Automated Risk Assessment System for Unstructured Data
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Solution Overview
Problem
Current risk assessment processes face challenges in automating the conversion of unstructured data sources and dealing with non-standard data vocabulary, leading to suboptimal accuracy and increased likelihood of fraud and criminal activity due to manual operation.
Innovation Solution
A computer-based system and method for automated risk assessment that includes an enrollment module, data aggregation module, risk assessment module, and adjudication module to convert risk information into standardized codes using predictive models, enabling accurate and automated risk evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual risk assessment processes are used to handle unstructured data sources, then flexibility in processing diverse data formats is maintained, but accuracy rates decrease and fraud detection capability is reduced
Solution Approach 1:
The patent replaces manual mechanical processing of risk assessment with an automated computer-based system that uses optical character recognition (OCR), natural language processing (NLP), and machine learning algorithms to convert unstructured data into standardized formats. This substitution eliminates human error and inconsistency while maintaining the ability to process diverse unstructured data sources including documents, images, and videos.
Solution Approach 2:
The patent introduces an intermediary layer of automated data conversion technology that bridges unstructured data sources and the risk assessment engine. This intermediary includes OCR modules for document scanning, NLP modules for text extraction and interpretation, and standardized data formatting layers that translate various data formats into a uniform structure suitable for automated analysis.
2Adaptability or versatility
If traditional rule-based engines are used to process non-standard data vocabulary, then system simplicity is maintained, but processing capability and accuracy are limited
Solution Approach 1:
The patent transforms the processing parameters by implementing dynamic parameter adjustment capabilities in the machine learning models. The system learns from training data to automatically adjust thresholds, weights, and decision criteria based on the specific characteristics of each data source and risk type. This allows the system to adapt to non-standard vocabulary and diverse data formats without requiring complex manual configuration of rules for each scenario.
Solution Approach 2:
The patent introduces dynamic adaptability through machine learning models that continuously learn and improve from new data. The system evolves its processing capabilities over time by training on additional examples of unstructured data and risk patterns, enabling it to handle increasingly diverse and complex data types without proportionally increasing system complexity. The models dynamically adjust their internal parameters and structures based on learned patterns.
3Productivity
If automated conversion of unstructured data is implemented, then processing speed and consistency are improved, but data standardization challenges increase
Solution Approach 1:
The patent implements a universal data standardization framework that handles multiple data types and formats through a single integrated system. The standardized data model uses common schemas and data structures that can represent various unstructured formats (documents, images, videos, tables) in a unified manner. This universal approach enables consistent processing across different data sources without requiring separate standardization pipelines for each type, thereby managing complexity while maintaining high processing speed.
Data Source
AI summary
A computer-implemented method for interactive visualization of a risk assessment for an entity on a graphical user interface of a computer system includes receiving, by the computer system, unstructured risk data associated with an entity, parsing, by the computer system, the unstructured risk data to produce risk information elements during a time period, combining, by the computer system, the risk information elements that comprise a single event, categorizing, by the computer system, each event in a category, generating, by a computer processor, a risk assessment for the entity from the categorized events for each time period, and displaying, on the graphical user interface, the risk assessments for each time period on a risk timeline that includes a timeline and a numerical risk scale.