FIN725 GDB 1 Solution and Discussion


  • Cyberian's Gold

    Total Marks 5
    Starting Date Tuesday, December 03, 2019
    Closing Date Sunday, December 08, 2019
    Status Open
    Question Title GDB No.1
    Question Description

    Discussion Question:

    Credit assessment helps banks in assessing and evaluating their potential borrowers in order to ascertain whether a potential borrower will be able to discharge his loan obligations as per contractual agreement with bank. There are many different credit assessment models commonly used in the market for standardized evaluation of credit assessment data, including heuristic models, empirical statistical models, and causal models. In practice, hybrid models are also used which combine two or three above mentioned models for credit assessment.

    Being a student of Credit and Risk Management (FIN725), you are required to give your opinion that whether heuristic or empirical statistical models are more suitable for the credit assessment of potential borrowers? Elaborate your answer by writing various features of heuristic and empirical statistical models.

    Important Instructions:

    1. Your discussion must be based on logical facts.

    2. The GDB will remain open for 06 working days.

    3. Do not copy or exchange your answer with other students. Two identical / copied comments will be marked Zero (0) and may damage your grade in the course.

    4. Obnoxious or ignoble answer should be strictly avoided.

    5. Questions / queries related to the content of the GDB, which may be posted by the students on MDB or via e-mail, will not be replied till the due date of GDB is over.

    NOTE: Copied Comments from any source will be marked as ZERO.

    Ø For Detailed Instructions please read the GDB Announcement


  • Cyberian's

    Case:

    Heuristic models attempt to take experiences and use them as a basis to methodically gain new insights. These experiences can stem from:

    • Conjectured business interrelationships

    • Subjective practical experiences and observations

    • Business theories related to specific aspects

    • In credit assessment, therefore, these models constitute an attempt to use experience in the lending business to make statements as to the future creditworthiness of a borrower.

    • The quality of heuristic models thus depends on how accurately they depict the subjective experience of credit experts.

    • Therefore, not only the factors relevant to creditworthiness are determined heuristically, but their influence and weight in overall assessments are also based on subjective experience.

    • In the development of these rating models, the factors used do not undergo statistical validation and optimization.

    • In practice, heuristic models are often grouped under the heading of expert systems

    Empirical statistical models, by contrast:

    • Try to assess a borrower’s credit standing on the basis of objectifying processes.
    • For this purpose, certain credit review criteria of the exposure under review are compared to the existing database which was established empirically.
    • This comparison makes it possible to classify the credit exposure.
    • The goodness of fit of an empirical statistical model depends to a great extent on the quality of the database used in developing the system. First, the database must be sufficiently large to allow significant findings.
    • In addition, it must be ensured that the data used also represent the credit institution’s future business adequately.

  • Cyberian's

    Case:

    Heuristic models attempt to take experiences and use them as a basis to methodically gain new insights. These experiences can stem from:

    • Conjectured business interrelationships

    • Subjective practical experiences and observations

    • Business theories related to specific aspects

    • In credit assessment, therefore, these models constitute an attempt to use experience in the lending business to make statements as to the future creditworthiness of a borrower.

    • The quality of heuristic models thus depends on how accurately they depict the subjective experience of credit experts.

    • Therefore, not only the factors relevant to creditworthiness are determined heuristically, but their influence and weight in overall assessments are also based on subjective experience.

    • In the development of these rating models, the factors used do not undergo statistical validation and optimization.

    • In practice, heuristic models are often grouped under the heading of expert systems

    Empirical statistical models, by contrast:

    • Try to assess a borrower’s credit standing on the basis of objectifying processes.
    • For this purpose, certain credit review criteria of the exposure under review are compared to the existing database which was established empirically.
    • This comparison makes it possible to classify the credit exposure.
    • The goodness of fit of an empirical statistical model depends to a great extent on the quality of the database used in developing the system. First, the database must be sufficiently large to allow significant findings.
    • In addition, it must be ensured that the data used also represent the credit institution’s future business adequately.

  • Cyberian's Gold

    Heuristics are the strategies derived from previous experiences with similar problems. These strategies depend on using readily accessible, though loosely applicable, information to control problem solving in human beings, machines and abstract issues.[3][4]

    The most fundamental heuristic is trial and error, which can be used in everything from matching nuts and bolts to finding the values of variables in algebra problems. In mathematics, some common heuristics involve the use of visual representations, additional assumptions, forward/backward reasoning and simplification.[5] Here are a few commonly used heuristics from George Pólya’s 1945 book, How to Solve It:[6]

        If you are having difficulty understanding a problem, try drawing a picture.
        If you can't find a solution, try assuming that you have a solution and seeing what you can derive from that ("working backward").
        If the problem is abstract, try examining a concrete example.
        Try solving a more general problem first (the "inventor's paradox": the more ambitious plan may have more chances of success).
    

    Reff

    @zareen said in FIN725 GDB 1 Solution and Discussion:

    features of heuristic and empirical statistical models

    Much research has highlighted incoherent implications of judgmental heuristics, yet other findings have demonstrated high correspondence between predictions and outcomes. At the same time, judgment has been well modeled in the form of as if linear models. Accepting the probabilistic nature of the environment, the authors use statistical tools to model how the performance of heuristic rules varies as a function of environmental characteristics. They further characterize the human use of linear models by exploring effects of different levels of cognitive ability. They illustrate with both theoretical analyses and simulations. Results are linked to the empirical literature by a meta-analysis of lens model studies. Using the same tasks, the authors estimate the performance of both heuristics and humans where the latter are assumed to use linear models. Their results emphasize that judgmental accuracy depends on matching characteristics of rules and environments and highlight the trade-off between using linear models and heuristics. Whereas the former can be cognitively demanding, the latter are simple to implement. However, heuristics require knowledge to indicate when they should be used.

    Copyright 2007 APA.



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