Rating Models and Validation - Oesterreichische Nationalbank

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Rating Models and Validation Chart 10: Rating Scale in the ÒManagementÓ Information Category for BVR-I Ratings 3.1.3 Expert Systems Expert systems are software solutions which aim to recreate human problemsolving abilities in a specific area of application. In other words, expert systems attempt to solve complex, poorly structured problems by making conclusions on the basis of Òintelligent behavior.Ó For this reason, they belong to the research field of artificial intelligence and are also often referred to as Òknowledge-based systems.Ó The essential components of an expert system are the knowledge base and the inference engine. 14 The knowledge base in these systems contains the knowledge acquired with regard to a specific problem. This knowledge is based on numbers, dates, facts and rules as well as ÒfuzzyÓ expert experience, and it is frequently represented using Òproduction rulesÓ (if/then rules). These rules are intended to recreate the analytical behavior of credit experts as accurately as possible. The inference engine links the production rules in order to generate conclusions and thus find a solution to the problem. The expert system outputs partial assessments and the overall assessment in the form of verbal explanations or point values. Additional elements of expert systems include: 15 Knowledge Acquisition Component As the results of an expert system depend heavily on the proper and up-to-date storage of expert knowledge, it must be possible to expand the knowledge base with new insights at all times. This is achieved by means of the knowledge acquisition component. Dialog Component The dialog component includes elements such as standardized dialog boxes, graphic presentations of content, help functions and easy-to-understand menu structures. This component is decisive in enabling users to operate the system effectively. 14 Cf. HEITMANN, C., Neuro-Fuzzy, p. 20ff. 15 Cf. BRUCKNER, B., Expertensysteme, p. 391. 36 Guidelines on Credit Risk Management
Explanatory Component The explanatory component makes the problem-solving process easier to comprehend. This component describes the specific facts and rules the system uses to solve problems. In this way, the explanatory component creates the necessary transparency and promotes acceptance among the users. Applied example: One example of an expert system used in banking practice is the system at Commerzbank: 16 The CODEX (Commerzbank Debitoren Experten System) model is applied to domestic small and medium-sized businesses. The knowledge base for CODEX was compiled by conducting surveys with credit experts. CODEX assesses the following factors for all borrowers: — Financial situation (using figures on the businessÕ financial, liquidity and income situation from annual financial statements) — Development potential (compiled from the areas of market potential, management potential and production potential) — Industry prospects. In all three areas, the relevant customer service representative or clerk at the bank is required to answer questions on defined creditworthiness factors. In this process, the user selects ratings from a predefined scale. Each rating option is linked to a risk value and a corresponding grade. These rating options, risk values and grades were defined on the basis of surveys conducted during the development of the system. A schematic diagram of how this expert system functions is provided in chart 11. Chart 11: How the CODEX Expert System Works 17 16 Cf. EIGERMANN, J., Quantitatives Credit-Rating unter Einbeziehung qualitativer Merkmale, p. 104ff. 17 Adapted from EIGERMANN, J., Quantitatives Credit-Rating unter Einbeziehung qualitativer Merkmale, p. 107. Rating Models and Validation Guidelines on Credit Risk Management 37
Page 1 and 2: ≈√ Guidelines on Credit Risk Ma
Page 3 and 4: The ongoing development of contempo
Page 5 and 6: 5 Developing a Rating Model 60 5.1
Page 7 and 8: Rating Models and Validation I INTR
Page 9 and 10: This segmentation from the business
Page 11 and 12: The best-practice segmentation pres
Page 13 and 14: Chart 2: Data Requirements for Gove
Page 15 and 16: 2.2 Financial Service Providers In
Page 17 and 18: Insurance Companies Due to their di
Page 19 and 20: Capital Market-Oriented/Internation
Page 21 and 22: — Market prospects are not assess
Page 23 and 24: Chart 5: Data Requirements for Corp
Page 25 and 26: elationships in the project, these
Page 27 and 28: Before the Project As the repayment
Page 29 and 30: Chart 6: Data Requirements for Reta
Page 31 and 32: During the Credit Term Instead of o
Page 33 and 34: 3.1 Heuristic Models Heuristic mode
Page 35: Chart 9: Information Categories for
Page 39 and 40: This example defines linguistic ter
Page 41 and 42: ments as to whether higher or lower
Page 43 and 44: Chart 16: Indicators in the ÒCrebo
Page 45 and 46: Logistic regression has a number of
Page 47 and 48: adapts the network according to any
Page 49 and 50: The parameters required to calculat
Page 51 and 52: ture of the borrowerÕs creditworth
Page 53 and 54: Chart 24: Vertical Linking of Ratin
Page 55 and 56: e necessary in this case if the def
Page 57 and 58: 4.1.5 Consistency Heuristic models
Page 59 and 60: Compared to heuristic models, stati
Page 61 and 62: the data set and statistical testin
Page 63 and 64: data collection procedure. This pro
Page 65 and 66: full surveys is often too high, esp
Page 67 and 68: essential structural characteristic
Page 69 and 70: For each block, interim objectives
Page 71 and 72: Chart 34: Creating the Analysis Dat
Page 73 and 74: Chart 35: Creating the Analysis and
Page 75 and 76: Once a quality-assured data set has
Page 77 and 78: an indicator should only be used in
Page 79 and 80: Transformation of Indicators In ord
Page 81 and 82: the scoring functions developed usi
Page 83 and 84: Chart 37: Significance of Quantitat
Page 85 and 86: — For all other statistical and h
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of approximately 10 intervals shoul
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With regard to the time interval be
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around the main diagonal, however,
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tial increase in marginal default r
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Chart 46: Aspects of Rating Model V
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— Model development procedure Mod
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and bad refer to whether a credit d
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numbers of cases per class observed
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Chart 54: Depiction of a and b erro
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Chart 57: Shape of the a—b Error
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Interpretation of the Pietra Index
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The following relation applies to t
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Chart 62: ROC Curve with Simultaneo
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Chart 65: Interpretation of the Bay
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The table below (chart 67) shows a
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The lower the Brier Score is, the b
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Chart 70 shows the reliability diag
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Chart 71: Identification of Signifi
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estimates, which means that it is e
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transition matrix, the data require
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tency of the cumulative and conditi
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to internal back-testing results in
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Changes in correlations One of the
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6.4.3 Developing Stress Tests This
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Counterparty-based and credit facil
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tify decisive factors influencing i
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III ESTIMATING AND VALIDATING LGD/E
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Chart 81: Loss Components in LGD to
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assets, as the danger exists that m
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— Collateral: Collateral value, c
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Chart 87: Overview of Customer Type
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transaction, however, it is also ne
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Implementing an in-house LGD estima
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nents; this is analogous to the use
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those assets which serve as collate
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during the realization period. The
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Chart 91: Example of Segmentation f
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ity of the realization market and t
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The level of utilization for off-ba
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8.3 EAD Estimation Methods As in th
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IV REFERENCES Backhaus, Klaus/Erich
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Stuhlinger, Matthias, Rolle von Rat
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Pytlik, Martin, Diskriminanzanalyse
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