Multi-level Analytical Record for Automated Risk Detection
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Solution Overview
Problem
Existing systems face challenges in accurately identifying and mitigating risks associated with multiple entities within a network, as risk detection often relies on unstructured data, human supervision, and historical calibration, which can be inefficient and prone to subjectivity.
Innovation Solution
A risk analysis platform processes data objects from various sources to generate a multi-level analytical record, identifying relationships between entities and determining risk indicators using unsupervised tests like break point analysis and peer group analysis, enabling the identification of patterns of risky behavior without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If risk detection relies on unstructured data and human supervision, then flexibility and adaptability are maintained, but efficiency and objectivity deteriorate
Solution Approach 1:
The patent replaces manual human analysis of unstructured data with automated machine learning models and algorithms. The system uses computational methods to process data objects, generate analytical records, and determine risk indicators without human intervention, thereby improving both efficiency and objectivity simultaneously
Solution Approach 2:
The system enables self-service risk detection by automatically processing data objects through multiple analytical tests (break point analysis, peer group analysis, outlier detection) and generating risk indicators autonomously. The platform serves itself by continuously learning from data patterns and making independent risk assessments without requiring human calibration or supervision
2Measurement precision
If risk analysis processes multiple data sources and entities comprehensively, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments the complex risk analysis process into distinct modular components: data object reception from multiple sources, analytical record generation, various analytical tests (break point, peer group, outlier detection), and risk indicator determination. Each module handles a specific aspect of the analysis, making the overall complex system manageable and maintainable while preserving comprehensive analysis capabilities
Solution Approach 2:
The analytical record structure serves multiple functions simultaneously: it stores relationships between data objects, enables various types of analytical tests, supports risk indicator calculation, and provides a standardized format for different data sources. This multi-functional design reduces system complexity by using a single unified structure rather than separate specialized components
3Productivity
If automated processing of large volumes of data is implemented, then productivity increases, but loss of information may occur
Solution Approach 1:
The patent implements a nested analytical structure where data objects are processed into analytical records, which then undergo multiple layers of analysis including break point detection, peer group comparison, and outlier identification. Each analytical layer nests within the previous one, allowing comprehensive processing of large data volumes while preserving detailed risk patterns at multiple levels of abstraction
Data Source
AI summary
A device may receive, from sources, data objects identifying values relating to entities for which a risk indicator is to be determined, and may process the data objects to generate an analytical record that identifies relationships between values of different data objects. The device may determine, based on the analytical record, the risk indicator corresponding to one or more entities. The risk indicator may be determined based on at least one of: a comparison between the analytical record and a data structure that identifies expected values of one or more of the data objects; an identification of a group of the entities, and an outlier from the group of the entities based on the analytical record; or an identification of a change in behavior of the one or more entities based on the analytical record. The device may perform an action based on determining the risk indicator.