ML Transaction Matching With Feedback-Based Reconciliation
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Solution Overview
Problem
Conventional accounting software struggles to efficiently align transactions across different systems, often leading to misalignments and significant financial losses due to the overwhelming effort required to verify transaction matches, resulting in organizations writing off millions of dollars in unaccounted revenue.
Innovation Solution
A computer-implemented method using machine learning algorithms to determine matches between transaction records by generating vector embeddings and applying different weights for component-level differences, with graphical marking of confidence levels and user feedback for model updating, enabling efficient reconciliation of transactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional accounting software is used to verify transaction alignment, then transaction matching can be performed, but the effort and time required becomes overwhelming, leading to missed misalignments and financial losses
Solution Approach 1:
The patent replaces manual mechanical review processes with machine learning algorithms that automatically analyze transaction data, generate match predictions, and provide confidence scores. This substitution enables high-accuracy transaction matching without the time-consuming manual effort previously required
Solution Approach 2:
The system performs self-service by automatically matching transactions across systems using AI algorithms. The machine learning model independently analyzes data patterns, identifies potential matches, and provides recommendations without requiring continuous human intervention, thereby reducing verification time while maintaining accuracy
2Reliability
If manual searching for misaligned transactions is performed, then potential matches can be identified, but the overwhelming effort required results in many misalignments being missed
Solution Approach 1:
Manual searching and verification efforts are replaced with automated machine learning algorithms that systematically analyze transaction data. The AI model reliably identifies matches and misalignments without the human effort and subjectivity that cause missed detections in manual processes
Solution Approach 2:
The system incorporates feedback mechanisms where users can review and correct match predictions, and this feedback is used to continuously improve the machine learning model. This feedback loop enhances reliability over time while the system requires minimal operational effort from users
3Productivity
If conventional methods are used to track transaction alignment, then basic matching can be achieved, but the process is inefficient and leads to writing off millions of dollars in unaccounted revenue
Solution Approach 1:
Inefficient conventional tracking methods are replaced with AI-powered automated reconciliation systems that process transactions across multiple systems simultaneously. This substitution dramatically improves productivity by identifying matches that would otherwise be lost, preventing financial write-offs
Solution Approach 2:
The system performs preliminary matching and identification of potential misalignments before final reconciliation is completed. By proactively detecting issues early in the process, the system prevents financial losses from unaccounted revenue while improving overall reconciliation efficiency
Data Source
AI summary
Systems, methods, and computer-readable media are provided for determining matches between records of different systems based on aggregate record data, and graphically marking potentially matched groups of data along with predicted confidence levels. Preliminary matching tools may allow allow users to define various rules based on which a majority of the transactions can be matched and reconciled. However, remaining transactions are disposed of in an interactive matching process. The matches may be determined unidirectionally from a source transaction to transactions from a target ledger, or bidirectionally from transactions in the target ledger to transactions other than the source transaction. Transactions may be matched many-to-many, one-to-many, or many-to-one, and a proposed order of match selections may be presented in a user interface. Match metadata or insights may be displayed to show a confidence of the match, reasons for the confidence, and/or a confidence of other matches that may be more beneficial than a match with a source transaction. The confidence and match insights may be generated by a machine learning model with access to transactions from a source transaction ledger and a target transaction ledger. The machine learning model may be trained on manual activity for prior matches that have been made. Matches may be performed using a hybrid machine learning model that accounts for random forests, decision trees, neural networks, naïve bayes algorithm, and/or a generalized linear model. Machine learning models also incorporate ongoing feedback from the users who can either accept or reject suggested matches and hence the models undergo an evolution process and constantly update from user patterns.