Immutable Cash Reconciliation System for Exception Handling
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Solution Overview
Problem
Manual reconciliation of cash-based transactions with missing, inaccurate, or incomplete information is time-consuming and inefficient, requiring significant human intervention to identify and resolve discrepancies.
Innovation Solution
An automated cash transaction reconciliation system that generates an immutable record of transactions, using processors to receive and analyze data from cash handling devices, identify data sources, update records, and create graphical user interfaces for data display and user input, thereby automating the reconciliation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual reconciliation processes are used for cash-based transactions, then human investigators can identify and resolve discrepancies, but the process requires significant time investment and is inefficient
Solution Approach 1:
The patent replaces the manual mechanical investigation process with an automated computer-based system that uses algorithms to identify, investigate, and reconcile cash transaction exceptions. The system automatically retrieves transaction data, identifies discrepancies, performs investigations, and generates reconciliation reports, eliminating the need for manual human investigation while maintaining or improving accuracy.
Solution Approach 2:
The system enables self-service automation where the reconciliation process performs its own investigation and resolution activities without human intervention. The automated system retrieves data from multiple sources, identifies exceptions, investigates root causes, and completes reconciliations independently, freeing human operators from time-consuming manual tasks.
2Productivity
If automated reconciliation systems are implemented, then processing speed and efficiency improve, but system complexity increases
Solution Approach 1:
The patent divides the complex reconciliation process into distinct modular components: data retrieval modules that collect transaction data from various sources, exception identification modules that detect discrepancies, investigation modules that analyze root causes, and reporting modules that generate reconciliation results. This segmentation allows each component to be developed, tested, and maintained independently, managing overall system complexity while enabling high-throughput automated processing.
3Measurement precision
If comprehensive data collection is performed for each transaction, then reconciliation accuracy improves, but data processing complexity increases
Solution Approach 1:
The system performs preliminary data collection and validation before the actual reconciliation process begins. Transaction data is retrieved and pre-processed in advance, with initial checks for completeness and consistency performed before exceptions are identified. This preliminary action ensures high-quality input data for reconciliation while organizing complex data processing into manageable stages.
Data Source
AI summary
To maintain an updated and immutable record of reconciliation data provided to reconcile an exception relating to one or more cash-based transactions. Exceptions are automatically identified based at least in part on data received from one or more data sources, and corresponding exception case data objects are generated as immutable exception case data objects. Update data is provided for the immutable exception case data objects, causing updates to a corresponding graphical user interface while maintaining an immutable historical record of updates provided for the exception case data objects.