Self-Healing Data Clusters for Financial Risk Accuracy
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Solution Overview
Problem
Financial institutions face issues with outdated user data leading to incorrect risk assessments, declined authorized transactions, and incorrect user identification due to data linkage problems such as over-linkage or under-linkage.
Innovation Solution
The implementation of a self-healing data clustering system that compares new user data with existing data to determine relationships, correct data linkages, and improve risk assessment by generating a framework for data standardization and evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data clusters are formed and stored without updates, then data storage efficiency is improved, but data accuracy and reliability deteriorate due to staleness
Solution Approach 1:
The patent implements dynamic data clustering where clusters are not static but continuously updated and re-evaluated. The system dynamically adjusts cluster compositions by incorporating new data, removing outdated records, and re-linking entities based on current information, thereby maintaining both storage efficiency and data accuracy through adaptive restructuring.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring data freshness and cluster integrity. When data staleness is detected, the system triggers re-clustering operations that use feedback from data quality metrics to adjust cluster compositions, ensuring reliability is maintained without sacrificing storage efficiency.
2Speed
If data linkage is performed without standardization, then processing speed is improved, but measurement precision deteriorates due to inconsistent data formats
Solution Approach 1:
The patent applies preliminary data standardization before the actual clustering and linkage operations. By pre-processing data to establish consistent formats, schemas, and normalization rules beforehand, the system ensures that subsequent processing operations can proceed quickly while maintaining high matching accuracy through standardized comparison criteria.
3Device complexity
If traditional data clustering methods are used, then device complexity is reduced, but data linkage quality deteriorates due to over-linkage or under-linkage problems
Solution Approach 1:
The patent segments the data linkage process into distinct phases: initial clustering based on key identifiers, secondary refinement using additional attributes, and final validation through consistency checks. This segmented approach improves linkage quality by addressing different aspects of data matching in sequence while keeping each individual step relatively simple and manageable.
Data Source
AI summary
Disclosed are various embodiments for self-healing data clusters. One or more candidates are determined from the candidate pool to be evaluated with the new record. A unique pair combination is generated for each one of the candidates of the candidate pool and the new record. Next, candidate data for the one or more candidates is identified from the existing record based at least in part on one or more matching rules. A weight is assigned to one or more matching rules. Then, the candidate data of the one or more candidates and the new record is evaluated for a data linkage. A distance is calculated between each of the unique pair combinations. Finally, the candidates of the existing record and the new record are clustered into groups.