Streamlined Auditing Engine for Entity Clustering
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Solution Overview
Problem
Current auditing methods are inefficient and inaccurate due to the inability to effectively utilize hundreds of millions of publicly and privately available data points to identify businesses that are likely to be in violation of laws and regulations, often relying on outdated and disconnected data sources.
Innovation Solution
A streamlined auditing engine that sources entity type data from multiple databases, uses entity clustering to identify linkages and generate graphs, and employs parallelized hardware for clustering, enabling the generation of initial inclusion lists of targeted entities for audit, thereby providing efficient and accurate audit results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of businesses for audit is used, then auditors can review each business individually, but the process is inefficient and cannot utilize hundreds of millions of data points to identify high-risk targets
Solution Approach 1:
The system performs preliminary clustering and risk assessment on hundreds of millions of data points before the actual audit selection. The entity clustering module pre-processes data to identify patterns and relationships, generating risk scores and candidate lists that guide subsequent audit decisions, thereby improving both efficiency and accuracy.
Solution Approach 2:
The system creates a virtual representation of the audit universe through graph databases and clustered data structures. Instead of manually reviewing each business, auditors work with cloned/synthesized risk profiles and candidate lists generated from the clustered data, enabling efficient analysis of massive datasets while maintaining audit precision.
2Measurement precision
If all available data points are analyzed to improve audit targeting, then identification of non-compliant entities improves, but processing time and computational resources increase
Solution Approach 1:
The system segments the massive dataset into manageable clusters using the entity clustering module. Data is divided into logical groups based on relationships and patterns, allowing parallel processing and reducing overall computation time while maintaining comprehensive analysis of all hundreds of millions of data points for accurate target identification.
Solution Approach 2:
The system performs clustering on the entire dataset upfront to create compressed representations, then uses these pre-processed clusters for subsequent audit targeting. This excessive initial action on all data points enables faster, more accurate querying later without re-processing the full dataset for each audit request.
3Adaptability or versatility
If traditional auditing methods are used, then auditors can maintain control over the process, but the system cannot leverage vast amounts of publicly and privately available data points
Solution Approach 1:
The entity clustering module serves as an intermediary between raw data sources and audit decision-making. It consolidates hundreds of millions of data points from diverse sources into structured clusters and risk profiles, simplifying the interface between complex data and auditors while enabling comprehensive data integration without overwhelming system complexity.
Data Source
AI summary
Methods, systems, and program products for streamlined auditing that receive an input audit request via the data interface; source entity type data (ETD) from one or more databases; prepare the ETD for input into an entity clustering module; match the ETD via the entity clustering module to locate linkages within the ETD and discover relationships amongst one or more entities identified within the ETD; cluster datapoints in the ETD that refer to the same real-world entities; analyze the ETD relationships via an entity intelligence module to identify and segment targeted entities, from the one or more entities, that are applicable to the audit request; generate inclusion lists of those targeted entities that are determined to fulfill the audit request; finalize the inclusion lists of targeted entities that fulfill the audit request to generate streamlined audit results; and output the streamlined audit results to an end user.