AI-Assisted Risk Assessment Interface for Investigation Efficiency
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Solution Overview
Problem
Manual review of case files for investigation is tedious, time-consuming, and inaccurate due to the large volume of documents and clues, and existing automated technologies fail to provide an accurate replacement.
Innovation Solution
A computer system configured to provide an interactive user interface for generating feedback to AI models, allowing for efficient investigations by accessing data from multiple databases, applying AI models to generate assessments, and dynamically updating the user interface with relevant data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of case files is performed by investigators, then detailed analysis of clues and documents can be conducted, but the process becomes tedious, time-consuming, and inaccurate due to the large volume of data
Solution Approach 1:
The patent introduces an intermediary system comprising AI models, natural language processing modules, and data visualization components that mediate between the raw case file data and the investigator. This intermediary automatically processes documents, extracts entities, identifies relationships, and presents synthesized findings, thereby maintaining accuracy while dramatically reducing the time required for manual review.
Solution Approach 2:
The patent replaces the mechanical manual review process with automated computational systems including machine learning models, natural language processing algorithms, and database querying systems. These automated systems perform data extraction, entity recognition, relationship mapping, and risk assessment functions that previously required manual human analysis, thereby reducing time loss while maintaining or improving accuracy.
2Productivity
If automated technologies are used to replace manual investigation, then time efficiency can be improved, but accuracy is insufficient as automated systems cannot provide satisfactory replacement
Solution Approach 1:
The patent implements feedback mechanisms where AI model predictions and assessments are presented to investigators who can review, correct, and provide feedback on the automated analysis. This human-in-the-loop feedback system allows the automated system to maintain high productivity while accuracy is validated and refined by human expertise, creating a synergistic relationship between automated efficiency and human judgment.
Solution Approach 2:
The patent creates a multi-functional system that combines automated data processing capabilities with human analytical skills. The system performs multiple functions including automatic document processing, entity extraction, relationship mapping, risk scoring, and presentation of findings, while also serving as a tool that enhances rather than replaces human investigator capabilities. This universal system addresses both productivity and accuracy requirements simultaneously.
3Productivity
If simple criminal history background checks are performed, then the process is quick and efficient, but the information provided is limited and may be inaccurate due to sealed or purged records
Solution Approach 1:
The patent expands the investigation beyond the traditional single-dimension criminal history check by incorporating multiple data dimensions including alternative data sources, financial records, property ownership information, social media data, and relationships with other entities. This multi-dimensional approach allows the system to quickly assess risk while compensating for limitations in traditional criminal history records that may be sealed or purged.
Solution Approach 2:
The patent merges multiple data sources and assessment methodologies into a comprehensive risk evaluation system. By combining criminal history data with alternative data, financial information, relationship networks, and behavioral patterns, the system creates a unified assessment that is both quick to generate and more complete than traditional background checks alone, thereby maintaining productivity while reducing information loss.
Data Source
AI summary
Systems and methods are disclosed herein for reducing a risk of associating with a client that may engage in illegal activity. A system accesses data associated with an entity for a given context, applies a plurality of AI models to the data based on the context to generate a plurality of AI assessments. Data for showing risk factors, assessments of the risk factors, and data for evaluating risk factors can be transmitted for rendering in a user interface in a display device. Analyst feedback can be received and used to update the AI models.