Textual Risk Analysis for Automated Early Risk Alerts
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Solution Overview
Problem
Existing risk management systems rely heavily on manual, event-driven processes for risk identification and remediation, often using single data sources, which are inefficient and time-consuming, failing to identify and address risks promptly.
Innovation Solution
A computing system that integrates network interfaces, databases, and processing circuits to analyze textual data, generate affinitized data sets, and identify responsible parties for potential risk events, enabling early risk alerts through automated risk detection and notification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual, event-driven processes are used for risk identification, then system complexity is reduced, but risk identification speed and productivity deteriorate
Solution Approach 1:
The patent replaces manual mechanical risk identification processes with automated computer-based systems that use natural language processing, machine learning algorithms, and data mining techniques to detect risks in textual data, thereby increasing productivity while managing complexity through automation
Solution Approach 2:
The system enables self-service risk detection by automatically monitoring, analyzing, and identifying risks without requiring continuous manual intervention, allowing the system to detect and report risks autonomously based on predefined criteria and learned patterns
2Measurement precision
If single data sources are used for risk assessment, then data processing complexity is reduced, but risk identification completeness and measurement precision deteriorate
Solution Approach 1:
The patent merges multiple data sources including textual data from various platforms, structured data from databases, and unstructured data from documents into a unified risk assessment framework, enabling comprehensive risk identification through data integration and cross-referencing
Solution Approach 2:
The system creates a universal risk management platform that can process and analyze different types of data from multiple sources using a single integrated architecture, allowing the same system to handle diverse data formats and sources without requiring separate processing mechanisms
3Loss of time
If automated textual analysis is implemented, then risk identification speed improves, but computational resource consumption and energy use increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and indexing textual data, pre-training machine learning models, and establishing risk detection rules in advance, so that when actual risk detection is needed, the system can quickly query and match against pre-prepared structures rather than analyzing everything from scratch
Solution Approach 2:
The system dynamically adjusts computational parameters such as analysis depth, data sampling rates, and algorithm complexity based on risk priority and resource availability, allocating more computational resources to high-priority risk areas while reducing resources for low-risk monitoring tasks
Data Source
AI summary
A computer system includes circuitry for executing operations, including receiving an input text file comprising metadata, receiving risk enrichment data, and generating an affinitized data set based on the input text file and the risk enrichment data. Generating the affinitized data set includes the operations of determining a context-indicative keyword, determining a synonym of the context-indicative keyword, searching the input text file for the context-indicative keyword or the synonym, identifying the context-indicative keyword or the synonym in the input text file, and generating the affinitized data set, the affinitized data set comprising a risk descriptor determined based on the at least one of the plurality of context-indicative keywords and the metadata.