Financial Transaction Reconciliation Using Subset Sum Segmentation
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Solution Overview
Problem
Current financial transaction reconciliation systems face inefficiencies in reconciling large numbers of credit and debit transactions, often requiring significant human analyst time and computing resources, and may not include all transactions, especially when reconciliation solutions do not account for all payments.
Innovation Solution
A method involving a computing device that determines positive and negative subset sizes, calculates maximum and minimum subset sums, identifies overlapping and non-overlapping portions between positive and negative subset sum ranges, and uses look-ahead tables and worker threads to efficiently reconcile financial transactions by performing subset sum comparisons and optimizing reconciliation attempts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reconciliation methods are used to reconcile large numbers of credit and debit transactions, then all transactions can be reconciled exactly, but significant human analyst time and computing resources are required
Solution Approach 1:
The patent divides the set of transactions into positive subsets (credit transactions) and negative subsets (debit transactions). It then segments these further by determining positive and negative subset sizes, and calculating maximum and minimum subset sums to create bounded ranges. This segmentation allows the system to process large transaction sets in manageable chunks rather than attempting to reconcile all transactions simultaneously, significantly reducing computational time while maintaining reconciliation accuracy.
Solution Approach 2:
The patent performs preliminary calculations by determining positive and negative subset sizes, calculating maximum and minimum subset sums, and identifying overlapping portions between positive and negative subset sum ranges before performing the actual reconciliation. This preliminary action filters out non-overlapping portions that cannot possibly reconcile, allowing the system to focus computational resources only on promising subsets, thereby reducing overall computational time while ensuring exact reconciliation.
2Reliability
If traditional reconciliation methods are used to reconcile large numbers of credit and debit transactions, then exact reconciliation can be achieved, but large amounts of computer memory are required
Solution Approach 1:
The patent extracts and processes only the essential information needed for reconciliation by determining positive and negative subset sizes and calculating maximum and minimum subset sums. It extracts the overlapping portions between positive and negative subset sum ranges, discarding non-overlapping portions that cannot contribute to reconciliation. This extraction approach reduces the amount of data that needs to be held in memory while maintaining the ability to achieve exact reconciliation.
Solution Approach 2:
By segmenting transactions into positive and negative subsets with determined sizes and sum ranges, the patent reduces the memory footprint compared to holding all transactions in memory for traditional reconciliation. The segmentation allows processing of subsets independently, requiring only the memory needed for current subset calculations rather than the entire transaction set.
3Reliability
If reconciliation attempts include all transactions, then complete reconciliation can be achieved, but computational complexity increases significantly
Solution Approach 1:
The patent performs preliminary determination of positive and negative subset sizes and calculates maximum and minimum subset sums to identify overlapping portions between positive and negative subset sum ranges. This preliminary action creates a filtered view that shows only which subsets could possibly reconcile, eliminating the need to examine all possible combinations of all transactions. The complexity is reduced from exponential (all combinations) to polynomial (comparing subset ranges), while still ensuring complete reconciliation by systematically examining all valid subset combinations within the overlapping ranges.
Data Source
AI summary
Methods, computer-readable media, and apparatuses are disclosed for reconciling sets of financial transactions. A reconciliation input set of positive and negative numbers may be received, and a positive subset size and negative subset size may be determined for one or more reconciliation attempts. Maximum and minimum subset sums for the positive and negative subset sizes may be determined, and certain overlapping and non-overlapping portions may be identified between the positive subset sum range and the negative subset sum range. For any overlapping portions, a plurality of subset sum comparisons may be performed between positive subsets of the determined positive subset size and negative subsets of the determined negative subset size.