1. Streamline On-boarding

Objectives:

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Unified Data Ingestion Framework:

Flexible data ingestion system that can handle various payment types including checks, lockboxes, ACH, wires, and EDI transmissions.

Advanced OCR for Check Processing:

Optical Character Recognition (OCR) technology to digitize and extract information from check images.

API Integration with Payment Systems:

API integration for electronic payment systems to automate the extraction of payment data.

Lockbox Data Processing:

Automated processing of lockbox data, including the extraction and interpretation of remittance information.

Standardized Data Format Conversion:

All incoming payment data standardized into a consistent format for uniform processing.

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Automated Data Mapping Tools:

AI-powered algorithms to automatically suggest mappings between incoming data fields and the standard target data model.

Machine Learning for Pattern Recognition:

ML algorithms to recognize common data patterns and suggest optimal data mappings.

User-friendly Mapping Interface:

User-friendly interface for reviewing and adjusting AI-recommended data mappings.

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Historical Data Analysis for Pattern Detection:

Historical payment and invoice data analysis to detect common matching patterns and behaviors.

Automated Rule Generation:

ML algorithms to automatically generate custom matching rules based on detected patterns.

Self-Learning Matching Algorithms:

Self-learning algorithms that continuously refine and update matching rules based on new transactions.

Feedback Loop for Continuous Improvement:

Feedback mechanism to allow users to refine and correct the system, thereby improving the accuracy of rule generation over time.

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