What factors should you consider when automating invoice processing?

Invoice processing is the most frequently automated process in the industry. At a very high level, it involves the following. - Extracting data from invoices - Make an entry in the ERP - Do 2/3/4 way matching to approve it. - Use workflow if any human approval is needed. - Queue it for payment. - Process the payment It's recommended to use IDP for invoice extraction to simplify the complexity of different invoice formats involved in it. Then, you can use surface automation or API integration to complete the remaining steps. It is as simple as it sounds.

If all the above steps have worked , by this time invoice processing will be a 0 Touch posting to ERP. The fact is : OCRs cannot identify beyond header fields and sometimes single line items. It fails in multiple lines, multiple wrapped lines and multiplage There is no RPA that does not use OCR to do the same job in the name of automation PDF parsing is more coding and may address multiple supplier format eco system Rate of recognition for ocr is less than 40 to 60%. The chance to get an invoice in and invoice out with 0 errors is less than 20% chance for a 0 touch Azure, Google cloud data capture face same ocr issue. The best cloud is Google if your system allows you to access 2,3,4 way match, grn, all at line items. Imagine ??

Detailed Analysis: Conduct a thorough examination of the various invoice formats, including PDFs, electronic formats, and traditional paper invoices. Tailored Automation: Develop automation strategies that cater to the unique characteristics of each invoice type, ensuring adaptability and accuracy in data extraction.

When automating invoice processing, consider the volume of invoices, accuracy requirements, integration with existing systems, regulatory compliance, and scalability. Evaluate the capabilities of the automation solution, such as optical character recognition (OCR), machine learning, and workflow automation. Ensure data security and implement measures for exception handling. Additionally, assess the cost-effectiveness and the potential for return on investment in streamlining the invoicing workflow.

- PDFs, scans, emails the invoice format is your first clue. Different tools excel at specific formats, so choose wisely! - Structured invoices, with their predictable layout, are data extraction gold. Unstructured ones, however, require AI and machine learning to decipher and extract data accurately. - Multiple line items? No worries! Choose a tool that tackles complexity, extracting item-wise details with laser focus. - Multilingual invoices? Opt for a tool that recognizes and processes them flawlessly, eliminating translation headaches. - Different suppliers, different formats? the automation tool needs to be flexible and adaptable to handle these variations.

Workload Forecasting: Analyze historical data and forecast future volumes and frequencies of invoices. Scalability: Implement automation solutions designed to scale efficiently, accommodating fluctuations in workload to maintain optimal processing speed and efficiency.

Scalability: The automation solution must be capable of handling the current volume of invoices without compromising on speed or accuracy. Processing Speed: If invoices are received frequently throughout the day, the system must be capable of processing these invoices promptly to avoid any backlog. Reliability: With high volume and frequency, the system's reliability becomes crucial. It should be robust enough to handle peak loads without crashing or slowing down. Error Handling: In high-volume situations, even a small percentage of errors can result in a significant number of incorrect invoices. The automation Solution should have strong error-detection and correction capabilities.

Nuanced Understanding: Explore the intricacies of invoice complexity, considering factors such as line item details, diverse layouts, and potential challenges in data extraction. Intelligent Tools: Select automation tools equipped with intelligent algorithms and machine learning capabilities to handle the variability inherent in invoices

System Examination: Investigate the existing systems involved in the end-to-end invoice processing workflow, including ERP and CRM systems. Seamless Integration: Prioritize the development of automation solutions that seamlessly integrate and communicate with various systems, promoting a cohesive and interconnected workflow.

Regulatory Compliance: Delve into specific regulatory requirements governing invoice processing in relevant industries or regions. Data Security Measures: Implement robust security measures to safeguard sensitive financial data, ensuring compliance with data protection and privacy regulations.

As Alok mentioned, automated invoice processing is a problem that already has powerful solutions. PDF parsing - solved OCR recognition for PDFs/A4 - solved Top solutions are built on industry-specific LLMs and provided by ML/IDP boutiques (so not Google, AWS or Microsoft off-the-shelf); they can be as high as 97% error-free for insurance. From a technology standpoint, the amount of edge cases requiring human intervention is reducing fast. Current complexity is in the context and compliance, e.g. is the invoice governed by a specific umbrella service agreement? Is it using a 3rd party that hasn't signed full NDAs? Were the freight costs adequate? Were the goods covered by a valid insurance policy? Enterprise solutions are coming soon.

In summary, a nuanced approach to automating invoice processing involves understanding the diverse nature of invoices, forecasting and managing workload fluctuations, addressing the complexities through intelligent tools, ensuring seamless integration with existing systems, and prioritizing compliance and data security measures. Insights from AI and the collaborative expertise of the LinkedIn community enrich this approach, contributing to a more informed and effective automation strategy.