2. Zero-touch Invoice & Payment Matching

Objective:

Reduce the # of payments that have to be manually reviewed & applied, with an aspiration to be “zero touch” using sophisticated ML techniques

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Accurate Customer Identification

Comprehensive Customer Data Repository:

Centralized database containing all relevant customer information, including payment history, contact details, and unique identifiers (like customer IDs).

Advanced Matching Algorithms:

ML algorithms that can match incoming payments to the correct customer accounts, including factors such as historical payment patterns, payment references, and partial identifiers (like partial names or addresses).

Reference and Memo Field Analysis:

NLP to analyze memo and reference fields in transactions, which might contain customer identifiers or invoice numbers.

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Invoice Matching and Reconciliation

Partial and Split Payments

Algorithms that can intelligently allocate partial and split payments to the correct invoices based on payment history, amounts, and customer-specific patterns.

Dynamic Discounting and Variable Payment Terms

AI algorithms capable of understanding and applying different payment terms and discount conditions automatically during the matching process.

Fuzzy Matching Techniques:

Fuzzy matching techniques to correctly identify and match payments even with incomplete or slightly inaccurate payment details.

Multi-factor Authentication Process:

Multi-factor authentication process that verifies the payment details against multiple data points (e.g., exact amount, payment date, customer ID, invoice number) to ensure accuracy.

Anomaly Detection and Fraud Prevention

Anomaly detection algorithms to flag unusual transactions for review, including unusually large payments, rapid changes in payment patterns, or irregularities in invoice details.

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