Cognitive System for Automated Regulatory Compliance Management

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Solution Overview

Problem

Entities face challenges in managing regulatory compliance due to the vast amount of changing text data and the need for human expertise to identify and adhere to laws, policies, and regulations, which can lead to compliance violations and legal issues.

Innovation Solution

A cognitive system using natural language processing (NLP) and named-entity recognition (NER) to automatically extract and analyze regulatory text, identify obligations, and determine compliance across jurisdictions, employing machine learning classifiers to filter and determine obligation-like content within text data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If automated NLP and machine learning classifiers are used to extract and analyze regulatory text, then productivity and efficiency in compliance management are improved, but device complexity and computational resources required increase

Engineering Contradiction:

Improvecompliance management efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments the complex compliance management task into distinct processing stages: text ingestion, NLP processing, named-entity recognition, obligation extraction, and classification. Each stage handles a specific aspect of the regulatory text analysis, making the overall complex system manageable through modular decomposition of functions.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces intermediary components such as the cognitive system and machine learning classifiers that act as mediators between the raw regulatory text data and the compliance determination outcomes. These intermediaries process and transform unstructured text into structured compliance information, bridging the gap between regulatory requirements and entity compliance status.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Loss of time

If automated cognitive systems with NLP are deployed to manage regulatory compliance, then loss of time in identifying obligations is reduced, but use of energy and computational resources increases

Engineering Contradiction:

Improvetime to identify obligationsVSAvoidcomputational energy consumption

Core Design Contradiction:

Loss of timeVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by pre-processing and indexing regulatory text data using NLP techniques before actual compliance queries are made. Named entities and obligations are extracted and stored in structured formats in advance, so that when compliance checks are needed, the system can quickly retrieve and match against pre-processed information rather than analyzing raw text each time.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent employs parameter changes by adjusting the complexity and depth of NLP processing based on the specific requirements of different regulatory texts and compliance scenarios. The machine learning classifiers can be tuned to different sensitivity levels, and the system can adapt processing intensity to balance between speed/accuracy and computational resource consumption.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11556938B2Managing regulatory compliance for an entity

Publication Date: 2023.01.17 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Various embodiments are provided for managing regulatory compliance for an entity in a computing environment by a processor. A law, policy, regulation, or a combination thereof extracted from one or more segments of text data from one or more data sources may be identified requiring an obligation to be performed by the entity.