Core Reconciliation System for Cross-Platform Data Validation
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Solution Overview
Problem
Insurance brokers face labor-intensive and error-prone manual processes in reconciling data across independent systems, leading to inaccurate and out-of-sync data that affects financial and operational processes.
Innovation Solution
A core reconciliation system that facilitates cross-platform data validation and reconciliation, using a core recon device to identify discrepancies and rectify them through a processor assignment queue, leveraging machine learning and AI to automate data processing and filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual cross-system verification is used to validate and reconcile data types, then data accuracy can be maintained through human review, but the process becomes labor intensive and highly prone to human error
Solution Approach 1:
The patent replaces manual mechanical verification processes with an automated computer-based system that performs cross-system data validation and reconciliation. The system automatically queries multiple data sources, compares data points, identifies discrepancies, and generates exception reports without human intervention, thereby eliminating human error while maintaining data accuracy and significantly improving processing efficiency.
Solution Approach 2:
The system enables self-service automated verification where the computer system independently performs data validation, discrepancy identification, and exception reporting without requiring manual human review. The automated processes serve themselves by systematically querying data sources, comparing values, and generating reports autonomously, freeing human staff from labor-intensive manual verification tasks.
2Stability of the object's composition
If manual verification processes are implemented to reconcile data across independent systems, then data consistency can be achieved, but the labor intensity and processing time increase significantly
Solution Approach 1:
The patent replaces time-consuming manual verification processes with automated computer-based validation that instantly queries multiple independent data sources, compares data points, and identifies inconsistencies. This substitution maintains data consistency through systematic automated checking while reducing processing time from manual review periods to automated instantaneous or near-instantaneous validation cycles.
Solution Approach 2:
The system implements continuous automated data validation and reconciliation processes that operate without interruption, systematically querying data sources and comparing values in an ongoing manner. This continuous automated action ensures data consistency is maintained at all times without the intermittent delays associated with manual verification, eliminating gaps where inconsistencies could go undetected.
3Productivity
If automated data processing is implemented to improve efficiency, then processing speed increases, but the system becomes more complex requiring integration across multiple independent systems
Solution Approach 1:
The patent implements a universal automated verification system that can query and validate data across multiple different independent data sources through standardized interfaces. The system performs multiple functions including data retrieval, validation, comparison, discrepancy identification, and report generation within a single integrated platform, thereby improving processing speed while managing complexity through multi-functional design rather than requiring separate systems for each task.
Solution Approach 2:
The system acts as an intermediary layer between multiple independent data sources, providing a unified interface for automated data validation. This intermediary approach simplifies integration complexity by centralizing the coordination of multiple data sources through a single automated verification platform, allowing high-speed processing without requiring direct complex point-to-point integration between all underlying systems.
4Measurement precision
If comprehensive data validation across all data points is performed, then data accuracy is maximized, but the resource consumption and processing overhead increase
Solution Approach 1:
The patent extracts and focuses validation efforts specifically on data points that are most critical for financial and operational processes. Rather than uniformly validating all data points with equal resource allocation, the system identifies and prioritizes key data elements requiring validation, thereby maintaining high validation accuracy for critical data while reducing computational resource consumption on less critical data points.
Solution Approach 2:
The system applies different validation intensities to different data points based on their importance and risk characteristics. Critical financial data receives comprehensive validation with high accuracy requirements, while less critical operational data receives streamlined validation. This local quality approach maximizes validation accuracy where needed while optimizing resource consumption across the overall system.
Data Source
AI summary
Systems and methods for validating and reconciliating data with a core reconciliation (recon) device. The core recon device may be configured to receive data from various data stores from various different independent systems. The core recon device may be configured to facilitate users such as agents or brokers to perform cross-platform data queries from all the data stores from all the independent systems. In embodiments, the core recon device may be configured to receive all the cross-platform queried data and then validate and reconcile all the received data into a single processor assignment queue. In the embodiments, the core recon device may be used by the user to identify any particular type of desired discrepancies from all the queried data. In the embodiments, the core recon device may then be used by the user to respectively work towards verifying and rectifying the identified discrepancies stored within the processor assignment queue.