiling model generation and optimization process consistent with certain disclosed embodiments; and  
       FIG. 4  shows an exemplary operational process consistent with certain disclosed embodiments. 
    
    
     DETAILED DESCRIPTION  
      Reference will now be made in detail to exemplary embodiments, which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts.  
       FIG. 1  illustrates a flowchart diagram of an exemplary credit risk profiling process modeling environment  100 . As shown in  FIG. 1 , a credit risk profiling (CRP) process model  104  may be established to build interrelationships between input parameters  102  and output parameters  106 . Input parameters  102  may include any appropriate type of data associated with a credit risk analysis application. For example, input parameters  102  may include information collected from credit users/customers and/or available public/private information about a credit user or a population of credit users. Input parameters  102  may also include historic and current credit information about credit customers.  
      Output parameters  106 , on the other hand, may correspond to certain credit risks or any other types of output parameters used by the particular credit risk analysis application. For example, output parameters  106  may include likelihood of repayment, credit level, the amount of credit to be granted, the duration for extending credit, and/or the financial return based on the credit risk, etc.  
      CRP process model  104  may include any appropriate type of mathematical or physical model indicating interrelationships between input parameters  102  and output parameters  106 . For example, CRP process model  104  may be a neural network based mathematical model that is trained to capture interrelationships between input parameters  102  and output parameters  106 . Other types of mathematic models, such as fuzzy logic models, linear system models, and/or non-linear system models, etc., may also be used.  
      CRP process model  104  may be trained and validated using data records collected from a particular application for which CRP process model  104  is established. That is, CRP process model  104  may be established according to particular rules corresponding to a particular type of model using the data records, and the interrelationships of CRP process model  104  may be verified by using part of the data records. After CRP process model  104  is established, values of input parameters  102  may be provided to CRP process model  104  to predict values of output parameters  106  based on given values of input parameters  102  and the interrelationships.  
      After CRP process model  104  is trained and validated, CRP process model  104  may be optimized to define a desired input space of input parameters  102  and/or a desired distribution of output parameters  106 . For example, CRP process model  104  may define limited ranges of input parameters  102  corresponding to certain credit risks, such as levels or amount of credit. The validated or optimized CRP process model  104  may be used to produce corresponding values of output parameters  106  when provided with a set of values of input parameters  102 . For example, CRP process model  104  may be used to produce credit risk prediction  110  based on credit user data  108 .  
      The establishment and operations of CRP process model  104  may be carried out by one or more computer systems.  FIG. 2  shows a functional block diagram of an exemplary computer system  200  that may be used to perform these modeling processes and operations.  
      As shown in  FIG. 2 , computer system  200  may include a processor  202 , a random access memory (RAM)  204 , a read-only memory (ROM)  206 , a console  208 , input devices  210 , network interfaces  212 , a database  214 , and a storage  216 . It is understood that the type and number of listed devices are exemplary only and not intended to be limiting. The number of listed devices may be changed and other devices may be added.  
      Processor  202  may include any appropriate type of general purpose microprocessor, digital signal processor, or microcontroller. Processor  202  may execute sequences of computer program instructions to perform various processes as explained above. The computer program instructions may be loaded into RAM  204  for execution by processor  202  from read-only memory (ROM)  206 , or from storage  216 . Storage  216  may include any appropriate type of mass storage provided to store any type of information that processor  202  may need to perform the processes. For example, storage  216  may include one or more hard disk devices, optical disk devices, or other storage devices to provide storage space.  
      Console  208  may provide a graphic user interface (GUI) to display information to users of computer system  200 . Console  208  may include any appropriate type of computer display device or computer monitor. Input devices  210  may be provided for users to input information into computer system  200 . Input devices  210  may include a keyboard, a mouse, or other optical or wireless computer input devices, etc. Further, network interfaces  212  may provide communication connections such that computer system  200  may be accessed remotely through computer networks via various communication protocols, such as transmission control protocol/internet protocol (TCP/IP), hyper text transfer protocol (HTTP), etc.  
      Database  214  may contain model data and/or any information related to data records under analysis, such as training and testing data. Database  214  may include any type of commercial or customized database. Database  214  may also include analysis tools for analyzing the information in the database. Processor  202  may also use database  214  to determine and store performance characteristics of CRP process model  104 .  
      Processor  202  may perform a credit risk profiling model generation and optimization process to generate and optimize CRP process model  104 .  FIG. 3  shows an exemplary model generation and optimization process performed by processor  202 .  
      As shown in  FIG. 3 , at the beginning of the model generation and optimization process, processor  202  may obtain data records associated with input parameters  102  and output parameters  106  (step  302 ). The data records may include information characterizing one or more credit users and/or a population of credit users. For example, the data records may include demographic (e.g., gender, age, education, occupation, income, etc.), geographic, and/or psychographic information, etc., about the credit users. The data records may also include parameters related to financial factors of the credit users. For example, the data records may include purchase information, price, loan amount, default, default amount, current and past customer credit, and finance records, etc.  
      The data records may also be collected from experiments designed for collecting such data. Alternatively, the data records may be generated artificially by other related processes, such as other financial modeling or analysis processes. The data records may also include training data used to build CRP process model  104  and testing data used to validate CRP process model  104 . In addition, the data records may also include simulation data used to observe and optimize CRP process model  104 .  
      The data records may reflect characteristics of input parameters  102  and output parameters  106 , such as statistical distributions, normal ranges, and/or precision tolerances, etc. Once the data records are obtained (step  302 ), processor  202  may pre-process the data records to clean up the data records for obvious errors and to eliminate redundancies (step  304 ). Processor  202  may remove approximately identical data records and/or remove data records that are out of a reasonable range in order to be meaningful for model generation and optimization. After the data records have been pre-processed, processor  202  may select proper input parameters by analyzing the data records (step  306 ).  
      The data records may be associated with many input variables, such as any demographic, geographic, psychographic, and/or financial information, etc., about a credit user or users, from which input parameters  102  may be selected. The number of input variables may be greater than the number of input parameters  102  used for CRP process model  104 . For example, data records may be associated with a broad characteristics of personal and/or public information about certain credit users, such as personal habits, consumption habits, and/or financial habits, etc.; while input parameters  102  of a particular process, such as consumer credit, may only include certain number of the broad characteristics.  
      A large number of input variables may significantly increase computational time during generation and operations of the mathematical models. The number of the input variables may need to be reduced to create mathematical models within practical computational time limits. In certain situations, the number of input variables in the data records may exceed the number of the data records and lead to sparse data scenarios. Some of the extra input variables may have to be omitted in certain mathematical models such that practical mathematical models may be created based on reduced variable number.  
      Processor  202  may select input parameters  102  according to predetermined criteria. For example, processor  202  may choose input parameters  102  by experimentation and/or expert opinions. Alternatively, in certain embodiments, processor  202  may select input parameters based on a mahalanobis distance between a normal data set and an abnormal data set of the data records. The normal data set and abnormal data set may be defined by processor  202  using any appropriate method. For example, the normal data set may include characteristic data associated with input parameters  102  that produce desired output parameters. On the other hand, the abnormal data set may include any characteristic data that may be out of tolerance or may need to be avoided. The normal data set and abnormal data set may be predefined by processor  202 .  
      Mahalanobis distance may refer to a mathematical representation that may be used to measure data profiles based on correlations between parameters in a data set. Mahalanobis distance differs from Euclidean distance in that mahalanobis distance takes into account the correlations of the data set. Mahalanobis distance of a data set X (e.g., a multivariate vector) may be represented as 
 
 MD   i =( X   i −μ x )Σ −1 ( X   i −μ X )′  (1) 
 
 where μ x  is the mean of X and Σ −1  is an inverse variance-covariance matrix of X. MD i  weights the distance of a data point X i  from its mean μ x  such that observations that are on the same multivariate normal density contour will have the same distance. Such observations may be used to identify and select correlated parameters from separate data groups having different variances. 
 
      Processor  202  may select a desired subset of input parameters such that the mahalanobis distance between the normal data set and the abnormal data set is maximized or optimized. A genetic algorithm may be used by processor  202  to search input parameters  102  for the desired subset with the purpose of maximizing the mahalanobis distance. Processor  202  may select a candidate subset of input parameters  102  based on a predetermined criteria and calculate a mahalanobis distance MD normal  of the normal data set and a mahalanobis distance MD abnormal  of the abnormal data set. Processor  202  may also calculate the mahalanobis distance between the normal data set and the abnormal data (i.e., the deviation of the mahalanobis distance MD x =MD normal −MD abnormal ). Other types of deviations, however, may also be used.  
      Processor  202  may select the candidate subset of input variables  102  if the genetic algorithm converges (i.e., the genetic algorithm finds the maximized or optimized mahalanobis distance between the normal data set and the abnormal data set corresponding to the candidate subset). If the genetic algorithm does not converge, a different candidate subset of input variables may be created for further searching. This searching process may continue until the genetic algorithm converges and a desired subset of input variables (e.g., input parameters  102 ) is selected.  
      After selecting input parameters  102  (e.g., gender, age, education, occupation, income, health, location, credit history, financial records, etc.), processor  202  may generate CRP process model  104  to build interrelationships between input parameters  102  and output parameters  106  (step  308 ). In certain embodiments, CRP process model  104  may correspond to a computational model, such as, for example, a computational model built on any appropriate type of neural network. The type of neural network computational model that may be used may include back propagation, feed forward models, cascaded neural networks, and/or hybrid neural networks, etc. Particular types or structures of the neural network used may depend on particular applications. Other types of computational models, such as linear system or non-linear system models, etc., may also be used.  
      The neural network computational model (i.e., CRP process model  104 ) may be trained by using selected data records. For example, the neural network computational model may include a relationship between output parameters  106  (e.g., credit risks, amount of credit, credit score, financial returns, etc.) and input parameters  102  (e.g., gender, age, education, occupation, income, health, location, credit history, financial records, etc.). The neural network computational model may be evaluated by predetermined criteria to determine whether the training is completed. The criteria may include desired ranges of accuracy, time, and/or number of training iterations, etc.  
      After the neural network has been trained (i.e., the computational model has initially been established based on the predetermined criteria), processor  202  may statistically validate the computational model (step  310 ). Statistical validation may refer to an analyzing process to compare outputs of the neural network computational model with actual or expected outputs to determine the accuracy of the computational model. Part of the data records may be reserved for use in the validation process.  
      Alternatively, processor  202  may also generate simulation or validation data for use in the validation process. This may be performed either independently of a validation sample or in conjunction with the sample. Statistical distributions of inputs may be determined from the data records used for modeling. A statistical simulation, such as Latin Hypercube simulation, may be used to generate hypothetical input data records. These input data records are processed by the computational model, resulting in one or more distributions of output characteristics. The distributions of the output characteristics from the computational model may be compared to distributions of output characteristics observed in a population. Statistical quality tests may be performed on the output distributions of the computational model and the observed output distributions to ensure model integrity.  
      Once trained and validated, CRP process model  104  may be used to predict values of output parameters  106  when provided with values of input parameters  102 . Further, processor  202  may optimize CRP process model  104  by determining desired distributions of input parameters  102  based on relationships between input parameters  102  and desired distributions of output parameters  106  (step  312 ). In particular, processor  202  may analyze the relationships between desired distributions of input parameters  102  and desired distributions of output parameters  106  based on particular applications.  
      For example, processor  202  may select desired ranges for output parameters  106  (e.g., favorable credit score, and/or desired amount of credit, etc.). Processor  202  may then run a simulation of the computational model to find a desired statistic distribution for an individual input parameter (e.g., gender, age, education, occupation, income, health, location, credit history, financial records, etc.). That is, processor  202  may separately determine a distribution (e.g., mean, standard variation, etc.) of the individual input parameter corresponding to the normal ranges of output parameters  106 . After determining respective distributions for all individual input parameters, processor  202  may then analyze and combine the desired distributions for all the individual input parameters to determine desired distributions and characteristics for overall input parameters  102 .  
      Alternatively, processor  202  may identify desired distributions of input parameters  102  simultaneously to maximize the possibility of obtaining desired outcomes. In certain embodiments, processor  202  may simultaneously determine desired distributions of input parameters  102  based on zeta statistic. Zeta statistic may indicate a relationship between input parameters, their value ranges, and desired outcomes. Zeta statistic may be represented as  
         ζ   =       ∑   1   j     ⁢       ∑   1   i     ⁢            S   ij          ⁢     (       σ   i         x   _     i       )     ⁢     (         x   _     j       σ   j       )             ,       
 
 where  x   i  represents the mean or expected value of an ith input;  x   j  represents the mean or expected value of a jth outcome; σ i  represents the standard deviation of the ith input; σ j  represents the standard deviation of the jth outcome; and |S ij | represents the partial derivative or sensitivity of the jth outcome to the ith input. 
 
      Under certain circumstances,  x   i  may be less than or equal to zero. A value of 3σ i  may be added to  x   i  to correct such problematic condition. If, however,  x   i  is still equal zero even after adding the value of 3σ i , processor  202  may determine that σ i  may be also zero and that the process model under optimization may be undesired. In certain embodiments, processor  202  may set a minimum threshold for σ i  to ensure reliability of process models. Under certain other circumstances, σ j  may be equal to zero. Processor  202  may then determine that the model under optimization may be insufficient to reflect output parameters within a certain range of uncertainty. Processor  202  may assign an indefinite large number to ζ.  
      Processor  202  may identify a desired distribution of input parameters  102  such that the zeta statistic of the neural network computational model (i.e., CRP process model  104 ) is maximized or optimized. An appropriate type of genetic algorithm may be used by processor  202  to search the desired distribution of input parameters with the purpose of maximizing the zeta statistic. Processor  202  may select a candidate set of input parameters  102  with predetermined search ranges and run a simulation of CRP process model  104  to calculate the zeta statistic parameters based on input parameters  102 , output parameters  106 , and the neural network computational model. Processor  202  may obtain  x   i  and σ i  by analyzing the candidate set of input parameters  102 , and obtain  x   j  and σ j  by analyzing the outcomes of the simulation. Further, processor  202  may obtain |S ij | from the trained neural network as an indication of the impact of the ith input on the jth outcome.  
      Processor  202  may select the candidate set of input parameters if the genetic algorithm converges (i.e., the genetic algorithm finds the maximized or optimized zeta statistic of CRP process model  104  corresponding to the candidate set of input parameters). If the genetic algorithm does not converge, a different candidate set of input parameters  102  may be created by the genetic algorithm for further searching. This searching process may continue until the genetic algorithm converges and a desired set of input parameters  102  is identified. Processor  202  may further determine desired distributions (e.g., mean and standard deviations) of input parameters  102  based on the desired input parameter set.  
      As explained above, output parameters  106  may include likelihood of repayment, credit level, the amount of credit to be granted, the duration for extending credit, and/or the financial return based on the credit risk, etc. The desired distributions of input parameters  102  may be determined based on certain criteria corresponding to different parameters of output parameters  106 . For example, the desired distributions of input parameters  102  may be determined based on output parameter  106  that is to maximize the financial return. The desired distributions of input parameters  102  may also be determined based on output parameters  106  that is to balance between the likelihood of repayment (i.e., the risk of non-repayment) and the financial return. That is, the output parameters  106  may be optimized to achieve certain level of the financial return while having a desired level of risk of non-repayment. Other criteria, however, may also be used.  
      Once the desired distributions are determined, processor  202  may define a valid input space that may include any input parameter within the desired distributions (step  314 ). For example, processor  202  may determine that the desired distributions (i.e., desired input space) include a list of occupations, certain range of income, certain age groups, certain credit history, etc.  
      In one embodiment, statistical distributions of certain input parameters may be impossible or impractical to control. For example, an input parameter may be associated with a physical attribute of a credit user, such as age, or the input parameter may be associated with a constant variable within CRP process model  104  itself. These input parameters may be used in the zeta statistic calculations to search or identify desired distributions for other input parameters corresponding to constant values and/or statistical distributions of these input parameters.  
      Returning to  FIG. 1 , after CRP process model  104  is trained, validated, and optimized, the CRP process model may be used to predict one or more credit risks (i.e., credit risk prediction  110 ) in response to credit user data  108 .  FIG. 4  shows an exemplary operational process performed by processor  202 .  
      Processor  202  may obtain credit user data  108  (step  402 ). Processor  202  may obtain credit user data  108  directly from users of computer system  200 , from a database, or from other computer systems maintaining such data. Credit user data  108  may reflect any relevant information about a credit user or users, such as age, sex, education, occupation, income, health, location, credit history, financial records, etc. Processor  202  may store credit user data  108  in a database, such as database  214 , such that credit user data  108  may be available for operation.  
      After obtaining credit user data  108 , processor  202  may calculate credit risk predication  110  based on CRP process model  104  (step  404 ). For example, processor  202  may calculate credit risks, such as whether to give or extend credit, how much credit to extend, financial return on extended credit, the duration of extended credit, and/or credit rating (e.g., credit score, etc.), based on credit user data  108  and CRP process model  104 . For example, processor  202  may present the financial returns based on credit user data  108  to the users of computer system  200  (e.g., creditors, etc.).  
      Processor  202  may also calculate certain other statistics related to credit user data  108  and credit risk prediction  110 , such as distributions or histograms of such data. For example, processor  202  may present a distribution of the financial return corresponding to distributions of other parameters, such as credit user data  108  and/or credit prediction  110 .  
      Processor  202  may also present credit user data  108 , credit risk prediction  110 , and/or results of other calculation to the user or users of computer system  200  through a user interface (step  406 ). The user interface may include any appropriate textual, audio, and/or visual user interface. For example, the user interface may include a graphical user interface (GUI) on console  208 . Credit risk prediction and interrelationships (e.g., how a set of credit user data drive certain credit risks simultaneously) may also be presented to the users of computer system  200  or creditors. Such as the interrelationships between how much financial return, how much risk of non-repayment, and user credit data  108 , etc.  
      Alternatively, processor  202  may also directly communicate with one or more credit users corresponding to credit user data  108  to notify parts or all of credit risk prediction  110  to credit users whose data records meet certain criteria. For example, if credit risk prediction  110  indicates that credit should be extended to a particular credit user (i.e., processor  202  may determine that the calculated likelihood of repayment is beyond a predetermined threshold), processor  202  may automatically notify the particular credit user about certain information included in credit risk prediction  110 . Processor  202  may notify the particular credit user that a favorable credit decision (e.g., approval on extending credit, etc.). Processor  202  may also notify the particular credit user other information, such as amount of credit to be extended, the duration for extending such credit, etc., and/or relevant business information.  
      Processor  202  may also optimize credit risk prediction  110  (step  408 ). For example, processor  202  may minimize overall credit risks by obtaining desired distributions of credit user data  108 , such as desired income level, education level, age, and/or gender, credit history, etc. Processor  202  may optimize credit risk prediction  108  based on zeta statistic, as explained in above sections. A new set of values of credit user data  108  (i.e., optimized or desired credit user data) may be identified to minimize a certain type of credit risk. For example, the desired credit user data may be used to define a desired credit population. Other optimization methods, however, may also be used. For example, the user or users of computer system  200  may define a set of values of user credit data  108  (i.e., user-defined user credit data  108 ) based on predetermined criteria to minimize one or more credit risks.  
      After obtaining the desired set of values of credit user data, processor  202  may select desired credit user data records from the data base, such as database  214 , with values within a certain range of the desired set of values of credit user data (step  410 ). The selected credit user data records may correspond to credit users who may be considered suitable or desirable to extend credit to. Credit risk prediction  110  corresponding to the selected credit user data records may also be calculated by processor  202  and the results of such calculations may be presented, as explained above. Because the selected credit user data records may be within or closer to optimized credit user data  108 , credit risk prediction  110  corresponding to the selected credit user data records may also be with or closer to optimized credit user prediction  110 .  
     INDUSTRIAL APPLICABILITY  
      The disclosed systems and methods may provide efficient and accurate credit risk profiling based on a large variety of information such as personal information, public information, and/or financial factors (both current and historical). Such technology may be used to obtain an individual credit risk profile, the risk of an individual in paying back the credit extended. The technology may also be used to manage credit risks of a group or a population of credit customers.  
      Financial institutions or other organizations may use the disclosed systems and methods to calculate credit risks of an individual user or credit risks among a population, such as a particular credit risk distribution among the population, to reduce exposure to such risks. The institutional users may also optimize the credit risk distribution to reduce the credit risks of a population and/or to promote healthy financial behavior.  
      Credit users may also use the disclosed systems and methods to check potential credit risks before making a financial decision involving credit. The individual users may also be able to reduce the credit risks by changing relevant credit data (e.g., change the income or occupation) corresponding to the credit risks.  
      The disclosed systems and methods may also be extended to be used in non-financial field to predict or optimize other risks, such as credit risks, business risks, and/or other financial risks, etc. Parts of the disclosed system or steps of the disclosed method may be used by computer system providers to facilitate or integrate other process models.  
      Other embodiments, features, aspects, and principles of the disclosed exemplary systems will be apparent to those skilled in the art and may be implemented in various environments and systems.