AI Model for Intelligent Line Item Matching and Anomaly Detection
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Solution Overview
Problem
The process of finalizing logged entries is inefficient due to mismatched line items between originator-specific formats and company-created entry request records, requiring manual search and matching, which is burdensome and prone to errors.
Innovation Solution
An AI model is trained on pairs of previously matched logged entry and entry request record line items to predict corresponding line items, reducing the need for manual matching and improving accuracy through intelligent prediction and autocomplete features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual search and matching of line items is performed, then accuracy of matching can be maintained, but time and effort required for finalizing logged entries increases significantly
Solution Approach 1:
The system performs preliminary matching of logged entry line items to entry request record line items using an AI model before the finalization process. The AI model predicts corresponding line numbers and populates data fields in advance, so that users only need to review and confirm the pre-matched results rather than performing manual search and matching from scratch.
2Productivity
If AI model prediction is used for line item matching, then processing speed increases, but risk of incorrect matching may increase
Solution Approach 1:
The system incorporates a feedback mechanism where users can review the AI model's predicted line item matches and provide corrections if needed. User corrections are fed back to refine and improve the AI model's future predictions, creating a continuous learning loop that enhances both speed and reliability over time.
Solution Approach 2:
The AI model performs preliminary matching to populate data fields with predicted line numbers, but the system allows users to review and adjust these predictions before finalization. This preliminary action speeds up processing while maintaining reliability through user verification.
3Adaptability or versatility
If originator-specific formats are used for logged entries, then flexibility in receiving data is maintained, but compatibility with company entry request records decreases
Solution Approach 1:
The AI model acts as an intermediary that translates between originator-specific logged entry formats and company entry request record formats. It automatically maps line items from various originator formats to the corresponding company format using predicted line numbers, enabling compatibility without requiring changes to originator systems.
Solution Approach 2:
The system is designed to handle multiple originator-specific formats universally through the AI model, which can process and adapt various formats from different originators to the company's standard entry request record format, making the system versatile and compatible with multiple data sources.
4Measurement precision
If users manually search for corresponding entry request record line numbers, then accuracy can be ensured, but operational complexity and burden increase
Solution Approach 1:
The system performs the tedious manual search operation preliminarily using the AI model, which automatically predicts and populates the corresponding entry request record line numbers. Users are relieved of the operational burden of manual searching and only need to review the pre-filled data for accuracy before finalization.
Solution Approach 2:
The system provides self-service by automatically performing the line number matching function that would otherwise require manual user effort. The AI model independently searches, compares, and populates the corresponding line numbers without requiring users to manually search through entry request records.
Data Source
AI summary
The present disclosure relates to systems and methods that use an artificial intelligence (AI) model to generate outputs that can be evaluated to predict which logged entry items match entry request record line items of an entry request record. Additionally, the present disclosure relates to systems and methods for intelligently detecting anomalies within data sets.