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Table 0: Loan Origination Result If: | 0| 1| 2| 3| 4| 5| Routing is Decline | Y| N| N| N| N| N| Routing is Refer | -| Y| Y| Y| N| N| Routing is Accept | -| -| -| -| Y| N| Adjudication is Decline | -| Y| N| N| -| -| Adjudication is Accept | -| -| Y| N| -| -| Then: Loan_Origination_Result is Decline | X| X| | | | | Loan_Origination_Result is Accept | | | X| | X| | Loan_Origination_Result is Undecided | | | | X| | | 'Additional info' | X| X| X| X| X| | # ....... GoalAttribute: Loan_Origination_Result Case: Decline Print: "Loan Origination Result is: Decline" Case: Accept Print: "Loan Origination Result is: Accept" Case: Undecided Print: "Loan Origination Result is: Undecided" Attribute: Adjudication Askable_using: "What is the final adjudication?" Table 1: Routing - Strategy = Bureau If: | 0| 1| 2| 3| 4| 5| Strategy is Bureau | Y| Y| Y| Y| Y| N| Post_Bureau_Risk_Category is High | Y| N| N| N| N| -| Post_Bureau_Affordability is True | -| Y| Y| Y| N| -| Banktrupt is True | -| Y| N| N| -| -| Credit_Score < 580 | -| -| Y| N| -| -| Then: Routing is Decline | | X| | | X| | Routing is Refer | X| | X| | | | Routing is Accept | | | | X| | | # ....... Table 2: Routing - Strategy <> Bureau If: | 0| 1| 2| Strategy is Decline | Y| N| N| Strategy is Through | -| Y| N| Then: Routing is Decline | X| | | Routing is Accept | | X| | # ....... rTable 3: Post Bureau Affordability If: | 0| Post_Bureau_Affordability_Formula = 1.0 | Y| Then: Post_Bureau_Affordability is True | X| # ....... NB: based on pythonic logic: float(7<8) = 1.0 ; float(7<6) = 0.0 Attribute: Post_Bureau_Affordability_Formula Derived_from_formula: Required_Monthly_Installment < Disposable_Income * Post_Bureau_Credit_Contingency_Factor Table 4: Post Bureau Credit Contingency Factor If: | 0| 2| 3| 4| 5| Post_Bureau_Risk_Category is High | Y| N| N| N| N| Post_Bureau_Risk_Category is Medium | -| Y| N| N| N| Post_Bureau_Risk_Category is Low | -| -| Y| N| N| Post_Bureau_Risk_Category is Very-Low | -| -| -| Y| N| Then: Post_Bureau_Credit_Contingency_Factor = 0.6 | X| | | | | Post_Bureau_Credit_Contingency_Factor = 0.7 | | X| | | | Post_Bureau_Credit_Contingency_Factor = 0.8 | | | X| X| | # ....... Attribute: Banktrupt Askable_using: "Is this a case of bankruptcy?" Attribute: Credit_Score Askable_using: "What is the Credit Score (>=580 is Accept)" Table 5: Strategy If: | 0| 1| 2| 3| 4| Eligibility is Eligible | Y| Y| Y| Y| N| Bureau_Call_Type is FULL | Y| N| N| N| -| Bureau_Call_Type is MINI | -| Y| N| N| -| Bureau_Call_Type is NONE | -| -| Y| N| -| Then: Strategy is Bureau | X| X| | | | Strategy is Through | | | X| | | Strategy is Decline | | | | | X| # ....... rTable 6: Eligibility If: | 0| Pre_Bureau_Risk_Category is Decline | N| Pre_Bureau_Affordability is True | Y| Age < 18 | N| Then: Eligibility is Eligible | X| # ....... rTable 7: Pre Bureau Affordability If: | 0| Pre_Bureau_Affordability_Formula = 1.0 | Y| Then: Pre_Bureau_Affordability is True | X| # ....... Attribute: Pre_Bureau_Affordability_Formula Derived_from_formula: Required_Monthly_Installment < Disposable_Income * Pre_Bureau_Credit_Contingency_Factor Attribute: Required_Monthly_Installment Type: Real Derived_from_formula: PMT + Monthly_Fee Attribute: PMT Type: Real Derived_from_formula: (Amount*Rate/12)/(1-(1+Rate/12)**-Term) # alternative (same result): abs(pmt(Rate/12, Term, Amount)) # NB: Function pmt imported from numpy; all functions from the math library can also be used. # NB2: backslash (\) is a continuation character for formulas (over multiple lines). Attribute: Rate Type: Real Askable_using: "What is the Rate (notation 0.0000) of the requested product?" Attribute: Term Type: Integer Askable_using: "What is the Term (in months) of the requested product?" Attribute: Amount Type: Real Askable_using: "What is the Amount of the requested product?" Attribute: Disposable_Income Type: Real Derived_from_formula: Monthly_Income - (Monthly_Repayments + Monthly_Expenses) Attribute: Monthly_Income Type: Real Askable_using: "What is the Monthly_Income?" Attribute: Monthly_Repayments Type: Real Askable_using: "What is the Monthly_Repayments?" Attribute: Monthly_Expenses Type: Real Askable_using: "What is the Monthly_Expenses?" rTable 8: Product Type If: | 0| 1| Product_Type is Standard_Loan | Y| N| Product_Type is Special_Loan | -| Y| Then: Monthly_Fee = 20.00 | X| | Monthly_Fee = 25.00 | | X| # ....... Attribute: Product_Type Askable_using: "What is the product type of the requested product?" Table 9: Pre Bureau Credit Contingency Factor If: | 0| 1| 2| 3| 4| 5| Pre_Bureau_Risk_Category is High | Y| N| N| N| N| N| Pre_Bureau_Risk_Category is Decline | -| Y| N| N| N| N| Pre_Bureau_Risk_Category is Medium | -| -| Y| N| N| N| Pre_Bureau_Risk_Category is Low | -| -| -| Y| N| N| Pre_Bureau_Risk_Category is Very-Low | -| -| -| -| Y| N| Then: Pre_Bureau_Credit_Contingency_Factor = 0.6 | X| X| | | | | Pre_Bureau_Credit_Contingency_Factor = 0.7 | | | X| | | | Pre_Bureau_Credit_Contingency_Factor = 0.8 | | | | X| X| | # ....... Table 10: Bureau Call Type If: | 0| 1| 2| 3| 4| 5| Pre_Bureau_Risk_Category is High | Y| N| N| N| N| N| Pre_Bureau_Risk_Category is Medium | -| Y| N| N| N| N| Pre_Bureau_Risk_Category is Low | -| -| Y| N| N| N| Pre_Bureau_Risk_Category is Very-Low | -| -| -| Y| N| N| Pre_Bureau_Risk_Category is Decline | -| -| -| -| Y| N| Then: Bureau_Call_Type is FULL | X| X| | | | | Bureau_Call_Type is MINI | | | X| | | | Bureau_Call_Type is NONE | | | | X| X| | # ....... rTable 11: Post Bureau Risk Category: existing customer. If: | 0| 1| 2| 3| 4| 5| Existing_Customer is True | Y| Y| Y| Y| Y| Y| Application_Risk_Score <= 100 | Y| Y| Y| N| N| N| Credit_Score < 580 | Y| N| N| -| -| -| Credit_Score <= 600 | -| Y| N| -| -| -| Credit_Score < 590 | -| -| -| Y| N| N| Credit_Score <= 615 | -| -| -| -| Y| N| Then: Post_Bureau_Risk_Category is High | X| | | X| | | Post_Bureau_Risk_Category is Medium | | X| | | X| | Post_Bureau_Risk_Category is Low | | | X| | | X| # ....... rTable 12: Post Bureau Risk Category: no existing customer. If: | 0| 1| 2| 3| 4| 5| 6| Existing_Customer is True | N| N| N| N| N| N| N| Application_Risk_Score < 120 | Y| Y| Y| N| N| N| N| Application_Risk_Score <= 130 | -| -| -| Y| Y| Y| N| Credit_Score < 590 | Y| N| N| -| -| -| -| Credit_Score <= 610 | -| Y| N| -| -| -| -| Credit_Score < 600 | -| -| -| Y| N| N| -| Credit_Score <= 625 | -| -| -| -| Y| N| -| Then: Post_Bureau_Risk_Category is High | X| | | X| | | | Post_Bureau_Risk_Category is Medium | | X| | | X| | | Post_Bureau_Risk_Category is Low | | | X| | | X| | Post_Bureau_Risk_Category is Very-Low | | | | | | | X| # ....... rTable 13: Pre Bureau Risk Category: existing customer. If: | 0| 1| 2| 3| Existing_Customer is True | Y| Y| Y| Y| Application_Risk_Score < 80 | Y| N| N| N| Application_Risk_Score < 90 | -| Y| N| N| Application_Risk_Score <= 110 | -| -| Y| N| Then: Pre_Bureau_Risk_Category is Decline | X| | | | Pre_Bureau_Risk_Category is High | | X| | | Pre_Bureau_Risk_Category is Medium | | | X| | Pre_Bureau_Risk_Category is Low | | | | X| # ....... rTable 14: Pre Bureau Risk Category: no existing customer. If: | 0| 1| 2| 3| Existing_Customer is True | N| N| N| N| Application_Risk_Score < 100 | Y| N| N| N| Application_Risk_Score < 120 | -| Y| N| N| Application_Risk_Score <= 130 | -| -| Y| N| Then: Pre_Bureau_Risk_Category is High | X| | | | Pre_Bureau_Risk_Category is Medium | | X| | | Pre_Bureau_Risk_Category is Low | | | X| | Pre_Bureau_Risk_Category is Very-Low | | | | X| # ....... Attribute: Existing_Customer Askable_using: "Applicant is an existing customer?" Attribute: Application_Risk_Score Summation_of: Age_Risk_Score + Marital_Risk_Score + Employment_Risk_Score Table 15: Age Risk If: | 0| 1| 2| 3| 4| 5| Age < 18 | Y| N| N| N| N| N| Age <= 21 | -| Y| N| N| N| N| Age <= 25 | -| -| Y| N| N| N| Age <= 35 | -| -| -| Y| N| N| Age <= 49 | -| -| -| -| Y| N| Then: Age_Risk_Score = 0 | X| | | | | | Age_Risk_Score = 32 | | X| | | | | Age_Risk_Score = 35 | | | X| | | | Age_Risk_Score = 40 | | | | X| | | Age_Risk_Score = 43 | | | | | X| | Age_Risk_Score = 48 | | | | | | X| # ....... Attribute: Age Askable_using: "What is the age of the applicant?" rTable 16: Marital Risk If: | 0| 1| Marital_Status is S | Y| N| Marital_Status is M | -| Y| Then: Marital_Risk_Score = 25 | X| | Marital_Risk_Score = 45 | | X| # ....... Attribute: Marital_Status Askable_using: "What is the marital status of the applicant?" Table 17: Employment Risk If: | 0| 1| 2| 3| 4| Employment_Status is Unemployed | Y| N| N| N| N| Employment_Status is Student | -| Y| N| N| N| Employment_Status is Employed | -| -| Y| N| N| Employment_Status is Self-Employed | -| -| -| Y| N| Then: Employment_Risk_Score = 15 | X| | | | | Employment_Risk_Score = 18 | | X| | | | Employment_Risk_Score = 45 | | | X| | | Employment_Risk_Score = 36 | | | | X| | # ....... Attribute: Employment_Status Askable_using: "What is the employment status of the applicant?" GoalProposition: 'Additional info' Print: "***************************************************************************************" Print: "Adjudication is: %s" Adjudication Print: "Routing is: %s" Routing Print: "Strategy is: %s" Strategy Print: "Bureau Call Type: %s" Bureau_Call_Type Print: "Eligibility: %s" Eligibility Print: "Pre Bureau Affordability: %s" Pre_Bureau_Affordability Print: "Pre Bureau Risk Category: %s" Pre_Bureau_Risk_Category Print: "Existing Customer: %s" Existing_Customer Print: "Application Risk Score: %s" Application_Risk_Score Print: "= Age_Risk_Score(%s)+ Marital_Risk_Score(%s)+ Employment_Risk_Score(%s)" Age_Risk_Score Marital_Risk_Score Employment_Risk_Score Print: "Based on Age (%s), Marital Status (%s) and Employment Status (%s)" Age Marital_Status Employment_Status Print: "Pre Bureau Affordability Formula: %s" Pre_Bureau_Affordability_Formula Print: "Required Monthly Installment: %s" Required_Monthly_Installment Print: "PMT: %s ; based on Amount: %s, Rate: %s, Term (in months): %s" PMT Amount Rate Term Print: "Monthly Fee = %s, because Product Type is: %s" Monthly_Fee Product_Type Print: "Disposable Income: %s" Disposable_Income Print: ".. is Monthly Income: %s - (Monthly Repayments: %s + Monthly Expenses: %s)" Monthly_Income Monthly_Repayments Monthly_Expenses Print: "Pre Bureau Credit Contingency Factor: %s" Pre_Bureau_Credit_Contingency_Factor Print: "Post Bureau Affordability: %s" Post_Bureau_Affordability Print: "Post Bureau Risk Category: %s" Post_Bureau_Risk_Category Print: "Post Bureau Affordability Formula: %s" Post_Bureau_Affordability_Formula Print: "Post Bureau Credit Contingency Factor: %s" Post_Bureau_Credit_Contingency_Factor Print: "Bureau data: Banktrupt: %s" Banktrupt Print: "Bureau data: Credit Score: %s" Credit_Score Print: "***************************************************************************************"

programstatus: sourcewait

dt5gl-v3.2 rel.20/05/20 - 2020-07-28 08:05:47. All decision tables (except the rTables) are checked and ok. GoalAttribute: Loan_Origination_Result Attribute: Adjudication Attribute: Post_Bureau_Affordability_Formula Attribute: Banktrupt Attribute: Credit_Score Attribute: Pre_Bureau_Affordability_Formula Attribute: Required_Monthly_Installment Type: Real Attribute: PMT Type: Real Attribute: Rate Type: Real Attribute: Term Type: Integer Attribute: Amount Type: Real Attribute: Disposable_Income Type: Real Attribute: Monthly_Income Type: Real Attribute: Monthly_Repayments Type: Real Attribute: Monthly_Expenses Type: Real Attribute: Product_Type Attribute: Existing_Customer Attribute: Application_Risk_Score Attribute: Age Attribute: Marital_Status Attribute: Employment_Status GoalProposition: 'Additional info' "What is the Amount of the requested product?" (real)> 12000 "What is the Rate (notation 0.0000) of the requested product?" (real)> 0.006 "What is the Term (in months) of the requested product?" (int)> 30 "What is the product type of the requested product?" 1. Standard_Loan 2. Special_Loan 3. Otherwise > 1 "What is the Monthly_Income?" (real)> 2300 "What is the Monthly_Repayments?" (real)> 200 "What is the Monthly_Expenses?" (real)> 300 "Applicant is an existing customer?" 1. True 2. False > 2 "What is the age of the applicant?" (int)> 55 "What is the marital status of the applicant?" 1. S 2. M 3. Otherwise > 1 "What is the employment status of the applicant?" 1. Unemployed 2. Student 3. Employed 4. Self-Employed 5. Otherwise > 4 "What is the Credit Score (>=580 is Accept)" (int)> 590 "Is this a case of bankruptcy?" 1. True 2. False > 2 Loan Origination Result is: Accept *************************************************************************************** Adjudication is: None Routing is: Accept Strategy is: Bureau Bureau Call Type: FULL Eligibility: Eligible Pre Bureau Affordability: True Pre Bureau Risk Category: Medium Existing Customer: False Application Risk Score: 109 = Age_Risk_Score(48)+ Marital_Risk_Score(25)+ Employment_Risk_Score(36) Based on Age (55), Marital Status (S) and Employment Status (Self-Employed) Pre Bureau Affordability Formula: 1.0 Required Monthly Installment: 423.10748976631345 PMT: 403.10748976631345 ; based on Amount: 12000.0, Rate: 0.006, Term (in months): 30 Monthly Fee = 20.0, because Product Type is: Standard_Loan Disposable Income: 1800.0 .. is Monthly Income: 2300.0 - (Monthly Repayments: 200.0 + Monthly Expenses: 300.0) Pre Bureau Credit Contingency Factor: 0.7 Post Bureau Affordability: True Post Bureau Risk Category: Medium Post Bureau Affordability Formula: 1.0 Post Bureau Credit Contingency Factor: 0.7 Bureau data: Banktrupt: False Bureau data: Credit Score: 590 ***************************************************************************************