Risk Assessment Platform for Entity Match False Positives
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Solution Overview
Problem
Financial and non-financial institutions face challenges in accurately detecting and categorizing risk levels of individuals and entities due to vast and duplicative data across multiple databases, leading to manual effort and high false positive matches during screening processes.
Innovation Solution
A risk assessment platform that aggregates and standardizes data from various databases, applies weights and conditions to generate risk scores, and provides visual representations to enhance false positive detection and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual screening processes are used to review entities against databases, then institutions can detect high-risk individuals and entities, but the process requires significant manual effort and time due to vast and duplicative data across multiple databases
Solution Approach 1:
The system performs preliminary actions by automatically compiling consolidated profiles from multiple databases before the manual review process. Screening records are pre-aggregated, deduplicated, and organized into consolidated profiles that present only the most relevant information to reviewers, eliminating the need for manual data collection and compilation during the screening process.
Solution Approach 2:
The system introduces an intermediary automated processing layer between the databases and human reviewers. This intermediary automatically retrieves records from multiple databases, consolidates them into unified profiles, removes duplicates, and presents processed results to reviewers, thereby reducing both manual effort and processing time while maintaining detection accuracy.
2Reliability
If comprehensive screening of all entities is performed to ensure no high-risk individuals are missed, then risk detection coverage is improved, but the volume of false positive matches increases requiring additional manual review effort
Solution Approach 1:
The system applies local quality by creating customized consolidated profiles tailored to each screened entity's specific risk characteristics. Rather than applying uniform screening to all entities, the system consolidates only the most relevant records and information for each entity type and risk category, reducing unnecessary false positives while maintaining comprehensive coverage where needed.
Solution Approach 2:
The system extracts and separates truly relevant risk indicators from irrelevant matching data. By consolidating profiles that focus only on high-value screening information and removing duplicative or low-relevance records, the system maintains comprehensive risk detection coverage while reducing the complexity of manual review processes.
3Measurement precision
If data from multiple databases is consolidated into unified profiles for review, then screening accuracy is improved, but the process requires significant manual effort to obtain and compile records from various sources
Solution Approach 1:
The system performs self-service by automatically retrieving, consolidating, and organizing screening records from multiple databases without requiring manual intervention. The automated profile compilation service gathers data from various sources, deduplicates records, and presents consolidated profiles ready for review, thereby improving assessment precision while eliminating manual data collection efforts.
Solution Approach 2:
The system performs preliminary consolidation of records from multiple databases into unified profiles before the screening review process. This pre-processing automatically obtains and compiles data from various sources, organizing it into standardized consolidated profiles that improve assessment precision while eliminating the need for manual compilation during operational screening.
4Measurement precision
If numerous records with same name, geographical location, and roles are identified during screening, then comprehensive matching is improved, but additional manual effort is required to sift through false positive matches
Solution Approach 1:
The system segments the large volume of matching records into organized consolidated profiles grouped by entity type, risk level, and relevance. By dividing and structuring the data into manageable segmented profiles with clear hierarchies and categories, the system maintains comprehensive match detection precision while enabling reviewers to efficiently navigate and process results without being overwhelmed by volume.
Solution Approach 2:
The system applies partial action by consolidating and presenting only the most relevant matching records in prioritized consolidated profiles, rather than displaying all possible matches. This selective presentation maintains precision in identifying true matches while dramatically improving productivity by reducing the number of false positives that require manual review.
Data Source
AI summary
Various examples disclosed herein relate to risk assessment and analysis of entities with respect to potential financial dealings. In an example embodiment, a risk assessment platform is provided that includes one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media that, based on being read and executed by a processing device, direct the processing device to perform various functions. Specifically, the program instructions direct the processing device to receive an input with an indication of a target entity, identify, based on the input, multiple possible instances of the target entity from a database, and for each possible instance of the multiple possible instances, evaluate a strength of a match of the possible instance to the target entity for display thereof on a graphical user interface.