AI Risk Assessment System for Third-Party Dependency Analysis
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Solution Overview
Problem
Existing techniques for assessing third-party risk are inadequate as they rely on insufficient and outdated data, failing to accurately quantify risks and their interactions, and do not account for real-time changes or the capabilities and dependencies of third-party entities.
Innovation Solution
A system that generates a dataset associated with third-party entities, applies rules to determine sentiment, and uses machine learning models to categorize and score risks, taking into account dependencies and capabilities, thereby providing a real-time and comprehensive risk assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If periodic financial data is used for risk assessment, then the analysis can be performed with available data, but the assessment cannot provide real-time risk evaluation and actionable insights
Solution Approach 1:
The patent replaces traditional mechanical data processing systems with an AI-based system that automatically collects, processes, and analyzes diverse data sources in real-time. The machine learning models continuously monitor third-party entities and generate risk assessments without manual intervention, enabling timely detection of risk changes.
Solution Approach 2:
The system establishes continuous monitoring of third-party entities through automated data collection from multiple sources including financial statements, news articles, social media, and regulatory filings. This continuous action ensures that risk assessments are always based on current information rather than stale periodic data.
2Adaptability or versatility
If existing risk assessment techniques are used, then the process is simple, but the analysis cannot account for dependencies between risks or real-time changes
Solution Approach 1:
The patent segments the risk assessment process into distinct modules: data collection from multiple sources, data processing and cleaning, risk factor identification, dependency analysis, and risk scoring. This segmentation allows complex analysis to be performed through manageable, independent components that can be optimized separately.
Solution Approach 2:
The system is designed to handle multiple types of data from diverse sources (financial, operational, reputational, regulatory) and apply unified AI models to assess various risk types. The same platform can evaluate different third-party entities and risk factors, making the system universally applicable across different industries and scenarios.
3Measurement precision
If real-time data collection is implemented, then current risk assessment accuracy is improved, but data processing complexity and computational requirements increase
Solution Approach 1:
The system extracts only the relevant risk-related information from large volumes of data using AI-powered natural language processing and machine learning algorithms. By focusing computational resources on processing only the most pertinent data points and patterns, the system achieves high measurement precision while minimizing unnecessary computational energy consumption.
Data Source
AI summary
Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support functionality for identifying, quantifying, and mitigating risks. A dataset that includes a plurality of data items associated with one or more entities is generated and a set of rules is executed against the dataset to determine a sentiment for each data item of the plurality of data items. The dataset may be evaluated against a machine learning model configured to produce a set of risk categorizations that associate each of the plurality of data items with a risk category. Scoring metrics are generated for each of the data items based at least in part on the evaluating. The scoring metrics may account for an impact of dependencies and capabilities of the one or more entities on risks corresponding to the risk categorizations. A data structure may be created and displayed to the user based on the scoring metrics, where the data structure is configured to quantify risks identified for each of the entities and to identify actions configured to mitigate the risks for each entity.