FICO ® Blaze Advisor ® Decision Rules Management System - How It Works Updated for Release 7.3

© 2016 Fair Isaac Corporation. All rights reserved. 1

March 2016

WHITE PAPER

How It Works Updated for Release 7.3

FICO® Blaze Advisor® decision rules management system is a complete solution for enterprise decision management. It includes an architectural framework for building decision management applications and state-of-the-art tools for deploying and maintaining decision services (also known as rule services). In this white paper, you’ll learn about using Blaze Advisor for rule authoring, implementation, deployment, and testing and maintenance, and how Blaze Advisor can be extended with analytics, simulations and a number of additional products within FICO’s Decision Management suite.

Introduction to Decision Management Suite

Automation Safety &Security

Architecture

SharedLogic

DecisionRules

Management Collaboration

Value DataAccess

Transparency

FICO® Blaze Advisor®

Decision RulesManagementSystem

WHITE PAPERFICO® Blaze Advisor® Decision Rules Management System

Decision rules management, also called business rules management, is software that provides organizations with a number of advantages.

Automation: Organizations can automate high-volume operational decisions quickly and effectively while maintaining business control over those decisions.

Safety & Security: Business analysts can maintain decision logic used in IT systems safely and securely without being dependent on technical resources. This reduces application modification time and speeds organizational response to changing conditions. It can also significantly reduce the total cost of ownership for the application over time.

Architecture: Application developers can use architectures that separate the decision logic from the application control logic. The logic that represents business decisions can be maintained independently of the application code and can be updated without stopping and restarting the applications. The rules in these decisions can be reviewed and understood by business analysts. Separating the decision logic from procedural code also allows faster development of the decision logic.

Shared Logic: Decision logic can be shared across applications, promoting consistent behavior throughout the enterprise. This becomes more important as organizations offer services over multiple channels such as web self-service, call center and mobile access.

Decision Rules: Decision rules can be defined, reviewed and maintained using a variety of different formats, such as decision tables, decision trees, scorecards, decision graphs and customized templates. This means that any set of rules can be displayed in the most practical and natural way, allowing a clear understanding of their purpose and interrelationship, even in a multi-step decision process.

Management: Decision rules management technology provides an excellent platform for a coherent approach to automating and improving operational decisions. The impact of new rules can be assessed before the rules go into production, improving decisions by comparing alternative approaches and preventing costly errors in strategies that might otherwise be missed.

Collaboration: Business analysts can collaborate and gain visibility into the authorization process for business rules using lifecycle management.

Value: Organizations can add value to their business processes by using decision rules to personalize their services, dialogs and content, based on user profile data and process characteristics.

Data Access: Businesses can make automated decisions based on data accessed as needed from a variety of sources, including multiple databases, XML documents, Java objects, .NET/COM objects and COBOL copybooks.

Transparency: Decision rules management brings transparency to business processes. It allows organizations to track how and why certain decisions were made to demonstrate regulatory compliance.


Introducing FICO®

Blaze Advisor®

decision rules

management system

FICO® Blaze Advisor® decision rules management system is a complete solution for enterprise decision management encompassing decision modeling, decision service design, authoring, testing simulation, deployment and maintenance. A decision service (also known as rule service) is defined as a monolithic view of all the conditions and actions that need to be considered in performing a self-contained, callable decisioning service from a larger application. The following graphic illustrates how Blaze Advisor’s functional components interact with business applications and data.


Blaze Advisor components integrate business applications and data (internal or external)

What is a decision service?

A decision service (also known as rule service) can be defined as a self-contained, callable component with a view of all the conditions and actions that need to be considered to make an operational business decision.

More simply, a decision service is a component or service that answers a business question for other services. Decision services use your data, and the insights derived from it, to drive automated decisioning. Decision services also isolate the logic behind your operational decisions, separating it from business processes and the mechanical operations of procedural application code. A decision service represents a single point of decision-making across all your systems and processes. As such, it allows you to focus resources on improving and optimizing decisions. You can reuse decision services across multiple applications in many different operational environments. Decision services can also eliminate the time, cost and technical risk of trying to reprogram multiple individual systems simultaneously to keep up with changing business requirements.

Rules-Driven Business or Operational Application

“I need a decision”

componentAPIWeb

Service

Applications could be: • Product recommendation• Regulatory compliance• Account management• Forms selection

• Fraud detection• Data validation• Customer rating• Financial risk analysis

Rule Repository


DesktopDevelopmentEnvironment

Cloud or On-PremiseWeb-Based

Decision Authoringand Management

Rule Engine/Rule ServerDeployment Manager

CustomerProfiles

IndustryData

EnterpriseData


• A business application asks a decision service to perform some business function (for example, to provide a business assessment, classify and diagnose information, recommend an action or validate data).

• The decision service is executed using the FICO® Blaze Advisor® Rule Server, which manages scalability, multiple service requests, rule updates and more.

• These Blaze Advisor components access any necessary data sources to obtain information as needed to perform the service.

• The rules that drive the decision service are maintained in a repository that provides the service with the latest definitions of rules that are updated by users in the development environment, also known as the IDE and maintenance applications.



• Blaze Advisor Rule Maintenance Applications (RMA) support controlled rule creation, maintenance and decision service lifecycle management by business analysts through visual and intuitive web interfaces. The RMA supports several languages.

• Analytics assets such as predictive models can be imported into Blaze Advisor for rapid deployment.

• Blaze Advisor’s integrated development environment (IDE) is used for the technical definition of the decision service (also known as rule service). As an Eclipse plug-in, Blaze Advisor’s IDE is a complete environment for rule service design, rule authoring, rule testing and configuration of custom templates for rules.

A sampling of the types of decision services our customers have built includes:

Interactions Between Business Processes, Applications, Data and RulesGenerally, business applications invoke a decision service (also known as rule service) for a decision just as they would invoke a database for data. The results of the decision process can be used to drive further operations and interactions. When invoked, a decision service uses the application data (referenced as any combination of Java objects, database records, XML documents, Microsoft .NET/COM+ objects, Cobol copybook data or custom-defined objects) to make decisions based on the application data and decision rules. The decision results can be passed back to the calling application or can be defined as actions taken in the decision process. Actions might include database update messages, calls to external applications or use of Java or .NET methods on objects. Decision services are often invoked by steps in business processes. As a part of FICO’s Decision Management suite, Blaze Advisor can also carry out optimized strategies and execute sophisticated analytic models when advanced mathematics are needed.

• Advertising scheduling and availability

• Benefits eligibility determination

• Claims handling

• Credit approval and limit determination

• Credit collection strategies

• Data validation

• Diagnostic advice

• Insurance underwriting

• Problem resolution procedures

• Product assembly configuration

• Product recommendations

• Regulatory compliance

Rules Use Corporate Data SourcesRules rely upon data values to test conditions, and they may specify actions that update data for use outside the decision service (also known as rule service). The data that rules operate against is provided by any external business object model that either:

• Conforms to a known standard (JavaBeans, EJB, JDBC, Microsoft .NET/COM+/DCOM, W3C XML, OMG CORBA); or

• Is accessible via Java and the Blaze Advisor Business Object Model Adapter.

Internally, Blaze Advisor maintains references to data in an object/property model representation analogous to native Java. For instance, a Customer object might be defined as having properties of Account Number, First Name, Last Name, Age, Account Balance and so on. Each property has a specified class (for example, string, date, integer, money) that tells the system how to format it and perform operations upon it. All data items are worked within Blaze Advisor using this object/property concept, regardless of their representation outside the decision service. Objects can inherit properties from parent objects, so you could define a Customer object and then define a VIP Customer that automatically takes on all the properties of Customer, with additional properties of Personal Representative and Credit Line.

FICO® Blaze Advisor® decision rules management system


Blaze Advisor decision services (also known as rule services) respond to triggering events from an application and provide results using data from any data source.

Rule EngineExecution

Rule Service #1

Predefined Rule Services Interactive/BatchApplication,

Process, Channel

Internal/External Data Sources

FICO®

BlazeAdvisor®

Rule ServerDecision

Processes Rules Models

Rule Service #2

DecisionProcesses Rules Models

Rule Service #3

DecisionProcesses Rules Models

ActivateAppropriateRule Service

CommunicateBack to Requesting

Application

Event orTransaction

OccursCall Center

Cellular

Email

Web

Application

Direct Mail

ATM

ApplicationMakes

Explicit Call

Response/Recommendation

Returned toApplication

ControlReturned toApplication

TransactionalData

CustomerData

OperationalData

BureauData Etc.

API Call

Web ServiceInvocation

EventListener


If you already have data definitions (also known as object models or data models), you can import them and automatically create corresponding object/property references to them for use in rules. This eliminates the need to reenter object definitions manually. Internal (rule-system-specific) object model representations can be created from external object models created with Java objects/JavaBeans, COM/CORBA/.NET objects, XML schemas or relational database table definitions. Blaze Advisor includes proprietary utilities called Business Object Model Adapters (BOMA) that read or introspect the external data source definitions, create an object/property representation and build automatic read/update links to the external data source for real-time reference in rule conditions or actions. You can also define data structures natively in Blaze Advisor using Business Terms, which can be mapped to external data structures at a later point. Business Terms are especially useful in that they allow business users to define their own data structures for rule authoring.

Object Model Import Wizardsautomatically connect toexternal data sources forimmediate use in writing rules.



Rules Authoring Blaze Advisor provides two methods for authoring rules:

• Blaze Advisor Eclipse plug-in

• Web-based Rule Maintenance Applications (RMA)

The Blaze Advisor Eclipse plug-in is an integrated development environment (IDE). Using the IDE, a technical developer can:

• Import an object model

• Create rules, rulesets, functions and other entities associated with the rules

• Test, debug, explore and report on rule projects

• Generate rule deployments

• Create the custom template for business analysts to create and maintain rules

• Generate a web-based RMA

The RMA enables business analysts and technical developers alike to create and maintain rules via a web application. In this environment, a user can:

• Define business terms

• Define and update rules in any number of formats

• Validate rules by running verification queries, data profiling and unit tests

• Assess business impact using simulation

• Publish decision service (also known as rule service) updates via the approval workflow

• Manage users and roles

Structured Rule LanguageBlaze Advisor provides a powerful rule syntax called Structured Rule Language (SRL) that provides a consistent and unambiguous way to specify the rules. The SRL syntax uses English language keywords, and allows for both compact and expanded styles of rule representation to suit different needs and different developers’ preferences.

Here is a simple business rule expressed three different ways in SRL. All three formats are valid and are functionally equivalent. Rule authors may mix syntax styles between rules or within a single rule. The decision as to which format to use depends upon user preference.

If customer.debt > customer.assets

then customer.application.status = Declined.

If the debt of the customer is greater than the assets of the customer

then the status of the application of the customer is Declined.

If the customer’s debt exceeds the customer’s assets

then set the status of the customer’s application to Declined.



Terms such as “is greater than” and “exceeds” are predefined within SRL. There are more than 100 keywords and language phrases to make rule definition as natural and intuitive as possible. Special language constructs exist to deal with strings, currencies, dates, missing data and operations over groups of data matching user-specified patterns or criteria.

When more power is required within a rule for doing complex procedural calculations, looping or calling upon external applications, SRL again allows these operations to be done within the rule language rather than forcing the use of an external programming language. This allows rules to remain independent of their execution environment so that customers do not need to make changes to their decision logic when they install a new operating system or compiler release.

Blaze Advisor SRL includessyntax and keywords that areclose to English.

Blaze Advisor SRL includesfunction definitions forprocedural operations whenneeded.

FICO® Blaze Advisor® includes a wide range of built-in syntax options and operations, and is ideally suited to represent a variety of industry-specific terminologies. Business analysts can also choose from a broad set of editors to define rules without using SRL.



If at least 2 children satisfy age < 8 then set discount to 0.25

If Product’s ID does not start with “SPX” then the Promotion Status of the Product is False

If the name of the customer is unknown then print [Please enter your name]

If order’s purchase date is earlier than January 1, 2002then print [Your purchase is no longer eligible for return]

Senior male is any customer such that (age > 65 and gender is male)

_monthlyPayment is a real initially 0.0.

_monthlyPayment = (amount * (plan.rate/12) *

Math.pow((1 + (plan.rate/12)), plan.numberOfPayments)) /

(Math.pow((1 + (plan.rate/12)), plan.numberOfPayments) - 1).

return_monthlyPayment.

Decision TablesMany organizations implement key decisions through the use of decision tables. This is especially true where the business rules are based on a small number of conditions, with each rule corresponding to a particular combination of data values or value ranges. Common examples include rate tables, pricing charts and discount schedules. The postage chart on the wall of your local post office is an example of a decision table, based on shipping method, weight and destination.

Blaze Advisor supports decision tables as a way to define and maintain large numbers of rules in a compact format that is easy to visualize and work with. Designers can choose to display their tables in a variety of formats, including single-axis or double-axis, multiple condition rows or columns, by single value or value ranges, and with color and font formatting as desired. Cell contents can be simple values or any complex action allowed within the SRL syntax. Tables can efficiently handle many thousands of rule conditions and actions.

With decision tables, users can conveniently import data and export data to Microsoft® Excel. Users can also edit decision tables directly in Excel by using a Blaze Advisor RMA.

25,000 – 34,999

Poor

2,500

25,000 – 34,999

Good

3,500

25,000 – 34,999

Excellent

4,000

35,000 – 44,999

Poor

3,000

35,000 – 44,999

Good

3,500

25,001 – 35,000

25,001 – 35,000

25,001 – 35,000

35,001 – 45,000

35,001 – 45,000

35,001 – 45,000

> 45,000

> 45,000

> 45,000

Income

Student Bronze

Student Gold

Student Platinum

Student Bronze

Student Gold

Student Platinum

Student Bronze

Student Gold

Student Platinum

Card Type

2,500

3,000

4,000

3,000

3,500

4,500

4,200

4,700

5,200

Credit Limit

Income

Card Type

Credit Limit

Student Bronze

Poor

False

Credit Limit

2,500

3,000

4,200

Student Gold

Good

True

Credit Limit

3,000

3,500

4,700

Student Platinum

Excellent

True

Credit Limit

4,000

4,500

5,200

1-Axis Vertical 1-Axis Horizontal 2-Axis Grid

Card Type

Credit Rating

Homeowner?

Income

25,001 – 35,000

35,001 – 45,000

> 45,000

Blaze Advisor decision tables display large numbers of conditions and actions in a variety of formats.



ScorecardsFICO® Blaze Advisor® lets you define and execute scorecards within a decision service (also known as rule service). A scorecard gives you an easy way to combine many factors into an overall measurement that can be used to drive your decisions. By assigning your own categories, ranges and associated scores to individual criteria, you can build up additive scores that predict things such as a customer’s relative worth, a prospect’s likelihood to accept a promotional offer or a mechanical component’s probability of failing within a given configuration.

For companies in sectors such as finance, government or other highly regulated disciplines, Blaze Advisor allows you to set, capture and record significant factors in calculating a score. By defining your own reason codes, scorecards become an auditable component of your decision processes. You can also import scorecards defined in PMML.

Blaze Advisor decision trees show a chain of conditions leading to a specific action.

Decision TreesAnother convenient way to represent sets of interrelated decisions is with decision trees. Decision trees allow you to trace a chain of conditions to a single appropriate action. Looking at branches coming from a decision point (or “node”) in the tree lets you quickly confirm that all applicable possibilities have been accounted for.

FICO® Blaze Advisor® supports decision trees with the flexibility to specify any number of levels and condition branches. Branches for a split are automatically generated based on the branch values specified by the user, ensuring complete range coverage and no overlap. The action nodes contain a single action value assignment. The visual tree editor allows interactive focus on any branch or node of the tree and dynamic updating of conditions and action.



Blaze Advisor scorecardsshow component factors andweights leading to an overallranking score.

Blaze Advisor decision flows control the execution order of decision entities with preview support.

Decision FlowA decision flow lets users create a data flow for a decision service (also known as rule service) to control the order in which the decision entities are executed. A decision flow can have splits, decision steps, loops, subflows and flow variables. Using these controls, the user can define the orchestration of rules in a way that is intuitive and easy to communicate with others.



Formula BuilderThe Formula Builder lets you create both simple and complex formulas without having to use Structured Rule Language (SRL). This feature makes formulas easy to construct and read. Business analysts can provide numeric input or enable formulas to derive that input using a wide variety of formula construction features, such as set operations, functions, expressions and others. For example, you could use SRL to write the following expression:

AllAccounts[every Account].balance.avg

Formula Builder provides a quicker method of choosing formula elements from pre-populated lists that requires no knowledge of SRL syntax and prevents spelling errors.

Grouping RulesOrganizations may have large numbers of rules reflecting many different types of decisions. Quite often it is possible to identify a subset of rules that contribute to a particular decision or part of an overall process, but that do not directly affect other parts of the application’s decision-making.

Blaze Advisor allows technical developers to group such rules into a ruleset. Rulesets offer important benefits in performance and organization. Functional groups within a company can work on rules “belonging” to them by accessing a particular ruleset. It also makes it possible to quickly find and edit rules covering a particular function by locating the ruleset dealing with that function. Rulesets also contribute to performance tuning, because individual rulesets can be set to use optimized inferencing (Rete III), sequential execution or compiled sequential execution for rule engine evaluation.

Rules and other decision process components may be grouped and stored in logical folders or physical files. Groups of developed components can be thought of as libraries of code that can be shared by different projects and applications.



Business TermsBusiness analysts who maintain their own rules sometimes prefer to use their own expressions for authoring rules that are independent of the operational data model or the application data model. This allows faster rule implementation while reducing dependency on other teams. Blaze Advisor’s Business Terms feature is designed to meet this need.

With Business Terms, users can define their own terms, value lists and term calculations in the RMA, and use these terms to author rules, decision tables and other decision entities. They only need to import the data model when they have completed the rules and are ready to publish the decision service (also known as rule service). Business Terms enables business analysts to augment the data model by adding their own derived variables.

Blaze Advisor Business Termsis used by business analyststo define their own vocabulary.



Advanced Rule FeaturesA number of specialized decision logic issues often becomes apparent in rule-driven applications. These include:

Decision Management for Business AnalystsOrganizations often want to enable business analysts to take more direct control over the decisions used in the company’s automated systems. In a decision management system, this control translates to rule maintenance — the ability to review, alter, create and delete the rules that implement business policies. Business analysts without technical training need a dedicated software application to give them the power and control necessary to do their work without exposing them to programming code.



Engine mode Blaze Advisor provides different engine modes so users can choose the most appropriate mode based on the nature of the rules in order to meet business and performance requirements. For example, if backward chaining is a desired capability for a specific set of rules, the optimized inferencing mode is the best choice. On the other hand, if a set of rules needs to be evaluated in a certain order and transaction volume is large, the compiled sequential mode may prove to be the best choice.

Effective dates for rules Often a rule will have a lifespan, outside of which it is not relevant. FICO® Blaze Advisor® supports explicit effective dates that can be defined for each and every rule, enabling the right rules to be executed while ensuring that ineffective rules have no impact on performance.

Data patterns On many occasions a rule may be relevant to a group of data items. The ability to refer to data patterns in rules means that rules can be made more widely applicable. This reduces the number of rules required and thus speeds development and improves performance. The data pattern syntax involves defining a pattern from class membership, set membership, data attributes or any combination of these.

Functions Some business definitions may not be represented best as a declarative business rule. When a procedural statement is the best fit, Blaze Advisor provides a “Function” capability that avoids having to use an external programming language, thereby reducing complexity and processing overhead. This allows you to control conditionally looped processing, incorporate Java methods and other advanced mathematical functions, or use calls to external processes.

Event rules andgoal-driven reasoning

Specific rule operations may be desired when information changes or when certain information is needed by rules. Blaze Advisor allows event rules that specify event conditions. In conjunction with other rules, these can be used to provide goal-driven (or “backward chaining”) reasoning. This can ensure that “expensive” data access is accessed only when it is essential.

Question sets For forms-based applications where rules are used to drive the questions asked of a user, questions are typically presented in groups. Blaze Advisor provides question sets to allow questions to be grouped together to improve the user experience and to reduce the amount of programming.

Rule priorities Within a ruleset it is occasionally desirable to ensure that some rules execute before others are considered. Blaze Advisor provides rule priorities that can be set for each rule, allowing fine-grained control over rule precedence within rulesets. Naturally, within sequential execution rulesets, all rule-firing order is explicitly determined.

Business analysts use anintuitive drag-and-drop toolto create rules.

Business analysts author rules in the RMA governed by an approval workflow.

The lifecycle management component in the RMA enables authorized users to review and approve changes made by others and to publish approved changes to an environment for testing or production. Users with administrative responsibilities can manage other users and create audit reports on all activities.

Users gain access to the rule maintenance editors through a standard login with their ID and password. Authorization facilities can control who has access to different types of template instances. As users make changes to template instances, including adding new instances or deleting old ones, the change history is persisted to allow tracking changes and reverting to older versions.



Blaze Advisor’s Rule Maintenance Application is such a tool. The RMA is a web-based tool that provides complete decision management functions to business analysts for the creation, maintenance and deployment of decision rules. RMA users do not need to know SRL; the editors provide an intuitive authoring experience for authoring rules in different formats such as decision tables, decision trees, scorecards and decision graphs. The RMA also provides a set of powerful tools to business analysts to test and validate the rules for logic correctness.

Building Custom Rule Maintenance ApplicationsWhile the default RMA provides the capability for authoring all types of new rules, some businesses prefer to constrain the range of activities or values business analysts can modify. This can be achieved by using custom templates. A custom template indicates a basic pattern to be followed by all rules of this type, indicating what can and cannot be changed. Technical developers can create different templates for different kinds of rules. Templates can also constrain other Blaze Advisor entities such as rulesets, lists of values, component syntax segments that can be reused by multiple rules, and more. Each of the replaceable fields in a template is referred to as a “value holder.”

Once a template is created, the next step is to specify what the user is allowed to put in the value holders. Blaze Advisor allows the technical developer to create constraints on each value holder. Some fields can allow type-in entries, while others present choice lists. Allowed values can be predefined in a static list, pulled at runtime from a database or other outside data source, or based on the results of other rules that have fired. The constraints for what may be entered in the value holders are referred to as “providers” in Blaze Advisor. Technical developers can also specify whether an entry is mandatory, optional or if it allows multiple values.

Obviously, templates are a very powerful way to organize and constrain different types of rules. You might create one template instance to represent current corporate policies and another with new rules under consideration for possible implementation. Either template instance could be loaded for use by your RMA or for testing in the development environment. The following table summarizes the three key aspects of maintaining rules according to a particular structure:

The utilities for creating custom rule maintenance applications are accessed by technical developers through the FICO® Blaze Advisor® development environment. Intuitive editors let technical developers choose the values or terms in a rule that should be exposed to the business analyst. Only those parts of the rule that are exposed will be editable in an RMA. Display properties let the developer present the editable and static parts of the template to the business analyst in any format desired, incorporating text, HTML formatting, and interaction formats such as radio buttons or drop-down menus that let the business analyst work in a business context, rather than having to read rule syntax.

Templates provide design patterns for rules, sections of rules or groupings of rules. They contain static sections and replaceable value holders. Templates can be reused to create distinct decision services.

Providers act as controls on allowed values for the value holders in a template. They specify how many values are allowed, whether information should be typed in or presented in a list, what formats are valid and more.

One template can be used to create many instances, each of which is a distinct entity with its own unique conditions and actions.



Rules Management

Components

The final step is to use the Rule Maintenance Application Generator to create the web-based rule maintenance application that the users will access. In the wizard you can choose titles and display characteristics for the web pages, what overall control buttons should be presented to the users and how files should be listed in the RMA.

Templates specify what items are presented to business analysts in a Rule Maintenance Application (RMA).

The ability to customize the display of the template instances allows the technical developer to provide business analysts with a customized editing experience.



Rules Implementation Repository Management and Version ControlsFICO® Blaze Advisor® stores rules and other project information in an XML-based repository. The repository can be implemented in an SQL database, in a no-SQL database such as MongoDB or in flat files in a standard file directory structure. Blaze Advisor can work with Source Code Management (SCM) systems to enable check-out, check-in and access control of files containing developed decision process components. FICO has also created a special repository versioning service, Blaze Versioning System (BVS), that integrates with both file-based and database-based repositories. Organizations can choose which versioning and source management system they wish to use, based on their internal development standards and desired functionality. Links to third-party SCM commands can be accessed directly from Blaze Advisor menus.

Blaze Advisor versioning capabilities lets techncial developers specify which rules and components should be tracked for version changes at an individual level. As business analysts use their rule maintenance applications to modify, add or delete rules, their changes are tracked and stored for later review and action. Each change is associated with a version number, the responsible author, the date and time of the change, and a comment for reference. Past versions can be viewed in a quick-reference history list or in a detailed syntax review. Multiple versions can be viewed in a split-screen mode to allow side-by-side comparison. If users need to “roll back” their changes to a previous version, the process is easy with single-button-click reinstatement of a past version to the current working version.

The Blaze RMA permitsversioning of rules for easytracking, comparison andreinstatement.



Identify and merge differencesusing the Visual Differences editor.With a versioned repository, users can compare different versions of a rule using the Visual Differences editor. This allows the users to visually see the differences between two versions, and if desired, merge the differences.

Management Properties, Queries and FiltersFICO® Blaze Advisor® is a centerpiece of FICO’s integrated approach to Decision Management. Management of rule projects requires an ability to keep track of many different rules and decision service components. Large-scale systems with multiple development teams may work with thousands or even tens of thousands of rules. Management Properties help technical users track and find items within a rule project. Blaze Advisor automatically keeps track of many attributes, such as creation date, last edit date, author, version and so on. But the real power comes in attaching your own properties to items such as rules, decision trees, decision flows and more. You can keep track of any property you wish to declare — responsible department, compliance regulation reference or development status. All of these properties can be queried and used to find matching objects within a single ruleset or an entire development project.

Management properties with custom labels and values can be used to find matching objects in the repository.



Queries are a powerful management tool, and Blaze Advisor allows tremendous flexibility when using them. In addition to simple queries of Management Properties, technical developers can create complex queries using the full power of the Blaze Advisor Java APIs to match and filter on a wide variety of attributes. Business analysts can develop and run queries in the RMA. Search and replace allows large-scale terminology changes to an entire rule project at once. Queries can be added to the Blaze Advisor Cross-Reference Browser to extend its power.

Queries allow you to searchfor template instances based on different attributes and management properties.

Release ManagementSoftware development requires efficient release management to promote tested projects to production status and to ensure a stable upgrade process. FICO® Blaze Advisor® includes release management as a built-in function. A complete decision process or a library of developed rules, tables, trees and other decision assets can be frozen as a released version and referenced by external applications or other rule projects. The released version is stored as a snapshot in time, separate from the main development source. Further development will not affect the operation of the released version. Blaze Advisor allows multiple released versions to coexist for flexibility in use and auditing.

Large team-based development projects also benefit from Blaze Advisor’s ability to support a combination of shared and private workspaces. A technical developer can download a copy of the main development code to his or her personal computer and work disconnected from the corporate shared repository. After development and unit testing have been completed in the private workspace, changes can be uploaded to the shared repository and made available to other technical developers. A full set of synchronization utilities allows technical developers to retrieve and upload specific items or entire projects, libraries and groups.





Release a project to make a copy of it in the same repository.

Blaze Advisor contains release management functions to:

• Publish your project or workspace to a target repository, making it easy to move releases between environments. You can choose to publish a complete copy or just the updated items.

• Import or export projects so that you can change the type of repository connection you are using or reuse project components in another project.

• Tag a project that has completed testing with a release number, and release it to another location in the same repository.

• Use filters to select only those items that you want to include when you release or publish a project.

• Generate a single deployable binary file for a compiled project and use the file for deployment in other environments.

These features allow you to use Blaze Advisor in a robust development/test/QA/production environment or in a much simpler one as required by specific projects.

For business analysts who use an RMA to maintain their business rules, Blaze Advisor provides an optional approval workflow to govern the release process. When enabled, changes must be reviewed and approved by the designated approver before they are published.

Business analysts canpublish approved rule changes in the RMA.



The Verifier gives clear guidance on problems with the rules based on user-selected criteria.



Testing Verification, Validation and Testing RulesAs the problems being solved with business rules get more complex, the need to validate and verify business rules becomes greater. Moreover, manual and automated testing of rule changes is often required before any changes can be put into production. FICO® Blaze Advisor® has several tools that facilitate testing. The Verifier performs a wide variety of static verification tests on decision entities in a project. It uses algorithms to highlight areas of potential conflict, inefficiencies in your rule architecture and semantic errors. Any anomalies that are found are reported in the Verification Results panel, along with the type, severity and location in the project where they were detected. A filter function allows you to select only those items that you want included for verification and validation, allowing you to focus on those elements that are ready for testing. Verification instances can be developed, modified and managed in your repository so that you can manage the various kinds of tests you want to apply at different stages of your project.

The BRUnit Test Runner shows the results of applying regression tests to updated rules.



While verification may be performed by both business analysts and technical developers, technical developers also need to be able to establish formal tests for business rules quickly and effectively. FICO® Blaze Advisor® contains a BRUnit test framework built on the industry standard xUnit framework. BRUnit stands for Business Rules Unit. This testing framework is fully integrated into Blaze Advisor. It provides technical developers with a convenient way of unit testing business rules applications. BRUnit allows you to write and organize test cases into suites, which can be stored in the same repository as your rules. You can run the test cases as often as necessary. The BRUnit Test Runner in the Builder IDE lets you quickly see which tests failed and which ones passed.

Business analysts use Decision Testing in the RMA to validate the rules.

Data profiling in a decision graph helps to visualize the impact of the sample data.



The RMA has additional tools for the business analysts to test the rules with bulk data. The Decision Testing tool in the RMA allows users to upload a csv file that contains the test data and execute a selected ruleset or other entities with the test data. The result is displayed in the RMA and can also be downloaded. This tool includes a wizard that guides the user through the configuration of the required test data.

For decision tables and decision graphs, you can attach a dataset (csv file) to evaluate the distribution of data records. This type of profiling allows you to visualize the impact of the sample data.

Deployment FICO® Blaze Advisor® Rule Server lets you deploy rule projects as decision services (also known as rule services). A decision service can be deployed on a supported server platform so that the decision logic encapsulated in the service can be accessible to multiple, concurrent, distributed clients.

Deploying a rule project as a decision service involves:

• Configuring a Rule Server for your deployment platform

• Specifying the rule project for the service

• Defining the service entry points that allow clients to access the service

Entry points are business methods that define the data to be passed to the service as arguments at runtime, and the results the client can expect to be returned.

Blaze Advisor provides Quick Deployer Wizards that guide you through these steps and generate the appropriate code and configuration files for your deployment.



Decision Management PerformanceIt’s critical to keep a constant pulse on the entire IT system, and Blaze Advisor delivers deployment tools to easily monitor and manage the IT ecosystem. Blaze Advisor has monitoring classes that capture information at all levels — rule server, decision services (also known as rule services), rules session, down to the execution level — but it requires coding. By wrapping monitoring classes around the JMX Framework in Java, coding to use monitoring classes is eliminated and users only need to subscribe to JMX events. For .NET, you would use MOM standards-based for .NET instead of the JMX Framework. Not only is it simple to integrate with management consoles, but it’s a better fit into IT processes. For instance, if an organization uses IBM Tivoli with the standard JMX interface, Blaze Advisor can be registered into these environments so that it’s possible to monitor and manage both the IBM Websphere application server and rules execution, lowering administrative costs.

Now that all the events are available in the monitoring framework, all the monitoring class activity can be logged to a file. The logging framework eliminates coding with an out-of-the-box configuration file that integrates with industry standard logging tools with adapters available for Log4J, Java Logging, Simple logging, Log4Net, Windows Events for .NET and open API for customer adapters.

In addition to configuration and execution monitoring and management, Blaze Advisor also supports decision performance monitoring, also known as business activity monitoring (BAM). The business performance of your strategy can be captured through user-defined events. The BAM framework pushes business performance information into a datamart, providing the building blocks for strategy orchestration/champion-challenger strategies.

One such monitor, the User-Defined Event Monitor, receives notifications about user-defined events. You can add SRL code to your project to send custom notifications during the execution of a rule service. For example, you can add SRL code that sends notifications when certain rules fire or when certain functionals are invoked.

Rule Execution Components

Rule ExecutionData manipulation through object/property references may use standardized input/output facilities for the data type (for example, database open/read/write commands), or may call on methods defined and stored with the object.

A rule is defined as “a statement that defines or constrains some aspect of the business....” It is “atomic” in that it cannot be broken down or decomposed.

A rule explicitly defines what action should result from a given set of conditions. Rules are often referred to as “declarative” because they declare a particular result that must occur any time the corresponding conditions are met.

As an example, consider a rule (expressed here in SRL for adjusting status and premiums of accountholders):

If the Outstanding_balance of Customer’s account exceeds $1000 and the Standing of Customer’s Account is “Overdue” and Customer’s ID starts with “AX3”

Then set Customer’s Status to “Watchlist” and increment Customer’s Rate by 0.05.

This example is fairly simple. The conditions examine specific characteristics of the customer and his or her account to determine the appropriate follow-up action and

Decision Flows graphically define the sequence of execution steps in a decision process, using events, loops, branches and tasks. Each task in the decision flow is defined by a collection of rules (ruleset), a decision table, a function or a subordinate decision flow, to name a few examples. Decision flows are reusable across multiple decision services.

Rulesets are groups of rules organized for clarity and efficiency of definition, execution and maintenance. Rules in a ruleset are executed as called upon by decision flow tasks. Rulesets, like all component types mentioned in this section, are reusable across multiple decision flows and decision services.

Decision Tables display rows and columns representing different conditions, and the resulting action or returned data value is defined by their intersection. Decision table data can be imported from external sources.

Decision Trees graphically depict chains of dependent conditions leading to an action. Trees can be imported from FICO® Model Builder, where they can be designed based on a combination of statistical data analysis and company policies.

Scorecards are a special form of a table that examines different properties or characteristics of an object or transaction, and assigns weights based on the values. All underlying weights are added to arrive at an overall score, which can be compared against other scores for a rank-based decision. Advanced features include the ability to track significant factors in the overall score.

Functions perform algorithmic or procedural processing, such as calculations, and execute external services. Blaze Advisor contains a rich programming syntax called Structured Rule Language (SRL) that allows array handling, looping, case selections and other manipulations familiar to programmers. SRL reduces or eliminates the need to link to external programs as part of a rule-based decision process.



rate adjustment. While individual rules of this sort can be simple to describe, the many possible combinations of condition values requires a way to quickly look at many rules to see which ones are appropriate.

For instance, consider the company in the previous example. If it has categories for four different types of account standings, 10 balance amount categories and 25 customer ID segments, there are 1,000 different combinations of possible conditions. The rule engine must locate one rule to be executed based on the one corresponding combination of conditions. To make matters more complex, there are three different properties that must be read in order to make the appropriate rule selection decision (Outstanding_balance, Account_standing and Customer_ID).

Blaze Advisor contains an advanced rule engine that delivers maximum performance for online/interactive and batch applications of rules. To accomplish this, it offers both optimized inference execution of rules and sequential execution of rules, each of which performs better in certain circumstances. Because the rule syntax is the same for all execution modes, and individual rulesets can be executed using the ideal approach, Blaze Advisor users can optimize performance across a range of systems and platforms without changing their code.

Inferencing ExecutionBlaze Advisor contains a rule engine that can execute rules based on an optimization of the “Rete” algorithm known as optimized inferencing (Rete III). This algorithm uses rapid pattern matching to relate the conditions that are true at any given moment to the appropriate rule or rules. In the account handling rule example, Blaze Advisor is much more efficient at determining that this rule is relevant when compared to “case” statements and “nested if” statements in procedural code. The more rules, conditions and objects included in a decision process, the more valuable the rule engine is in saving processing time.

Of course, rules do not exist in isolation. The results of one rule may influence many others. The previous rule was conditioned in part by the customer’s account standing being “Overdue.” It is likely that the determination of account standing comes from other rules such as:

If the Outstanding_balance of Customer’s account exceeds $5 and Customer’s Last_Payment_Date is earlier than today — 2 months

Then set the Account_standing of Customer’s Account to “Overdue.”

The rule engine maintains proper order dependencies between rules so that technical developers do not need to write explicit code to specify which rules affect others. Blaze Advisor provides very high performance for such data-directed reasoning (or “forward chaining”) using optimized inferencing.

Optimized inferencing is the most advanced commercially available inference engine, benchmarking more than 300% faster than its competitors. It was developed by Charles Forgy, Ph.D. — the inventor of the original Rete rule algorithm used in almost all production inference-based solutions in the industry today. Optimized inferencing



was developed specifically to make it possible for rule systems to deal efficiently with large amounts of data as well as large numbers of rules. While other rule engines slow down as rule complexity or transaction volume increases, optimized inferencing scales effectively to provide a high-performance enterprise solution. Rule engines based on the traditional Rete rule algorithm scale well as rules complexity increases, but performance degrades with an increase in data volumes. Optimizing inferencing is significantly more scalable and efficient, making the engine 10 times faster on large rulesets with large quantities of data to assess.

For example, in benchmark testing conducted by FICO, the optimized inferencing execution mode completed the standard “Miss Manners 128” benchmark invocation in just 0.32 seconds on a single CPU while the “Waltz 50” benchmark was completed in just 0.331 seconds. These represent 4,493 and 2,860 rules per second per CPU, respectively. These are industry-leading numbers and can easily be compared across platforms using the published test syntax. Additionally, Blaze Advisor exhibited near linear scalability as the number of available processors was increased.

Graph showing the relativeperformance of traditional Rete and optimized Rete for an increasing number of CPUs.

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FICO® Blaze Advisor® also supports a “backwards chaining” process that searches for rules necessary to determine the conditions for a rule under consideration. For instance, if the above rule determined that the account status was Overdue, it would trigger the potential execution of the first rule. But for that rule to be processed, it is also necessary to know the customer’s ID. We could specify a rule covering this need as follows:

Whenever Customer_ID is needed

Then ask (“Customer_Questions,” Customer).

In the above rule, “Customer_Questions” specifies a pre-defined set of questions designed to collect multiple data values at one time.

Sequential ExecutionNot all groups of rules require a Rete network to track rule interdependencies and maintain condition-matching tables for determining which rules to fire. Many applications need to call upon sets of rules that are independent. For instance, an automated system that checks applications for valid data needs to consider all validation rules for each data element. It is more efficient in such cases to simply evaluate each rule’s conditions in a fixed sequence. Each rule is evaluated to be either true, in which case a corresponding action is triggered, or false, in which case it is skipped and the next rule is checked. The results of a rule’s actions will not trigger a previously evaluated rule to be reconsidered.

Blaze Advisor allows this type of sequential rule execution to be chosen for a given ruleset, while other rulesets are evaluated with optimized inferencing. Sequential execution is often useful in batch applications, where a limited number of rules are invoked over and over for large numbers of data records. Each individual rule may cause a data update or an exception to be logged, but an individual rule is unlikely to change the conditions of another rule.

For the fastest possible execution of these types of rules, technical developers may choose to identify a set of rules for execution through automatically generated code. This code can be compiled and run on the host platform at native processing speeds, with no additional load from rule engine features processing. Blaze Advisor can generate code for both .NET and Java platforms with an option known as “compiled sequential” execution.



Graph showing the relativeperformance of sequential andcompiled sequential for anincreasing number of CPUs.

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For mainframe deployments, the critical performance issues are often not those of rule execution, but of data marshalling and data access. Using a Java-based solution in these circumstances can result in performance problems despite the very high-speed execution of rules possible with the compiled sequential processing. To address this issue, and allow direct access to data in COBOL copybooks and COBOL sub-programs, Blaze Advisor provides an option to generate COBOL code to replace the sequential execution of rules using the engine. This COBOL code can be used in a standard mainframe environment to maximize performance and throughput.

Blaze Advisor’s combination of optimized inferencing, sequential execution and compiled sequential execution on Java, .NET and COBOL gives rule architects the flexibility to take advantage of added-value rule engine features when they provide needed benefits and faster execution.



The sequential or batch tests — both of which used real customer projects — attained a rule execution rate of 187,728 rules per second per CPU for complex rules (representing 1,183,252 predicate evaluations per CPU per second) and 3,543,424 rules per second per CPU for simpler rules (representing 6,565,757 predicate evaluations per CPU per second). These numbers are extremely high for real customer rules, as opposed to the “if true then do nothing” kinds of rules often used for performance benchmarking. Additionally, Blaze Advisor exhibited near linear scalability as the number of available processors was increased.

Decision Services in ProductionBlaze Advisor runs on any size server or mainframe. It can run as a Java application or service, a .Net application or service or as a COBOL program. It can be invoked via an explicit API call from an application, or you can register event listeners to watch for changes in data or other asynchronous events to occur, triggering a corresponding decision service (also known as rule service).

In order to handle high-volume production loads, Blaze Advisor is architected to run multiple simultaneous rule execution processes in separate threads on both Java and .NET. Each thread is dedicated to handling the complete rule service processing for a service requestor, which may be a physical requestor, such as a web client in an interactive internet application, or a logical requestor, such as a queued service request in a batch application. Each thread is an instantiation of the Rule Engine processing code held in memory. These threads are referred to as Rule Agents.

The number of Rule Agents and their system properties are configurable by the system administrator – for instance, restart/recycle policies for ending one service request and beginning another. Administrators can even make configuration parameters dynamic, based on load factors as seen by an application server. Each Rule Agent that is running and available uses system memory, but adds more parallel load capacity to the overall application. This architecture ensures that Blaze Advisor is effectively and infinitely scalable to handle any desired load volumes, simply by adding more processing threads, in addition to the supporting system resources.

Overseeing the synchronization of Rule Agents and service requests is a layer of software known as the Blaze Advisor Rule Server. This software layer is built into the product so that system programmers at the customer site do not need to write custom code to manage Rule Agent availability, queue service requests and handle other coordination tasks, such as rule updates. The Rule Server is also the level of software that manages communications with application servers on the host system.

With this feature, customers can balance their hardware resource utilization against their throughput needs. Rule Agents can be configured to pass rule execution auditing information to files for later querying and reporting, and they can be used to support remote debugging within the production system over workstations running the Blaze Advisor IDE.


The Blaze Advisor Rule Server manages and coordinates Rule Agents and service requests.

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Rule redeployment is automatically managedwithout interrupting production operations.

Rule RedeploymentFICO® Blaze Advisor® has the capability to update rules at any time on the production system, without recompiling the production application and without interrupting production operations. The software layer that manages this updating is referred to as the Blaze Advisor Deployment Manager. The Deployment Manager recognizes a request to update production rules and works with the Rule Server to coordinate the updates with rule service processing that is already in progress. A copy of the new rule network is stored in memory. The Rule Server looks for the first Rule Agent that is or becomes idle after the redeployment request. It blocks any new service requests from accessing that Rule Agent while the Deployment Manager configures the Rule Agent to use the new set of rules. As soon as the Rule Agent is updated, the Rule Server allows new service requests to use it and the new rules. In the meantime, existing service requests continue processing with the old rules so that they are not placed in an inconsistent state. As each Rule Agent finishes its processing task, the Rule Server and Deployment Manager update the Rule Agent to use the new rules and release the rules for use by new service requests. Eventually, all Rule Agents are updated and the Deployment Manager goes idle until it receives another rule update request.

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Before you launch an important new product or change in strategy, it’s critical to understand the likely effect on your business results. Validating that your rules will fire as expected isn’t enough — you need to know whether your resulting decisions will help or hurt your business.

The difficulty of estimating business impact can delay or even stop new rules from being put into production, especially in areas that deal with customer strategies and risk management. Business analysts want to know in advance how changes to their rules will change the decisions they make, and how those decisions will impact their business.

Decision Simulator for Blaze Advisor makes it easy to add this capability to both new and existing rules projects. It allows you to run historical data through your latest rules and analyze the potential business impact prior to moving them into production. It includes a flexible framework with pre-packaged templates and reports that make it easy to set up simulations that allow business analysts to configure the data they want to use, along with the rule results, business metrics and reports that they want to include in their analysis.

With the Decision Simulator module you can:

• Validate logic and assess the impact of rule changes prior to deployment

• Avoid costly errors in strategies that might otherwise be missed

• Accelerate approval for putting rule updates into production

• Improve decisions by comparing alternative approaches

When creating a decision strategy for approving loans, for example, a credit officer will ask: What change will this new strategy have on my ratio of accepts to declines? Will this new strategy result in a different risk score distribution for accepted applicants? What impact will this strategy have on my expected profits?

Extending Blaze

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Generating Production Deployment FilesOnce a decision service (also known as rule service) has been designed and tested in the Blaze Advisor development environment, it needs to be exported to the production environment. Blaze Advisor contains a set of automated utilities, referred to as Quick Deployers, that create all the files necessary to deploy and access the decision services from a production application. Quick Deployers ask a series of questions that determine the specific configuration of the production environment and the desired operation of the decision service. They can deploy decision services as EJB session beans, Java Web Service and Java POJO Web Service for use with most of the major J2EE application servers, such as Oracle WebLogic, WebSphere and JBoss. If the selected application server supports the features, you can choose to run as a Message-Driven Bean, a stateless service or a stateful (synchronous or asynchronous) service. Quick Deployers can generate deployments for in-process Java, Microsoft Transaction Server, JSR-94 rule engine interface compliance and web services. Quick Deployers can also generate COBOL code or .NET deployments. In a Microsoft environment, Blaze Advisor can access COM+ objects, and through them, .NET objects. It also creates C# access code for Microsoft ASP .NET web services. Configuration files include the ability to use ANT makefiles for technical developers.

Decision Simulator can provide this type of information quickly — in minutes. It gives technical developers a point-and-click interface they can use to generate a simple but powerful web-based user interface that makes it easy for business analysts to configure and run simulations and reports to meet their specific needs.

Wizards guide the technical developer through the set up and management of simulation projects that connect rule services to data sources, calculate additional items of interest (such as profit), configure reports to analyze results and then provide these capabilities to business analysts. They can then run their own estimations, select the rules to invoke and data sources to use, and edit logic and values used in business calculations.

For example, an insurance company using Blaze Advisor to automate its underwriting rules could use Decision Simulator to identify whether the distribution of tier assignments has been accidentally skewed or unexpectedly high discount levels have been applied, and make the necessary improvements prior to deployment. A financial company using Blaze Advisor to streamline the processing of credit card disputes could use simulations to see if it would approve inappropriate claims or send too many items for manual review, and fix these potentially costly issues.

Whatever your decision goals are, Decision Simulator will help increase your agility by providing a safe environment to vet rule changes faster, while making it easier to compare alternatives and improve the quality of strategies to better achieve your business objectives.



Decision Simulator makes iteasy to analyze alternativestrategies side by side andcompare the results.

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Blaze Advisor can also be extended with analytics using other tools within the FICO® Decision Management Suite. This integration of business rules with predictive analytic models is a mainstay of decision management.

Blaze Advisor allows you to import decision trees from FICO® Analytic Modeler Decision Tree Professional and scorecards from FICO® Analytic Modeler Scorecard Professional, two SaaS products in the FICO® Analytic Cloud for mining customer data to create rules for segmentation, profiling and scoring.

Blaze Advisor also allows you to execute models from FICO® Model Builder, a complete workbench for developing and managing sophisticated predictive models, including neural nets, segmentation models, decision tree models and predictive scorecards. Model Builder is the platform FICO uses for all its predictive analytic model development.

In addition, Blaze Advisor supports PMML (Predictive Model Markup Language) models. Models supported include neural networks, linear and logistic regressions, decision trees and scorecards. This “white-box” integration allows the models to be viewed and edited in the Blaze Advisor environment, both in the IDE and in rule maintenance applications.

Integrating Analytics

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shows the import of a

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Blaze Advisor is a great complement to your existing IT ecosystems and to some of the trends that influence the way your IT department builds information systems. In particular, Blaze Advisor offers great value in a service-oriented architecture (SOA) and for those adopting Business Process Management.

One of the major benefits expected from an SOA is an increase in business agility due largely to a reduction in the time, cost and difficulty of making a change. The definition of functionality as coherent components or services with well-defined interfaces helps limit the impact of a change to a single service, making change easier to control and execute. Clearly, some services will implement a business capability that must be better able to adapt to change than others. These services are what is meant by decision services (also known as rule services) and are typically best implemented using Blaze Advisor. This allows the decision logic to be changed more easily and safely, independent of the rest of the composite applications in which the service appears.

Blaze Advisor and

the IT Ecosystem

Model Builder 7.5 has the ability to export its models and model entities as PMML files. Model Builder also generates additional elements that describe models more thoroughly in an extension to the standard PMML definitions, allowing Blaze Advisor to render the model with a greater degree of precision.

Finally, FICO’s optimization product, FICO® Decision Optimizer, lets you optimize business actions based on resource constraints, behavioral and economic measures, and business objectives to develop optimized business rules for use in Blaze Advisor.



Simplify a process by automating critical decisions.

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Organizations are using Business Process Management Suites (BPMS) to design and build more and more processes. A BPMS focuses on how a particular process should be carried out. It helps standardize processes, facilitates collaboration and compliance, defines and manages workflow, automates steps, and provides activity monitoring, alerts, process reporting and integration. What it does not do well is decide what should be done. Using Blaze Advisor to automate decisions in the “new process” can dramatically simplify it. You can often eliminate multiple steps in favor of a single decision service (also known as rule service) within the process.

If you manage decision services (also known as rule services) in this way, you can also manage and deploy process and decision changes independently. There is no reason the need to change a decision — for example, product pricing — should require a change in the process steps for ordering it. When you have long-running processes, keeping decisions separate also gives you the ability to change work in progress. The process definition is fixed for a particular item of work at the time that it is instantiated. For long-running processes, this can be a problem if the business rules and analytic models are not current when evaluated. If you manage decisions separately, and retrieve them when the process needs the decision, they will always be current.

An application architecture based on rules technology — one that has logic managed as an independent layer by dedicated services within the information architecture — enables an organization to more easily deploy business rules across multiple applications and communication channels. Domain experts within specific business units control business policies and rules while allowing IT resources to move to other development tasks without recoding or risking miscommunications.

Companies using rules technology can expect benefits in areas such as:

• Business agility

• Straight-through processing

• Compliance

• Application development costs

• Application maintenance speed and precision

• Logic and rule reusability

• Enterprise consistency

• Personalization of the interactions with customers, prospects, agents and employees

Decision Management

Technology Benefits



Join the Decision Management Community, the industry’s first online community focusing on decision rules management. In addition to providing technical developers with 24x7 access to answers and advice, the Community features a trial download of Blaze Advisor. After you try Blaze Advisor, you can discuss it with other users on the ideas exchange, or submit your ideas for improvement.

To join the Decision Management Community

and download the Blaze Advisor demo,

visit https://community.fico.com/welcome.

Decision Management

Community

FICO and Blaze Advisor are trademarks or registered trademarks of Fair Isaac Corporation in the United States and may be trademarks or registered trademarks of Fair Isaac Corporation in other countries. © 2016 Fair Isaac Corporation. All rights reserved. 4202PS_EN 03/16 PDF

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FICO (NYSE: FICO) is a leading analytics software company, helping businesses in 80+ countries make better decisions that drive higher levels of growth, profitability and customer satisfaction. The company’s groundbreaking use of big data and mathematical algorithms to predict consumer behavior has transformed entire industries. FICO provides analytics software and tools used across multiple industries to manage risk, fight fraud, build more profitable customer relationships, optimize operations and meet strict government regulations. Many of our products reach industry-wide adoption — such as the FICO® Score, the standard measure of consumer credit risk in the United States. FICO solutions leverage open-source standards and cloud computing to maximize flexibility, speed deployment and reduce costs. The company also helps millions of people manage their personal credit health.

Learn more at www.fico.com.

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