Dr. Alok Aggarwal
Traditional invoice processing is a paper-laden burden, plagued with manual data entry, riddled with errors, and slow approvals. But there’s a paradigm shift with AI-powered invoice processing that leverages the power of Artificial Intelligence (AI) and Machine Learning (ML) to automate the entire workflow. This translates into significant benefits for businesses, including:
Collatio® provides automated invoice digitization with data reconciliation features across documents such as SoWs, purchase orders, and MSAs with an efficiency of 98%. It is capable of processing 200+ invoices/hour. Collatio® delivers end-to-end invoice document processing and categorization for pre-forma, interim, past due, final invoices, purchase orders, etc. This AI-based processing of invoices helps prevent fraud and provides actionable insights using proprietary algorithms for inter and intra-document reconciliation. Collatio® can not only extract key-value pairs from the documents but also perform reconciliation with other documents to identify if there is a duplicate invoice or any pricing inconsistency between the invoice and the purchase order or other such documents. It can further generate alerts for duplicate invoices, payment due dates, early payment discounts, etc. based on the extracted key-value pairs and reconciliation. The alerts and reconciliation algorithms can be customized to meet business requirements. Collatio® is equipped with ready-to-use API that allows seamless integration with Enterprise Resource Planning (ERP) systems.
Reading or capturing invoice data and classifying documents: Collatio®’s proprietary AI-based model can handle invoices from various sources, be it scanned images, PDFs, or emails. They use advanced algorithms like Deep Learning Networks for images and specialized algorithms for PDFs to extract data and classify each document page as per its type (invoice, purchase order, etc.).
Recreating tables automatically and reconciling them: Collatio® doesn’t just extract data, it understands it! Collatio®’s proprietary AI models can recreate tables within invoices with exceptional accuracy, and even learn formulas used within those tables (e.g., quantity x price). This allows for automated reconciliation, minimizing errors and discrepancies. Collatio® can recreate such tables with more than 98% accuracy
Extracting key-value pairs and attributes and reconciling them: Collatio®’s AI-based algorithms can extract crucial data points like supplier names, product details, and invoice amounts. Advanced systems like Collatio® can also reconcile these key-value pairs from various invoice sections (e.g., billing address vs. shipping address), ensuring data accuracy.
Reconciling the extracted information from invoices with other documents: The AI and ML-based models of Collatio® don’t work in isolation. They compare extracted invoice data with past invoices from the same vendor. Using the formulas that these AI-based algorithms would have learned during the training phase, Collatio® would also reconcile these key-value pairs with those provided in the purchase order, statement of work, and master services agreement. This ensures all terms and conditions are considered for accurate processing and payment (e.g., early payment discounts).
Straight-through processing versus manual intervention: For invoices with complete and accurate data, and those meeting pre-defined thresholds (e.g., payment amount), Collatio® can automatically process the document for immediate payment within a matter of few seconds with straight-through processing (STP) accuracy of more than 95%. Any discrepancies or exceptions are flagged for manual review by the accounts payable team (e.g., missing information, or exceeding approval limits).
Documents Marked as Valid are Processes Without Any Manual Intervention
Integration with ERP and Intelligent Reporting: Finally, Collatio® can seamlessly integrate with existing ERP or accounting software using direct integration or APIs. This ensures smooth data flow across financial systems. Furthermore, Collatio® automates the creation of dashboards and custom reports. These visualizations provide the Accounts Payable team with immediate insights into vendor payments, enabling data-driven forecasting of spending trends and informed budgeting decisions.
Process Evaluation: Analyze your existing invoice processing workflow to identify bottlenecks and areas for improvement (e.g., slow data entry, and manual reconciliation).
Stakeholder Engagement: Involve key stakeholders from finance, procurement, accounts payable, and IT for a smooth transition and to address any concerns regarding user adoption and data security.
Workflow Design: Define workflows that seamlessly integrate with your existing systems and organizational policies (e.g., ERP system) to ensure user buy-in and efficient data flow.
AI System Selection: Carefully assess various AI vendors based on your specific needs (processing speed, accuracy, ease of use, cost-effectiveness). Consider free trials offered by many SaaS providers to test functionalities and ensure user satisfaction within your AP team. This rigorous testing phase is crucial to ensure you choose the AI system that best aligns with your unique needs and workflows.
SaaS vs. On-Premises: A key consideration is choosing the deployment model: Cloud-based (SaaS) solutions offer ease of use and lower upfront costs, but may not be suitable for all businesses with strict data security needs (e.g., financial institutions). On-premises deployments provide greater control over data but require investment in IT infrastructure and ongoing maintenance.
Appropriate training to users: Comprehensive training empowers your AP team to effectively leverage the new AI system and maximize its benefits (e.g., understanding system functionalities, and handling exceptions).
The benefits of AI-based invoice processing extend far beyond automation. Here’s how it empowers your business:
Faster Processing and Payments: AI-based systems significantly reduce processing times by transforming a three-week-long process (using legacy systems) into two hours (using straight-through processing) or at most two days (with analyst involvement).
Reduced Labor Costs: Free your AP team from tedious tasks like data entry and reconciliation. AI minimizes manual labor expenses and the need for additional staff as invoice volume grows.
Improved Accuracy and Reduced Errors: Automated data extraction and reconciliation reduces errors significantly. It eliminates the risk of incorrect payments and associated late fees or missed discounts.
Deeper Insights and Improved Decision Making: AI-based algorithms can provide valuable insights from invoice data (e.g., spending trends by vendor). You can use this data to optimize vendor relationships, identify cost-saving opportunities, and make informed business decisions regarding procurement strategies.
ROI calculation of AI-powered invoice processing is crucial for understanding the value generated through AI-powered invoice processing.
To this end, key performance indicators (KPIs) serve as critical benchmarks for measuring success. These metrics encompass:
Cost Savings: Quantify the financial benefits of AI automation, including reduced labor costs and streamlined operational expenses.
Processing Speed: Track the reduction in processing time at various levels (e.g., page, invoice, document) to demonstrate efficiency gains.
Accuracy and Error Reduction: Measure the system’s accuracy and identify error rates to showcase increased financial data integrity.
Straight-Through Processing (STP) Rate: Monitor the percentage of invoices processed autonomously by AI, highlighting the system’s automation capabilities.
Human Intervention Time: Track the average time required for human intervention in non-STP invoices to assess the efficiency of exception handling.
Approval Time Differential: Compare average approval times for STP and non-STP invoices to quantify the time saved through automation.
Cost-Effectiveness: Evaluate the annual licensing cost alongside the cost per invoice or page to assess the system’s affordability relative to its benefits.
Vendor Satisfaction: Measure vendor satisfaction with faster payments and improved communication facilitated by AI-driven processing.
Beyond these individual metrics, a comprehensive total cost of ownership (TCO) analysis should be conducted. This includes one-time and annual AI software costs, potential hardware expenses (for on-premise solutions), and employee training investments. A robust TCO analysis, coupled with the aforementioned KPIs, will provide a clear picture of the financial advantages associated with AI-powered invoice processing.
By implementing a data-driven approach to ROI assessment, businesses can confidently embrace AI automation and unlock a future of financial strength and operational excellence.
Blog Written by
CEO, Chief Data Scientist at Scry AI
Author of the book The Fourth Industrial Revolution
and 100 Years of AI (1950-2050)
