Regulation implementation has become complex recently as organizations face mounting pressure to adhere to the fast pace of changes in regulation with multiple regulatory considerations. Regulations are often challenging and can vary depending on the industry, location, and type of operations. This complexity can create challenges for organizations in understanding, interpreting, and implementing these regulatory changes.

How are these challenges impacting organizations?

The challenges of regulation implementation can have a significant impact on organizations.

  1. The complexity of regulations and the fast pace of changes can make it difficult for organizations to keep up with the latest requirements, leading to potential issues such as legal and financial penalties, reputational damage, and loss of business opportunities.
  2. The resources required to implement changing regulations can be substantial. Changing Regulation implementation may involve hiring additional staff, investing in new technologies or systems, and conducting regular assessments. These costs can strain organizations' budgets and resources, impacting their overall financial performance and competitiveness. This is a critical concern with the growing volume of regulatory change, implementation, and embedding of regulatory change.
  3. Integrating regulation requirements with existing business processes can be challenging, especially if different systems or databases are involved, such as government databases, background check systems, and immigration systems, as other systems may use different data formats or protocols.

How can organizations handle regulation implementation challenges?

The better solution is to use an advanced decision management suite for organizations to handle the challenges of regulation implementation, as it enables organizations to quickly adapt to new regulatory requirements and make informed decisions based on regulation and business insights. It offers the advantage of modeling, managing, governing, and scalable solutions for these complex implementation scenarios.

Residency Permit Eligibility

As part of a regulation implementation, government agencies are responsible for determining the eligibility of applicants for various permits and benefits. For example, in the case of a resident permit, the agency needs to ensure that the applicant meets specific criteria before approving their residency application. One of the simple rules that the agency might use to assess eligibility is whether the applicant and spouse have lived at a particular address while married and shared the same address for at least seven years of the last ten years. In this scenario, we will explore how this rule can be applied to determine an applicant's eligibility for a resident permit.
Decision Graph

We have implemented this Permit Eligibility Decision using five significant decisions.

  1. Adjust for 10 Years of History – This decision adjusts the from and to dates relevant to the last ten years using the inbuilt dateAdd() and today() functions for each input data.
  2. Match Applicants – In this decision, to find common addresses between the applicant and the spouse where they lived together, we join two input data collections based on their address. This provides us with a collection of Common Addresses for both the applicant and spouse and their date periods.
  3. List of Applicant and Spouse Living together – Based on the common address list, we create a new collection(common dates) with overlapping date periods for both applicant and spouse where they were at a common address. This helps to eliminate further processing of date periods in which they were not living in the same address.
  4. Calculate Duration – In this decision, we calculate the married and lived-together duration in years for each address by considering common dates and looping through marriage periods inside a decision table. At the end of the loop, by using a decision table function, we calculate the total married and lived together duration in years.
  5. Determine Permit Eligibility – This decision uses a decision table to determine the permit eligibility using total married and lived together duration. Since the dates are already adjusted for the last ten years in step 1, at this stage, we only consider if the total married and lived together duration is seven years or more. In this decision table, a description is set as a notification.

And also, by looking at the input data, we stop executing the decision graph if the Applicant and Spouse’s total marriage periods are less than seven years in the last ten years. This decision is achieved by having a Boolean condition for the decision node. The decision graph is executed only if the condition is true; otherwise, permit eligibility will be set as ineligible. A notification will be created as this applicant and spouse’s married duration is insufficient for a Resident permit. The complete model is as follows:

Define Test Cases

We have created multiple test cases to cover different scenarios. This allows us to rapidly implement the modifications to the model along with changing regulations and validate against those test cases. After each modification, we can run the test cases to validate that the model delivers the desired output.


Creating test case documents not only allows for manually creating test cases but allows for importing multiple test cases from Excel documents. This increases organizations’ agility to respond to rapid regulatory requirements.
Test cases also help to improve the reliability of the system. By testing the model under different conditions and scenarios, you can identify potential issues and ensure the model performs as expected. To explore more about this project, visit FlexRule Resource Hub.

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Implementing fast-changing and complex regulations can lead to customer dissatisfaction, reputational decline, and massive fines from regulatory bodies that organizations can avoid falling in. All they need is a system that can adapt to rapidly changing regulations, easily integrate with existing systems, and clearly reflects logic implementation for stakeholders, leading to the final decision and why it was made.
The tabular form of business rule modeling and the Decision Graph, which supports visual modeling techniques, enrich the advanced decision management suite to provide a platform for designing a highly scalable and transparent model which allows organizations to cope with the complexity of regulation implementation and agility of the regulations.

Last updated May 4th, 2023 at 04:53 pm, Published May 3rd, 2023 at 04:53 pm