Rules Engine for Legal Entity Application Mapping
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Solution Overview
Problem
Current methodologies for associating legal entities with applications and deployments require manual processes, leading to inefficiencies and potential inaccuracies, especially for large international companies, as each legal entity has unique regulatory requirements and data sources that need to be managed manually.
Innovation Solution
A system and method utilizing a rules engine with customizable rules to automatically generate mappings between applications, deployments, and legal entities, including interactive user interfaces for review and override, leveraging system-level and attribute-level rules, and AI for enhanced data management and compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual processes are used to associate legal entities with applications and deployments, then flexibility in handling unique regulatory requirements for each legal entity is maintained, but inefficiency and potential inaccuracies increase
Solution Approach 1:
The system implements dynamic rule configurations that can be adjusted based on different legal entity requirements. The rules engine allows runtime modification of association rules without requiring system redesign, enabling the system to adapt to unique regulatory needs while maintaining automation. This resolves the contradiction by providing both operational flexibility and automated efficiency.
Solution Approach 2:
The system changes parameters by allowing configurable rule attributes that can be modified for different legal entities. Through the user interface, administrators can adjust rule parameters such as confidence thresholds, data source priorities, and mapping criteria to match specific regulatory requirements. This enables automated processing while maintaining the flexibility needed for diverse legal entity requirements.
2Adaptability or versatility
If manual processes are used to manage data sources for each legal entity, then customization for unique regulatory requirements is possible, but resource requirements and potential mistakes increase
Solution Approach 1:
The system implements a universal rules engine that serves multiple legal entities with different requirements through a single platform. The engine can load, store, and execute different rule sets for various legal entities without requiring separate systems. This multi-functionality reduces resource requirements while maintaining customization capability across all legal entities.
Solution Approach 2:
The system uses templates and reusable rule configurations that can be copied and adapted for different legal entities. Instead of manually creating unique configurations for each entity, administrators can replicate and modify standardized rule templates, significantly reducing the manpower and resources needed while maintaining customization for unique regulatory requirements.
3Productivity
If automated rules engine is implemented to associate legal entities with applications, then efficiency and accuracy improve, but system complexity increases
Solution Approach 1:
The system introduces a rules engine as an intermediary layer between data sources and the association process. This mediator component handles the complexity of rule evaluation, data source integration, and mapping logic, while presenting a simplified interface to users. The rules engine absorbs system complexity internally while maintaining automated efficiency, resolving the contradiction between automation and complexity.
Solution Approach 2:
The system segments the association process into distinct modular components: data source interfaces, rule definition modules, rule execution engine, and result validation layers. Each segment handles specific functions independently, making the overall complex system manageable through modular design. This segmentation enables automated processing while organizing complexity into maintainable, independent modules.
4Measurement precision
If comprehensive data sources are integrated to feed into rules for each legal entity, then mapping accuracy improves, but data management complexity and resource requirements increase
Solution Approach 1:
The system extracts and isolates only the necessary data elements from comprehensive data sources based on configured rules. Instead of processing all available data, the rules engine identifies and extracts specific attributes needed for legal entity association, reducing data management complexity while maintaining mapping accuracy through targeted data extraction.
Data Source
AI summary
An embodiment of the present invention is directed to allowing permissioned legal entity managers to define rules for a legal entity to derive an association of an application with a legal entity. The application manager may then be presented with an option to accept suggested associations or perform an override. Once a legal entity is ready, applications may be published to a system of record. Due to the vast amount of applications and entities available, the solution provides automation while still allowing overrides. The results of the classification may then be stored in a system of record and data quality suggestions sent to the source systems to clean source system data. An embodiment of the present invention may also associate other attributes to applications and deployments.