Automatic Asset Data Reconciliation System
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Solution Overview
Problem
Large organizations face challenges in reconciling discrepancies between asset information from physical discovery mechanisms and financial systems, leading to a burdensome and time-consuming process that is prone to further changes during reconciliation.
Innovation Solution
A method for automatically reconciling discrepancies in asset data between physical discovery software and financial systems using user-configurable rules and actions, allowing for automated updates, deletions, or changes to the financial system data, with optional logging and undo capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual reconciliation methods are used to compare asset information between physical discovery and financial systems, then accuracy of discrepancy identification can be maintained, but the process becomes extremely time-consuming and labor-intensive
Solution Approach 1:
The system performs self-service by automatically comparing asset information between physical discovery and financial systems without requiring manual intervention. The computer automatically identifies discrepancies, determines their types, and executes reconciliation actions based on predefined rules, eliminating the need for human analysts to manually review each discrepancy while maintaining high accuracy.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computer-based system that uses algorithms and predefined rules to identify and reconcile discrepancies. This substitution of mechanical human analysis with automated computational processes dramatically reduces reconciliation time while maintaining or improving accuracy through consistent rule-based evaluation.
2Reliability
If comprehensive manual review of all asset discrepancies is performed, then complete accuracy can be achieved, but the complexity and resource requirements of the process increase significantly
Solution Approach 1:
The patent segments the reconciliation process into distinct automated stages: retrieving asset information from both systems, comparing the information, identifying specific discrepancy types, determining reconciliation actions, and executing those actions. This segmentation transforms a complex monolithic manual process into manageable automated components, reducing overall process complexity while ensuring comprehensive coverage of all discrepancies.
Solution Approach 2:
The system changes parameters by using predefined rules and thresholds to automatically categorize discrepancies and determine reconciliation actions. Instead of requiring complex human judgment for each case, the system evaluates specific parameters (such as asset status, value thresholds, and discrepancy types) against predefined criteria to automatically determine the appropriate reconciliation action, simplifying the overall process complexity.
3Productivity
If automated reconciliation is implemented to reduce manual effort, then processing speed increases, but the ability to handle complex judgment scenarios may be reduced
Solution Approach 1:
The patent implements dynamics by allowing the automated system to adapt to different discrepancy types and scenarios through configurable rules and thresholds. The system can handle various complex situations (such as asset additions, deletions, modifications, and mismatches) by evaluating specific conditions and applying appropriate reconciliation actions, making the automated process versatile enough to handle diverse complex scenarios while maintaining high processing speed.
Data Source
AI summary
Discrepancies in two sets of asset data for an organization are identified and automatically reconciled. One set of asset data may be compiled using automatic physical discovery software while the other set is from a financial system of the organization. Automatic reconciliation is performed according to user-configurable rules and corresponding user-configurable actions.