AI Wire Transfer Matching for Receivables Reconciliation
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Solution Overview
Problem
Conventional methods for reconciling incoming wire transfers with receivables require human intervention and are prone to errors due to system inconsistencies and complexity, leading to delays and inaccuracies.
Innovation Solution
An AI/ML model employing NLP techniques and TF/IDF similarity scoring is used to automate the matching process, determining probabilities of matches between wire transfers and receivables, and continuously updating through feedback for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods are used to reconcile wire transfers with receivables, then human intervention and system communications are required, but this leads to errors, delays, and increased system complexity
Solution Approach 1:
The patent introduces an AI/ML model as an intermediary component that sits between the wire transfer processing system and the receivables management system. This model automatically analyzes transaction data, identifies matches, and reconciles discrepancies without requiring direct human intervention or complex communications between heterogeneous systems, thereby reducing both error rates and system complexity
Solution Approach 2:
The system enables self-service by implementing automated matching algorithms that independently process wire transfer data against receivables data. The AI/ML model performs self-learning and self-adjustment through feedback mechanisms, continuously improving matching accuracy without requiring manual system configuration or human operational intervention
2Productivity
If conventional reconciliation methods are used, then human intervention is required, but this causes delays and system latency
Solution Approach 1:
The patent replaces the mechanical system of manual human review and intervention with an automated AI/ML-based electronic processing system. The model rapidly analyzes transaction data, performs pattern recognition, and generates matching results in seconds, eliminating the time-consuming human review process and significantly reducing system latency while improving overall reconciliation productivity
3Adaptability or versatility
If heterogeneous systems are used for wire transfer and receivables data, then more data sources are available, but this increases system complexity and causes information inconsistency
Solution Approach 1:
The patent implements a universal AI/ML model that can process and reconcile data from multiple heterogeneous systems and data sources. The model is designed to handle various data formats, structures, and sources uniformly, adapting to different input types while maintaining consistent processing logic and output standards, thereby ensuring information consistency across diverse data sources without requiring separate processing systems for each source
Data Source
AI summary
Various methods and processes, apparatuses or systems, and media for using an AI/ML model to perform automated matching of incoming wire transfers with receivables in an accurate and efficient manner are disclosed. The method includes: obtaining first information that is associated with a set of wire transfers; obtaining second information that is associated with a set of receivables; using the AI/ML model to compare the first information with the second information; determining, based on a result of the comparison, a respective probability that each of the wire transfers matches with each of the receivables; and using a result thereof to generate an assessment of respective matched pairings of wire transfers and receivables.