Global Consulting Firm Automates Payment Processing

The Challenge

As one of the largest professional services firms in the world, this Big Four consulting company processes millions of customer payments annually across hundreds of global offices. The payment processing workflow was a critical yet labor-intensive operation.

The existing process relied heavily on manual intervention:

• Staff manually extracted payment details from various document formats
• Data entry errors led to payment mismatches and reconciliation issues
• Approval workflows required manual routing and tracking
• Peak periods created backlogs that delayed cash application
• Limited visibility into payment status across the organization

With clients submitting payments via checks, wire transfers, ACH, and electronic payments—each with different remittance formats—the complexity was enormous. The firm needed a solution that could intelligently handle this variety while maintaining the accuracy required in financial services.

The Solution

The firm implemented an intelligent payment processing solution using Power Automate, AI Builder, and Dataverse. The system uses artificial intelligence to automatically extract payment information from documents and route them through configurable approval workflows.

Intelligent Document Processing

The AI Builder component was trained on the firm's specific document types:

• Invoice Recognition: Automatically identifies and extracts key fields from invoices
• Remittance Advice: Processes payment details from various client formats
• Check Images: Extracts payer, amount, and reference information
• Wire Confirmations: Parses bank notification formats

Automated Workflow

Once documents are processed, Power Automate handles the downstream workflow:

• Validates extracted data against customer master records
• Matches payments to outstanding invoices
• Routes exceptions to appropriate staff for review
• Triggers approval workflows for payments above thresholds
• Updates financial systems upon approval

The Results

AI Model Training

A key success factor was the approach to training the AI Builder models. The team followed a structured process:

• Sample Collection: Gathered representative samples of each document type from across the organization
• Field Identification: Defined the key fields to extract from each document type
• Model Training: Used AI Builder's document processing capability to train extraction models
• Validation: Tested models against held-out samples to verify accuracy
• Iteration: Refined models based on production exceptions and edge cases

Key Takeaways

• Start with High-Volume Documents: Focus AI training on the most common document types first for maximum impact
• Plan for Exceptions: Build robust exception handling for documents that require human review
• Integrate Deeply: Connect to financial systems for end-to-end automation
• Monitor and Refine: Use production data to continuously improve AI model accuracy
• Maintain Compliance: Ensure audit trails meet regulatory requirements for financial processes

Technology Stack

• AI Builder: Document processing models for intelligent data extraction
• Power Automate: Workflow orchestration and system integration
• Dataverse: Structured storage for payment records and audit trails
• Power Apps: Exception handling interface for staff review
• Power BI: Payment processing metrics and analytics