Decision Services for SAI

Decision Services A pattern for business rules in Service Oriented Systems and Architectures James Taylor Principal Smart (enough) Systems LLC

Agenda
Smarter Systems
Decision Services Defined
Building Decision Services
Decision Services and SOA
Decision Services and BPM

Dumb Systems Are Everywhere… Report but don’t learn Built to last, not to change Wait rather than act CRUD

Why Smarter Systems? Decision-Making Well-Defined Increasingly Complex Timeliness Days Real-time Objectives Local and clear Complex Trade-Offs Regulations National and simple Complex and global Changes to Strategy Every 3-5 Years Constant Operational Volume Low High

So What IS A Smarter System
Operational
Real-Time
Rapidly evolving - agile
Learning
Demonstrably Compliant
Cost-Effective
Business-Driven

Smarter Systems Make More Decisions People Not Made Embedded People Embedded Not Made New Before After Larger boxes represent more decisions, by volume

Different kinds of decisions Low High High ECONOMIC IMPACT OF INDIVIDUAL DECISION Low DECISION VOLUME High-value, low-volume decisions Medium-value, medium-volume decisions Low-value, high-volume decisions

Decisions, Decisions, Decisions

Agenda
Smarter Systems

Decision Services – A Pattern
Automated decision systems have been a common element of Financial Services application portfolios for some time
With the advent of SOA and component architectures, the idea of a separate Decision Service has become widely accepted
As the cost of technology has fallen and awareness increased, the approach has been validated

Applications have evolved Data Process Logic User Interface Browser BPM Services

Evolution Completed
A self-contained, callable service with a view of all the conditions and actions that need to be considered to make an operational business decision.
Decision Services
A service that answers a business question for other services and processes.
What is a decision service? Services

Problem
Some business services implement decisions
These services must follow policies and regulations
These services must be easy to change
These services must be driven “by the numbers”
These services simplify business processes
These services often replace high-change elements of legacy applications

Many Forces At Work

Agenda
Smarter Systems

The Solution
ENTERPRISE DECISION MANAGEMENT is an approach for automating and improving high-volume operational decisions.
Focusing on operational decisions,
it develops decision services
using business rules to automate those decisions,
adds analytic insight to these services using predictive analytics
and allows for the ongoing improvement of decision-making through adaptive control and optimization .

Applicability
Decisions where large numbers of policies or regulations apply
Decisions with policies and regulations that change a lot
Decisions that require business domain knowledge to understand
Decisions that are complex or have complex interactions
Decisions that the business insists on owning
Decisions requiring analytic insight
Decisions that cut across events and processes

Consequences Isolation of Decisions Control and Agility in Business Processes Business Control of Decisions Integration Points for Advanced Analytics Externalization from Legacy Applications

Context

Delivering Decision Management

Business Rules Are Everywhere Experienced Personnel Regulations Policy Manuals Legacy Systems Historical Data Managed Business Rules

Manageable Business Rules Smart (Enough) Systems, Prentice Hall June 2007. Fig 4.3 If customer is GoldCustomer and Home_Equity_Loan_Value is more than $100,000 then college_loan_discount = 0.5% If member has greater than 3 prescriptions and prescription’s renewal_date is less than 30 days in the future then set reminder=“email” If patient’s age is less than 18 and member’s coverage is “standard” and member’s number_of_claims does not exceed 4 then set patient’s coverage to “standard”

Decision Flow
Smart (Enough) Systems, Prentice Hall June 2007. Fig 6.2
score >= 3 score < 3 true true true true approval is true approval is false Loan Decision Reject Application Generate Report Account Coordination Notify Customer Service Billing Adjustment Reject Application Price Structuring Booking

A list of rules representing a rule set
AdjustIncome RuleSet (newApplicant) Name: Six months or less on the job if newApplicant’s monthsInCurrentJob is less than or equal to 6 then decrement newApplicant’s income by 5600 Name: About 1 year if newApplication’s monthsInCurrentJob is between 6 and 13 then increment newApplicant’s income by 1000 Name: Greater than 1 year if newApplicant’s monthsInCurrentJob is greater than 12 then increment newApplicant’s income by 2500 Name: Computer Total Income if newApplicant is married and newApplicant’s spousalIncome is greater than 0 then newApplicant’s totalIncome is equal to newApplicant’s income plus newApplicant’s spousalIncome else newApplicant’s totalIncome is equal to newApplicant’s income Name: Minimum Income if newApplicant’s totalIncome is less than 40000 then newApplicant is not eligible

A selection of decision table layouts
I n c ome C a r d T ype C r edit Limit 2,500 P oor 25,000 - 34,999 3,000 G ood 25,000 - 34,999 4,000 E x c elle n t 25,000 - 34,999 3,000 P oor 35,000 - 44,999 3,500 G ood 35,000 - 44,999 I n c ome C a r d T ype S tude n t B r on z e 25,001 - 35,000 25,001 - 35,000 S tude n t G old 25,001 - 35,000 35,001 - 45,000 35,001 - 45,000 35,001 - 45,000 > 45,000 > 45,000 > 45,000 S tude n t P l a tinum S tude n t B r on z e S tude n t G old S tude n t G old S tude n t B r on z e S tude n t P l a tinum S tude n t P l a tinum C r edit Limit 2,500 3,000 4,000 3,000 3,500 4,500 4,200 4,700 5,200 S tude n t B r on z e 2 - Axis G r id 1 - Axis V e r tical 1 - Axis Ho r i z o n tal C a r d T ype C r edit R a ting Home O wner? S tude n t B r on z e P oor false C r edit Limit S tude n t P l a tinum E x c elle n t true C r edit Limit 4,000 4,500 5,200 S tude n t G old G ood true C r edit Limit 3,000 3,500 4,700 I n c ome 25,001 - 35,000 35,001 - 45,000 >45,000 2,500 3,000 4,200

A decision tree
IsEmpl o y ed C r editEstim a t e Y ea r ly I n c ome Y ea r ly I n c ome L oan T ype R e f e r r ed poor othe r wise fair e x c elle n t true Y ea r ly I n c ome L oan Limit 20,000 L oan Limit 10,000 L oan Limit 5,000 L oan Limit 3,000 L oan Limit 2,000 L oan Limit 1,000 L oan Limit 1,000 L oan Limit 500 L oan Limit 1,500 < 25,000 25,00 - 75,000 > 75,000 > 90,000 < 100,000 < 40,000 40,000 - 90,000 50,000 - 100,000 < 50,000

Help the Business to Manage Decisions So you business-types want to be able to change your business rules? I want to relax my underwriting policy I want to be able to promote a new product combination I need to add the new regulations No…

Which Would Your Users Understand? Smart (Enough) Systems, Prentice Hall June 2007. Fig 6.8 public class Application { private Customer customers[]; private Customer goldCustomers[]; ... public void checkOrder() { for (int i = 0; i < numCustomers; i++) { Customer aCustomer = customers[i]; if (aCustomer.checkIfGold()) { numGoldCustomers++; goldCustomers[numGoldCustomers] = aCustomer; if (aCustomer.getCurrentOrder().getAmount() > 100000) aCustomer.setSpecialDiscount (0.05); } } } If customer is GoldCustomer and Home_Equity_Loan_Value is more than $100,000 then college_loan_discount = 0.5% College Loan Discounts Current Discount = Eligibility Gold Customer and Home Equity Loan more than $100,000 %

Rule templates to make maintenance simple Smart (Enough) Systems, Prentice Hall June 2007. Fig 6.7 Rules R eposi t o r y Rule: M issing I t ems: and or S t r eet A dd r ess // I n c omple t e add r ess // I f A pplica n t ’ s zip is missing or A pplica n t ’ s ci t y is missing and A pplic a tio n ’ s st a t e is missing or A pplica n t ’ s st r eet A dd r ess is missing then set c omple t eness of A pplic a tion t o F ALSE // I nsufficie n t Name // I f A pplica n t ’ s LastName is missing then set c omple t eness of A pplic a tion t o F ALSE // A dd r ess does not e xist // I f v e r ifyUSPS A dd r essCheck ( A pplica n t ’ s add r ess) is F ALSE then set eli g ibili t y of A pplic a tion t o F ALSE // T oo y oung and poor // I f A pplica n t ’ s age is less than 30 and A pplica n t ’ s I n c ome is less than $100,000 or A pplica n t ’ s age is less than 25 and A pplica n t ’ s in c ome is less than $150,000 then set eli g ibili t y of A pplic a tion t o F ALSE C onfi r m Eli g ibili t y Rules A pplic a tion . c omple t eness .eli g ibili t y . A pplica n t .lastname . . . . P oli c y R equest . . . D a ta C omple t eness T empl a t e K noc k - out T empl a t e Rule: and or A ge C i t y S t a t e Zip Last Name M o r e than < v alue> L ess than < v alue> B e t w een <a> and <b> I n c ome G ender R isk S c o r e A dd r ess V alidi t y

Decisions remain in sync with market changes Smart (Enough) Systems, Prentice Hall June 2007. Fig 2.11 Business users Conventional Approach Enterprise Decision Management Programmers Frequent code changes CRM System Other Systems Frequent policy changes Infrequent code changes Decision Service CRM System Other Systems Programmers Policy Changes Business users so r t ed=1: f or I y = I–I= y > 11; y – I t p r i n t ( y – Id ,y) : if [1a r r a y I y] < 1a r r a y I y -1]) t holder - [a r r a y[ y -1] | Ia r r a yl y -1]) = 1 [y]) 1 a r r a y = holder so r t ed=1: f or I y = I–I= y > 11; y – I t p r i n t ( y – Id ,y) : if [1a r r a y I y] < 1a r r a y I y -1]) t holder - [a r r a y[ y -1] | Ia r r a yl y -1]) = 1 [y]) 1 a r r a y = holder

Analytics <> Graphs
Even graphics require interpretation
Not everyone can see the patterns
And code does not “see” at all

Data Mining - Improve Rules * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Low-moderate income, young High Income High income, low-moderate education Moderate-high education low-moderate income High Moderate education, low income, middle-aged Low education, low income Education Age High

Predictive Analytics – Add Insight 10 20 30 40 Member renews Member fails to renew

Impact May Take Time to Play Out

Use Adaptive Control to Continuously Improve

The Evolution Of An Interaction
Automate Decision
Apply rules
Segment customers
Predict risk, value
Optimize decision
Web http://www.f Email Call Center Mobile

Building Decision Services Smart (Enough) Systems, Prentice Hall June 2007. Fig 5.1 Production Process Enterprise IT Infrastructure Data Warehouse Operational Data Store Business Rules Analytics Decision Service Adaptive Control Events

Analytic Modeling Analytic Modeling Smart (Enough) Systems, Prentice Hall June 2007. Fig 5.6 Data Warehouse Model Repository Data Mining and Analysis Data Preparation Data Requirements Determine Outcome Model Deployment Model Validation Model Tuning Model Development Predictive Model Rules

Rule Development Smart (Enough) Systems, Prentice Hall June 2007. Fig 6.9 Business Rules Rules Repository Rules Deployment Rules Validation and Testing Reporting Dashboards Rules Development Rules Maintenance Model Repository Predictive Model Rules Rules Rules

Adaptive Control Smart (Enough) Systems, Prentice Hall June 2007. Fig 7.9 Adaptive Control Rules Repository Experimental Design Champion/ Challenger Setup Simulation Optimization Model Design Model Repository Decision Service Data Warehouse Model Repository Decision Analysis Predictive Model Rules

Business Rules Management Technology Smart (Enough) Systems, Prentice Hall June 2007. Fig 6.6 Design Tools Rule Management Applications Business Application Decision Service Rule Engine Customers Employees Partners Suppliers IT Staff Business Experts Data Sources Rule Repository

Business Rules and Decision Services

Adaptive Control Inside a Decision Service

Deployed Decision Services Smart (Enough) Systems, Prentice Hall June 2007. Fig 4.5 ERP CRM Billing SCM f (s) Business Intelligence Analytic Development Rules Management Decision Analysis Process Data, Rules and Analytics to Determine Best Decision Decision Service Data Warehouse Request Decision Decision Channels Call Center Web Email Telemarketing Direct Mail Store/Branch Kiosk/ATM Field

Agenda
Smarter Systems

8 Principles of Service-Orientation
Standardized service contract
Service Loose Coupling
Service Abstraction
Service Reusability
Service Autonomy
Service Statelessness
Service Discoverability
Service Composability

7 Goals of Service-Orientation Goal Decision Management Impact Increased intrinsic interoperability Increased federation Increase business and technology domain alignment Business and IT share an understanding of business logic Increased vendor diversification options Increased ROI Increased Organizational agility Decision Services are more transparent and agile Decreased IT burden Empowering the business to maintain decision services

4 Characteristics of SOA
Business-Driven
Vendor-Neutral
Enterprise-Centric
Composition-Centric

Benefits Of Decision Services
“ Built to change not built to last”
In the real world, nothing is set in stone
What do you do if a service must change a lot?
Managing “change-time”
Legacy Modernization
Externalize decisions hidden in legacy applications
A bridge between your mainframe and your SOA
Differentiation
Buy services based on best practices, standards
Buy services that don’t differentiate you
Build Decision Services for competitive differentiation

A Central Credit Decision Service
Business Rules A nalytics I nput EL C alcul a tion D ecision S e r vi c e E x t e r nal D a ta I n t e r nal D a ta S ales D a ta L oan D a ta R a ting R esults Business Rules Black List O utput S ales S y s t ems I n t e r fa c e L a y er PD C alcul a tion

Decision Service and Message Bus
Business P r o c ess M anageme n t / W o r kfl o w M anageme n t M essage Bus A pplic a tion S e r v er P r odu c tion S y s t ems Business Rule R eposi t o r y A c ti v e D ecision En g ine T r ansa c tion w aiting T r ansa c tion p r o c essing T r ansa c tion p r o c essed D ecision S e r v er Subsc r ibe I n t e r fa c e P ublish I n t e r fa c e D ecision S e r vi c e A

Decision Services in Mortgage Lending

Agenda
Smarter Systems

An Underwriting Example  Process Tasks  Decision Tasks
May include customer visits, collection of forms, etc.
Triggers more data gathering process steps
Triggers rejection process
Calculate risk and determine price, may trigger exception handling

Risk and Benefits
Process without Decisions
Decisions an afterthought
Decisions buried in process
Process becomes complex
Inconsistency of rules likely
Decisions only evolve with process
Hard to share decisions
Process with Decisions
Decisions first class object
Decisions are linked but not buried
New process is simplified
Independent process & decision changes
Decisions (and Decision Services) are reusable
Decision logic <> Process logic

Process Management
Standardizes processes How should a process be carried out?
Facilitates collaboration and workflow
Process automation around decision-making
Workflow definition and management
Integration broker
Decision Management
Standardizes decision-making What should the decision be based on?
Facilitates decision automation and maintenance
Replaces manual decision-making within processes
Business rules definition and management
Decision broker
Decisions and Processes Complementary but not the same

Decision Services in BPM
Processes become complex when decision making is mismanaged
Explicit decision services simplify processes dramatically

All Kinds of BPM Need Decisions Sense and Respond Systems People-Centric Automation Transaction-Centric Automation ESB CEP BAM Process Analytics Process Warehouse Decision Services Investigate Exceptions Automation After IDC Group “A View of BPM in 2007 and beyond”, Maureen Fleming

Wrap Up
Smarter Systems are Needed
Systems must make more decisions
In an SOA world this means Decision Services
Decision Services are a pattern
Well proven
Built on existing technology
Compatible with Rules Centralization
Ready for analytics

Known Uses
Recommendations
Next Best Action
Underwriting
Fraud Detection
Eligibility
Scheduling
Accounting Allocation
Billing
Dynamic Pricing
…

Action Plan
Identify your decisions
Decisions that matter to customers
Hidden decisions
Transactional decisions
Consider
Who takes them now
What drives changes in them
What the context is for them
Assess
Change Readiness
Technology adoption
Organizational change
Adopt Business Rules
Approach and technology
Management and governance
Change the relationship between business and IT
Investigate Data Mining and Predictive Analytics
Data Mining for rules
Predictive reporting
Executable analytics
Build Adaptive Control into your applications

An overview of traversing the steps in EDM Smart (Enough) Systems, Prentice Hall June 2007. Fig 9.1 Phase 1 - Piecemeal Phase 3 - Expansion Readiness Assessment Phase 2 – Local Decision Management Rules and Analytics Champion Challenger Steady State EDM Enterprise Management Analytic Value Chain Optimization and What If Enterprise Backbone Broaden Analytic Base Improved Foundation Foundation First Rules Project First Analytic Project Manage Scenarios Overlapping and Adjacent Projects

Smart (Enough) Systems – The Book
How key business trends impact the decision-making process
Why organizations need systems smart enough to cope with these trends
How decision automation can make their systems smart enough
How to translate decisions into a corporate asset and competitive advantage
The ROI and business impact of better decisions and smarter systems
The core concepts and technologies needed and how they work together
The book is full of insightful examples of problems solved by applying Enterprise Decision Management across various industries and outlines a practical and incremental method for implementing the technology.

Thank You James Taylor [email_address] http://www.smartenoughsystems.com Blog : www.smartenoughsystems.com/wp

Decision Services for SAI

by Decision Management Solutions on Oct 09, 2008

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Decision Services for SAI — Presentation Transcript