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Decision models using dmn and bpmn standards: mortgage recommender

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Given our collective experience analyzing, modeling and deploying dozens of Business Rule systems our objective was to explore DMN interactions with existing standards and determine its value-added in the context of a complex-enough business decision.

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Decision models using dmn and bpmn standards: mortgage recommender

  1. 1. DEVELOPING COMPLEX-ENOUGH DECISION MODELS USING DMN & BPMN STANDARDS Gil Ronen gilronen@revvisionconsulting.com Jacob Feldman, PhD jacobfeldman@openrules.com
  2. 2. OBJECTIVE Question: Is the new OMG Decision Model and Notation standard (DMN) useful beyond toy problems? Given our collective experience analyzing, modeling and deploying dozens of Business Rule systems our objective was to explore DMN interactions with existing standards and determine its value-added in the context of a complex-enough business decision Ronen & Feldman (c) 2014 2
  3. 3. SYNOPSIS •Setting the stage for the complex-enough business decision to be modeled •Introduction to the new OMG Decision, Model & Notation standard (DMN) •Interactions between state-of-the-art business standards for modeling decision logic (OMG DMN and BPMN) •End-to-end model validation •Conclusions Ronen & Feldman (c) 2014 3
  4. 4. BUSINESS BACKGROUND: MORTGAGE RECOMMENDATIONS Business Opportunity: •Lenders offer multiple products and programs •Lenders support multiple channels •channels may offer the same product from different lenders •Customer touch-points may not be knowledgeable about the mortgage domain or specific lender products •Highlighting benefits of different products is time- consuming and error-prone in a sales setting Solution: •Automate mortgage loan recommendations Ronen & Feldman (c) 2014 4
  5. 5. BUSINESS CASE: OBJECTIVE-BASED MORTGAGE RECOMMENDATION 1.Customer provides objective and skeletal preferences 2.Generate loan constraints 3.Match loan constraints to lender products 4.Determine base rate 5.Determine risk-based pricing adjustors 6.Determine monthly payment 7.Determine objective-based option ranking 8.Present ranked options to customer Ronen & Feldman (c) 2014 5
  6. 6. REQUEST/RESPONSE Request Objective Requested Program Requested Product Category Requested Product Term Requested LTV Requested Loan Amount Lowest Monthly Payment Non-Prime Fixed 30 87.50 $350,000 Program Category Term Liens LTV 1st LTV 2nd Amount Mortgage Insurance Tax & Insurance Principle & Interest Total Monthly Payment Benefits Prime Fixed 30 First 87.50% --- $350,000 $175.00 $437.50 $2,017.01 $2,629.51 Corresponding Prime product carries a lower rate; Requires qualifying by more stringent criteria. Non- prime Fixed 30 First 85.00% --- $340,000 $170.00 $425.00 $2,123.38 $2,718.38 Price advantage at 85% over higher LTV; Specific to a particular lender. Non- prime ARM 3/1 First 87.50% --- $350,000 $175.00 $437.50 $2,126.64 $2,739.14 ARM products typically offer lower rates initially; 3/1 is fixed for first 3 years. Non- prime Fixed 30 First+ Second 80.00% 10.00% $360,000 $0.00 $450.00 $2,364.94 $2,814.94 Avoids Mortgage Insurance; Scenario includes 2 loans closing simultaneously. Non- prime Fixed 40 First 87.50% --- $350,000 $175.00 $437.50 $2,238.85 $2,851.35 A longer term reduces the monthly payment. Non- prime Fixed 30 First 87.50% --- $350,000 $175.00 $437.50 $2,243.40 $2,855.90 Requested scenario. Ronen & Feldman (c) 2014 6
  7. 7. EXISTING STANDARD: BPMN •BPMN = Business Process Model & Notation •Understanding and communication of internal business procedures in graphical notation •Highlights collaboration and transactions between business actors (roles and organizations) •Concerned with temporal aspects (flow, life-cycle) •Supports business continual improvement (business controls, re-engineering) •Already incorporates a Business Rules Task in the context of Business Decision Management (in BPMN 2.0) Ronen & Feldman (c) 2014 7
  8. 8. NEW STANDARD CONTEXT: BDMS •BDMS = Business Decision Management System •BDMS is at the center of modern enterprise architectures •Business decisions affect customer satisfaction, competitive analysis, and profitability in any businesses •Examples: loan approval, insurance underwriting, customer service tactics, clinical guidelines, risk management, compliance, and many others Ronen & Feldman (c) 2014 8
  9. 9. NEW STANDARD: DMN •DMN = Decision Model & Notation •New OMG standard for decision modeling •Beta version 1.0 made available Set. 26, 2013 •Target audience primarily business users (work product modifiable not only by IT) •Provides constructs to support modeling decisions so that they can be represented graphically, modeled by analysts and, optionally, automated •Compliments BPMN (and CMMN) Ronen & Feldman (c) 2014 9
  10. 10. DECISION REPRESENTATION COMPONENTS •Business Process •Decision Requirements •Decision Logic Ronen & Feldman (c) 2014 10
  11. 11. DECISION REQUIREMENT CONSTRUCTS •Decision Requirements Graph (DRG) •Decision Requirements Diagram (DRD) •Decision •Input Data •Business Knowledge Model •Knowledge Source •Connectors (Information, Knowledge, Authority) Ronen & Feldman (c) 2014 11
  12. 12. DMN MODELING Ronen & Feldman (c) 2014 12 Decision Requirements Diagrams (DRD) Decision Logic (Standardized Decision Tables)) Integration with Business Processes (BPMN)
  13. 13. DECISION LOGIC “A business knowledge model may contain any decision logic which is capable of being represented as a function. This will allow the import of many existing decision logic modeling standards (e.g. for business rules and analytic models) into DMN. An important format of business knowledge, specifically supported in DMN, is the Decision Table. “ Ronen & Feldman (c) 2014 13
  14. 14. DMN NOTATION Ronen & Feldman (c) 2014 14 DRD Output of the Decision-2 is used as an input for the Decision-1 This Decision Table represents Business Knowledge-2 (decision logic)
  15. 15. DECISION DIAGRAMING TOOLS •Commonly Used Microsoft tools: •Visio •Excel •Business Process Management tools: •Any BPMN tool •E.g. ProcessOn •Specialized DMN tools •“DecisionFirst” – Decision Management Solutions Ronen & Feldman (c) 2014 15
  16. 16. EXAMPLE MODEL: DECISIONFIRST MODELER & OPENRULES Ronen & Feldman (c) 2014 16 DecisionFirst Diagram with a URL that points to the OpenRules Decision Table
  17. 17. DMN LITERAL EXPRESSIONS •Not every problem is a nail (Decision Table) •Literal expressions are text describing how output values are derived from input values •The expression language may, but need not be, formal or executable •Examples include: plain English, first-order logic, Java code, etc. •FEEL Ronen & Feldman (c) 2014 17
  18. 18. DMN DECISION REPRESENTATION •Business Process •Decision Requirements •Decision Logic Ronen & Feldman (c) 2014 18
  19. 19. BUSINESS PROCESS Ronen & Feldman (c) 2014 19
  20. 20. DMN DECISION REPRESENTATION •Business Process •Decision Requirements •Decision Logic Ronen & Feldman (c) 2014 20
  21. 21. DECISION REQUIREMENTS Match Loan Constraints to Lender Products Determine Product Decision Table Decision Requirement Graph (DRG) for Objective-based Mortgage Loan Recommendations Lender Products Determine Objective-based Mortgage Loan Recommendations Determine Objective-based Recommendations Determine Objective-based Ranking Ranking Strategy Decision Table Client Objectives Client Objectives and Preferences Determine Lien-based Scenarios Determine Requested Scenario Determine Term-based Scenarios Determine Risk-based Scenarios Risk-based Options Decision Table Requested Product Decision Table Lien Options Decision Table Term Options Decision Table Lender Pricing Policy GSE Loan Amount Guidelines Credit Information Generate Loan Constraints Determine Loan Constraints Decision Business Knowledge Input Data Knowledge Source Information Requirement Knowledge Requirement Authority Requirement Determine Base Rate Decision Table Determine Adjustors Decision Table Principle & Interest Calculation (PMT) Mortgage Insurance Decision Table Taxes & Insurance Decision Table Real-time Pricing Data Determine Monthly Payment Secondary Markets Ronen & Feldman (c) 2014 21
  22. 22. FILTERED VIEW: HIGH-LEVEL Match Loan Constraints to Lender Products Determine Monthly Payment Determine Objective-based Mortgage Loan Recommendations Determine Objective-based Recommendations Determine Objective-based Ranking High-level DRD: Objective-based Mortgage Loan Recommendations Ronen & Feldman (c) 2014 22
  23. 23. FILTERED VIEW: DECISION Client Objective and Preferences Generate Loan Constraints Determine Lien-based Scenarios Determine Requested Scenario Determine Term-based Scenarios Determine Risk-based Scenarios Risk-based Options Decision Table Requested Product Decision Table Lien Options Decision Table Term Options Decision Table DRD for the Decision Generate Loan Constraints Match Loan Constraints to Lender Products Lender Pricing Policy GSE Loan Amount Guidelines Credit Information Ronen & Feldman (c) 2014 23
  24. 24. DMN DECISION REPRESENTATION •Business Process •Decision Requirements •Decision Logic Ronen & Feldman (c) 2014 24
  25. 25. DMN DECISION TABLE Term Options Decision Table NI Collect # Request Objective Requested Product Category Requested Product Term Recommended Product Category Recommended Product Term Recommended Liens LOWEST MONTHLY PAYMENT, EQUITY BUILDER, LOWER RATE FIXED, ARM see products list FIXED, ARM see products list FIRST, FIRST+SECOND 1 LOWEST MONTHLY PAYMENT FIXED ARM 3/1 FIRST 2 FIXED 15 FIXED 20 FIRST 3 FIXED 20 FIXED 30 FIRST 4 FIXED 30 FIXED 40 FIRST 5 ARM 7/1 ARM 3/1 FIRST 6 ARM 5/1 ARM 3/1 FIRST 7 ARM 3/1 ARM 1/1 FIRST 8 EQUITY BUILDER FIXED 30 FIXED 20 FIRST 9 FIXED 20 FIXED 15 FIRST 10 FIXED 15 FIXED 10 FIRST 11 ARM FIXED 20 FIRST 12 LOWER RATE FIXED 30 FIXED 20 FIRST 13 FIXED 20 FIXED 15 FIRST 14 FIXED ARM 5/1 FIRST 15 ARM Not 1/1 ARM 1/1 FIRST Ronen & Feldman (c) 2014 25
  26. 26. DMN CUMULATIVE DECISION TABLE Determine Adjustors Decision Table NI SUM # Recommended Program Recommended Liens Recommended 1st Lien LTV Adjustor # PRIME, NON-PRIME FIRST, FIRST+SECOND [0…100] 1 0 2 FIRST [85.01..100] 0.0025 3 NON-PRIME 0.01 Ronen & Feldman (c) 2014 26
  27. 27. IMPLEMENTATION TOOL: OPENRULES •OpenRules is a general purpose Business Rules and Decisions Management System available as an Open Source product •Allows subject matter experts and software developers to create, test, execute, and maintain enterprise-class decision support applications •To this point the slides show a tool-independent representation •Below slides show the translation from the DMN graphical representation and DMN decision logic representation into the OpenRules table-based executable representation •Implementation executes and results are reported Ronen & Feldman (c) 2014 27
  28. 28. TOP-LEVEL DECISION Decision GenerateLoanConstraintsAndMatchProducts Condition ActionPrint ActionExecute Number of Loan Options Decisions Execute Generation Loan Constraints (initial loan options) GenerateLoanConstraints > 0 For each loan constraint, Match It to Lender Products MatchToLenderProducts Decision Main Condition ActionPrint ActionExecute Number of Loan Options Decisions Execute Generate Loan Constraints and Match them to Lender Products GenerateLoanConstraintsAndMatchProducts > 0 Determine Monthly Payments for Generated Loan Options DetermineMonthlyPayments > 1 Determine Objective-based Ranking RankLoanOptions Ronen & Feldman (c) 2014 28
  29. 29. TERM OPTIONS DECISION TABLE DecisionTableMultiHit TermOptionGenerationRules # If If If ActionAny Then Then Then Then Then Then Then # Request Objective Requested Product Category Requested Product Term Add New Recommendation Recommended Program Recommended Product Category Recommended Product Term Recommended Liens Recommended 1st Lien LTV Recommended 2nd Lien LTV Recommended Loan Amount 1 Lowest Monthly Payment Fixed :=add(decision) := ${Requested Program} ARM 3/1 First ::= $R{Requested LTV} 0 ::= $R{Requested Loan Amount} 2 Fixed 15 :=add(decision) := ${Requested Program} Fixed 20 First ::= $R{Requested LTV} 0 ::= $R{Requested Loan Amount} 3 Fixed 20 :=add(decision) := ${Requested Program} Fixed 30 First ::= $R{Requested LTV} 0 ::= $R{Requested Loan Amount} 4 Fixed 30 :=add(decision) := ${Requested Program} Fixed 40 First ::= $R{Requested LTV} 0 ::= $R{Requested Loan Amount} 5 ARM 7/1 :=add(decision) := ${Requested Program} ARM 3/1 First ::= $R{Requested LTV} 0 ::= $R{Requested Loan Amount} 6 ARM 5/1 :=add(decision) := ${Requested Program} ARM 3/1 First ::= $R{Requested LTV} 0 ::= $R{Requested Loan Amount} 7 ARM 3/1 :=add(decision) := ${Requested Program} ARM 1/1 First ::= $R{Requested LTV} 0 ::= $R{Requested Loan Amount} Ronen & Feldman (c) 2014 29
  30. 30. GENERATE SCENARIOS Decision GenerateLoanConstraints Decisions Execute Term Option Generation Decision TermOptionGenerationRules Price Option Generation Decision PriceOptionGenerationRules Lien Option Generation Decision LienOptionGenerationRules Requested Product Generation Decision RequestedProductGenerationRules Ronen & Feldman (c) 2014 30
  31. 31. DETERMINE ADJUSTORS DECISION TABLE DecisionTableMultiHit DetermineRateAdjustorRules If If If Conclusion Recommended Program Recommended Liens Recommended 1st Lien LTV Adjustor = 0 First [85.01..100] += 0.0025 Non-Prime += 0.01 Ronen & Feldman (c) 2014 31
  32. 32. END-TO-END VALIDATION Ronen & Feldman (c) 2014 32
  33. 33. RECAP •Loan recommendation problem presented •Process modeled using BPMN 2.0 •Decision logic modeled using new DMN standard •Problem implemented in OpenRules •End-to-end testing provides verifiable results •Customer stated preferences in conjunction with lender product and pricing data and a set of mortgage configuration rules produce a ranked set of mortgage loan options and their corresponding benefits Ronen & Feldman (c) 2014 33
  34. 34. CONCLUSIONS •BPMN provides insufficient support for BDMS •DMN formalizes package of material in support of decision logic •DMN’s ability to bring together context of decision table invocation, authority levels for decision management maintenance and governance, and interdependencies between decisions can bridge the gap between business process representations and low-level IT data models and code •Encapsulation of decision logic brings with it the benefits of modularity and flexibility as-well-as transparency throughout the enterprise from business people to IT staff •Vendor support remains to be seen Ronen & Feldman (c) 2014 34

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