From Laws and Regulations to Decision Automation

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Regulations are a set of obligations that apply to corporations and individuals. They can be established through laws or under the authority of a governing body. Regulations may explicitly define processes and rules, but often they prescribe outcomes or performances without detailing how to achieve them. When an organization must comply with a regulation, it aligns its operations with the obligations specified in the regulation. Compliance is the action of ensuring this alignment. However, demonstrating compliance can be a challenge because organizations must be able to trace their implementation back to the regulation. To create traceability, a knowledge entity model (KEM) is developed. This model represents the regulation using vocabulary, concept maps, and business rules. The KEM is derived from the text of the regulation, breaking it down into vocabulary terms, concept connections, and business rules. Using the KEM, an automated solution can be created using decision automation and business process automation (DMN and BPMN). This solution links the business rules to the decision or process as a knowledge source, creating a traceable solution.

Business

Trisotech.com
From Laws and Regulations
to Decision Automation
Providing complete traceability and accountability.

Trisotech.com
Today’s Program
 Regulations affect an organization's processes and decisions and these need
to be traceable in some way to the source.
 Traceable means the language of the regulation is reflected in the process
or decision solution.
 The OMG standard Semantics of Business Vocabulary and Business Rules
(SBVR) can create this language in a knowledge entity model (KEM)
 We create a KEM for two paragraphs of the Dodd-Frank regulations that
limit the cost of mortgages.
 Finally, we will create a decision model of the regulation with the KEM.
REGTECH

Trisotech.com
Regulatory Compliance
 Regulation are the guidelines or restrictions on society’s entities that
are established through authority. They might be laws or authority
passed to a governing entity.
 Compliance is the practice of aligning processes and decisions with the
regulation or establishing policies and guidelines.
 Some regulations explicitly define processes and decisions, regulations
can ambiguously state that best practices, or judgement should be
followed.
 In all cases record keeping is required.
REGTECH

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The compliance challenge
REGTECH

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A Proposition for Best Practices in Compliance
 Compliance with rules and regulations is best supported with a
knowledge entity model (KEM).
oA KEM is a representation of a regulation using a vocabulary,
concepts and business rules
oThe KEM is lifted directly from the text of the regulation. The
words of the regulation becomes elements of the vocabulary,
concepts and rules.
oThe concept model becomes a canonical data model and
processes, and decision can directly use these models to
implement the business logic described by the business rules
REGTECH

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Example: 2 Paragraphs (126.32) E&F from Dod-
Frank High-Cost Mortgage Regulations
ATR/QM

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Knowledge Entity Model
A concept model that clearly and unambiguously
models a business area’s operations by creating a
vocabulary of defined terms, connecting these terms
with concept maps and assembling business rules that
connect concepts, terms and operational constraints.
REGTECH

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A Proposition for Best Practices in Compliance
oCore concepts:
• In the KEM a vocabulary is a set of terms that are used in a
particular business topic. For instance, a common compliance
terms might include covered activities, allowable range.
• A concept model structures the terms of the vocabulary. A well-
built concept model will convey the know-how of a problem
domain. Most concept models have a central topic, and the
terms articulate the details of the topic
• Business rules use the terms and the concepts to describe the
logic that guides, constrains behavior.
REGTECH

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Best Practices in Compliance
oProcedure:
•Select the terms from the regulations
•Develop the concepts by establishing the
relationships between the terms
•Create business rules that use the concepts
•Create decision and process models that
implement the concepts
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Best Practices in Compliance: Vocabulary Terms
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Best Practices in Compliance: Terms
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Best Practices in Compliance: Concepts
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Best Practices in Compliance: Concept Diagram
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Best Practices in Compliance: Business Rules
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Best Practices in Compliance: Decision Model
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Generative AI can aid businesses, especially in the banking and finance industry, to meet regulatory compliance challenges by extracting important terms, creating concept models, and generating code to align with specified obligations. By utilizing a knowledge entity model (KEM), organizations can achieve traceable implementations, reduce errors, and minimize subjective interpretations when integrating decision models with regulatory requirements.

Biz Talk Demo slideshareerios

Business rules

Doug Walters

A short & plain english definition of Business Rules, which are a key element in systems definition. In theory, you can express a system entirely through the constructs of Business Rules. However, in practice, there is a law of diminishing returns in this effort, which the practitioner begins to sense through experience. The need to identify business rules as early as possible in the discovery phase is increasingly driven by the possibility to feed these rules together with process maps and thereby automatically generate executable code

BUSINESS RULE MANAGEMENT FRAMEWORK FOR ENTERPRISE WEB SERVICES

ijwscjournal

Making a business rule extraction more dynamic is an open issue, and we think it is feasible if we decompose the business process structure in a set of rules, each of them representing a transition of the business process. As a consequence the business process engine can be realized by reusing and integrating an existing Rule Engine. We are proposing a way for extracting the business rules and then to modify it at the runtime. Business rules specifies the constraints that affect the behaviors and also specifies the derivation of conditions that affect the execution flow. The rules can be extracted from use cases, specifications or system code. But since not many enterprises capture their business rules in a structured, explicit form like documents or implicit software codes, they need to be identified first, before being captured and managed. These rules change more often than the processes themselves, but changing and managing business rules is a complex task beyond the abilities of most business analysts. The capturing process focuses on the identification of the potential business rules sources. As business logic requirements change, business analysts can update the business logic without enlisting the aid of the IT staff. The new logic is immediately available to all client applications. In current trend the rules are modified or changed in the static time phase. But this paper provides to change the rules at the run time. Here the rules are extracted from the services and can be a changed dynamically. The existing rules are modified and attached to source code without hindering service to the end user which can be achieved with source control systems. When the rules are revised, it provides a path in budding new business logic. This new business logic can be adopted for the efficient software development.

BUSINESS RULE MANAGEMENT FRAMEWORK FOR ENTERPRISE WEB SERVICES

ijwscjournal

BUSINESS RULE MANAGEMENT FRAMEWORK FOR ENTERPRISE WEB SERVICES

ijwscjournal

BUSINESS RULE MANAGEMENT FRAMEWORK FOR ENTERPRISE WEB SERVICES

ijwscjournal

Solving Semantic Disparity and Explanation Problems in Regulatory Compliance

Dr.-Ing. Sagar Sunkle

Modern enterprises increasingly face the challenge of keeping pace with regulatory compliances. Semantic disparity between regulation texts, their interpretations, and operational specifics of enterprise often leads enterprises to situations where it becomes difficult for them to establish what compliance means, how they are supposed to affect it in the operational practices, and how to prove that they comply when asked for explanations of (non-)compliance. We take a step toward reducing the semantic disparity by using semantic vocabularies to map regulations with available operational details of enterprise and utilize them in enacting compliance. We also propose to provide explanations of proofs of (non-)compliance. We report our ongoing work in this regard using the design science research (DSR) paradigm. Initial iterations of design cycle from DSR have been useful to us in identifying and matching stakeholder-specific goals in solving these problems.

With recent regulatory advances, modern enterprises have to not only comply with regulations but have to be prepared to provide explanation of proof of (non-)compliance. On top of compliance checking, this necessitates modeling concepts from regulations and enterprise operations so that stakeholder-specific and close to natural language explanations could be generated. We take a step in this direction by using Semantics of Business Vocabulary and Rules to model and map vocabularies of regulations and operations of enterprise. Using these vocabularies and leveraging proof generation abilities of an existing compliance checking technique, we show how such explanations can be created. Basic natural language explanations that we generate can be easily enriched by adding requisite domain knowledge to the vocabularies.

RuleML2015: Explanation of proofs of regulatory (non-)complianceusing semanti...

RuleML

With recent regulatory advances, modern enterprises have to not only comply with regulations but have to be prepared to provide explanation of proof of (non-)compliance. On top of compliance checking, this necessitates modeling concepts from regulations and enterprise operations so that stakeholder-specific and close to natural language explanations could be generated. We take a step in this direction by using Semantics of Business Vocabulary and Rules to model and map vocabularies of regulations and operations of enterprise. Using these vocabularies and leveraging proof generation abilities of an existing compliance engine, we show how such explanations can be created. Basic natural language explanations that we generate can be easily enriched by adding requisite domain knowledge to the vocabularies.

EA_More_Than_Just_StandardsDavid Rudawitz

CHAPTER Modeling and Analysis Heuristic Search Methods .docx

tiffanyd4

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CHAPTER Modeling and Analysis Heuristic Search Methods .docx

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