Financial Record Reconciliation Using Transaction Attribute Prediction
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Solution Overview
Problem
Reconciliation of financial records is challenging due to insufficiently detailed entries in financial systems, making it difficult for both humans and automated systems to identify relevant transaction information, particularly in the absence of standardized descriptions for transactions.
Innovation Solution
A method involving a numerical representation generation model and a transaction attribute prediction model, trained on historical records, to determine transaction attributes such as account codes and entity identifiers, facilitating automated reconciliation by generating and approving suggested attributes for financial records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated reconciliation systems are used, then productivity is improved, but measurement precision deteriorates due to insufficiently detailed entries in financial records
Solution Approach 1:
The patent introduces an intermediary natural language processing system that acts as a mediator between the insufficient financial record entries and the reconciliation requirements. The NLP model generates intermediate representations (entity mentions, coreference chains, semantic roles) that bridge the gap between vague transaction descriptions and precise attribute identification, enabling automated reconciliation without sacrificing accuracy.
Solution Approach 2:
The patent replaces traditional mechanical rule-based reconciliation systems with an intelligent NLP-based system. Instead of relying on rigid predefined rules that fail with varied financial record formats, the system uses machine learning models to automatically extract and interpret transaction attributes from unstructured text, adapting to different entry styles while maintaining precision.
2Measurement precision
If manual reconciliation is performed, then measurement precision is improved through human experience, but loss of time increases significantly
Solution Approach 1:
The patent implements a self-service automated reconciliation system that performs attribute identification without continuous human intervention. The NLP models automatically process financial records, extract transaction attributes, and generate reconciliation results, freeing accountants from manual analysis while maintaining high accuracy through trained algorithms that capture expert knowledge.
Solution Approach 2:
The patent applies preliminary action by pre-training NLP models on historical financial records and reconciliation patterns before actual reconciliation tasks. The system performs preliminary extraction of entity mentions, coreference resolution, and semantic role assignment in advance, so that when reconciliation is needed, the heavy analytical work has already been completed, dramatically reducing processing time.
3Measurement precision
If entity-specific models are created, then measurement precision is improved for specific entities, but device complexity increases
Solution Approach 1:
The patent creates a universal NLP-based reconciliation system that can handle multiple entities and transaction types through a single framework. The models are designed to be entity-agnostic, processing any financial record through the same pipeline of entity mention extraction, coreference resolution, and attribute identification, eliminating the need for separate models for each entity while maintaining adaptability through training on diverse data.
Data Source
AI summary
Described embodiments relate to determining a candidate financial record associated with a transaction between a first accounting entity and a second entity, and determining, using a numerical representation generation model, a numerical representation of the candidate financial record, the numerical representation generation model having been trained on a corpus generated from historical transaction records. The method further comprises providing, to a transaction attribute prediction model, the numerical representation of the candidate financial record, the transaction attribute prediction model having been trained using a dataset of previously reconciled financial records, each associated with a respective first transaction attribute; and determining, by the transaction attribute prediction model, at least one first transaction attribute associated with the candidate financial record.