GRC Document Ranking via Cross-Reference Analysis
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Solution Overview
Problem
Governance, Risk, and Compliance (GRC) systems face challenges in efficiently retrieving relevant information due to the large amount of data, with conventional querying techniques returning numerous non-relevant results lacking useful order, leading to inefficient use of computer resources and time-consuming document reviews.
Innovation Solution
A processing platform is configured to define fields in electronic documents corresponding to GRC system data structures, identify relationships between documents through cross-references, and assign ranks to documents based on these relationships, allowing for query processing that retrieves and prioritizes relevant documents efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional querying techniques are used to retrieve information in GRC systems, then all documents matching search criteria are returned, but the results lack useful order and require users to review many non-relevant documents
Solution Approach 1:
The system performs preliminary ranking of documents based on cross-reference relationships before presenting results to users. By pre-computing ranks that reflect document importance and connectivity within the GRC system, the most relevant documents are positioned at the top of search results, eliminating the need for users to manually review numerous non-relevant documents.
Solution Approach 2:
The patent replaces manual document review with an automated ranking system that uses cross-reference analysis. Instead of relying on users to sequentially examine documents, the system automatically computes relevance scores based on the density and patterns of cross-references between documents, substituting computational analysis for human evaluation effort.
2Reliability
If conventional querying techniques are used to retrieve information in GRC systems, then all matching documents are returned, but this consumes excessive computer resources
Solution Approach 1:
The system applies partial action by ranking documents and presenting results in order of relevance rather than returning all matching documents equally. The ranking mechanism uses cross-reference density as a filter to prioritize documents, allowing the system to maintain completeness of information retrieval while reducing the effective workload by presenting only the most relevant results first.
3Ease of operation
If cross-reference relationships between documents are analyzed to improve search results, then relevant documents can be prioritized, but the system complexity increases
Solution Approach 1:
The processing platform is designed with multi-functionality, serving both as the GRC system backbone and as the ranking mechanism. The same infrastructure that stores and manages GRC documents also computes and maintains the cross-reference ranking data, eliminating the need for a separate ranking system and reducing overall complexity.
Data Source
AI summary
A method in one embodiment comprises defining a plurality of fields in a plurality of electronic documents, wherein the plurality of fields respectively correspond to governance, risk and compliance system data structures, identifying a plurality of relationships between the electronic documents based on one or more cross-references between fields of two or more different electronic documents of the plurality of electronic documents, and assigning respective ranks to the plurality of electronic documents based on the relationships. In the method, a query is received from a user device, and a listing of candidate documents of the plurality of electronic documents is retrieved in response to the query. Scores for respective ones of the candidate documents are computed based on at least the assigned ranks, and a response to the query is transmitted to the user device, wherein the response comprises the listing of candidate documents sorted according to the computed scores.