What is invoice processing automation?
Invoice processing automation is a part of accounts payable workflow automation process. In this automation process, developers automate every step in the workflow starting from sourcing invoices from emails and external folders to capturing data from invoices to validating invoices to integrating extracted data with the existing accounting software. Using document AI and RPA, you can automate almost every step in the invoice processing workflow.
What technologies are used to automate invoice processing workflow?
Developers uses majorly 2 technologies to automate invoice processing workflow:
Artificial Intelligence (AI):
AI is used to extract relevant data from invoices, which commonly arrive in PDF or scanned document formats. Invoices exhibit a diverse range of formats and patterns, making it challenging for traditional OCR (Optical Character Recognition) systems to handle. Document AI, also referred to as Intelligent Document Processing (IDP), is employed in conjunction with OCR to accurately extract invoice data from PDFs and scanned invoices.
Robotic process automation(RPA):
RPA bots are used to automate different steps in invoice processing workflow. RPA, a technology that uses software robots or “bots” to mimic human actions and automate repetitive tasks. Mundane and time-consuming activities such as data entry, copy-paste, form fillup, following business rules, data transfer, report generation and many more can now be delegated to RPA bots. The best part is that RPA can work on any online and desktop software, the way a human works.
So if you are using QBD (quick book desktop) or any other desktop accounting software, RPA bots can handle most of the tasks on these software.
What is a pre-trained invoice processing AI model?
A pre-trained invoice processing AI model refers to an artificial intelligence model that has been trained on a large dataset of invoices to learn patterns, structures, and relevant information extraction techniques. This model is typically trained using machine learning algorithms, deep learning architectures, or a combination of both.
However, for some organizations, a pre-trained AI model does not provide all the necessary data that are needed for invoice processing. In that case, the model needs to be fine-tuned and customized with the specific dataset to get the desired output.
How to integrate a pre-trained AI model to existing accounting systems?
The pre-trained AI model can either be integrated with API or RPA. Most of the available pre-trained models provide necessary APIs and webhooks for existing software integration. However, if your accounting system does not support APIs, you need to use RPA bots for the integration. For all the desktop based accounting systems does not provide API support and RPA is the only option here for the seamless integration.
What are steps that can be automated in invoice processing?
Almost every step in invoice processing can be automated to streamline the workflow and improve efficiency. Here are some common steps that can be automated:
Sourcing invoices: Most cases Invoices come in different emails and sometimes physical copies of invoices come in mail. Automated systems, powered by RPA and AI can retrieve invoices from various sources, such as email attachments or designated folders. This eliminates the need for manual downloading or file transfer.
Invoice data extraction: AI-based technologies, like Optical Character Recognition (OCR) and Intelligent Document Processing (IDP), can automatically extract relevant data from invoices, including invoice numbers, dates, line items, amounts, and vendor information. These technologies can handle diverse formats, including scanned documents and PDFs.
Data validation: Automated validation rules can be applied to ensure the accuracy and integrity of the extracted data. These rules can flag potential errors, such as duplicate invoices, missing information, or discrepancies between invoice details and purchase orders.
Purchase order matching: AI models can compare invoice data with corresponding purchase order information to ensure alignment. Automated matching algorithms can identify discrepancies and exceptions, reducing the need for manual review.
Approval workflow: Automated systems can route invoices through predefined approval workflows based on predetermined rules or roles. Approvers can be notified electronically, and their actions can be tracked, eliminating the need for physical routing or manual follow-up.
Exception handling: Automated systems can flag and handle exceptions or discrepancies in the invoice processing workflow. For example, if an invoice fails validation or requires manual review, it can be flagged for further investigation and resolution.
Integration with accounting systems: Extracted and validated invoice data can be automatically integrated into the organization’s accounting or financial management software either using RPA or API and webhooks. This reduces manual data entry and ensures accurate and timely updates to financial records.
Reporting and analytics: Automated systems can generate reports and provide analytics on invoice processing metrics, such as processing time, cycle times, exceptions, and bottlenecks. These insights can help optimize the invoice processing workflow and identify areas for improvement.
What is the invoice processing AI platform?
Invoice processing AI platform refers to an online software solution that leverages AI technologies to automate the end-to-end invoice processing workflow. It encompasses various functionalities and features designed to streamline and optimize the invoice processing tasks within the accounts payable (AP) process.
An invoices processing platform generally comes with the following features:
- Invoice sourcing API or other integration technologies.
- Pre-trained AI models for invoice, receipt and other documents.
- Extracted invoice data visualization with data validation.
- Webhooks and API for accounting software integration.
Key Benefits of Invoice Processing Automation
Invoice processing automation brings multiple benefits to an organization. If you are processing more than 100 invoices a month, the ROI can be realized within a month. Invoice processing and AP automation is mandatory for organizations who process thousands of invoices every month manually.
Increased Efficiency: Automation eliminates time-consuming manual tasks, allowing invoices to be processed swiftly and accurately. It reduces processing times, improves cycle times, and frees up valuable resources for more strategic activities.
Enhanced Accuracy: By utilizing AI and OCR technologies, invoice processing automation ensures accurate data extraction, validation, and reconciliation. The risk of human errors and data inconsistencies is significantly reduced, leading to more reliable financial records.
Cost Savings: Automation reduces the need for manual labor, minimizing staffing requirements and associated costs. It also minimizes the risk of late payment fees, duplicate payments, and other financial penalties, leading to cost savings for the organization.
Improved Visibility and Control: Automation provides real-time visibility into the invoice processing workflow. Stakeholders can track the status of invoices, identify bottlenecks, and take proactive measures to resolve issues. This transparency enhances control and compliance within the process.
Invoice processing automation revolutionizes the way organizations handle invoices, significantly improving efficiency, accuracy, and control. By leveraging AI, machine learning, and automation technologies, businesses can streamline their invoice processing workflows, reduce errors, and gain valuable insights into financial operations. Implementing an invoice processing automation solution empowers organizations to allocate resources strategically, improve vendor relationships, and achieve significant cost savings. Embracing automation in invoice processing is a key step towards a more efficient, accurate, and digitally transformed financial operation.
A Guide to Selecting the Right platform for document data extraction
With the growing need to digitize and extract information from vast amounts of documents, choosing the right Document Extraction OCR (Optical Character Recognition) platform is paramount. This comprehensive guide aims to provide valuable insights into the key factors to consider when selecting an OCR platform, empowering businesses to make informed decisions and streamline their document processing workflows.
- Identify Your Requirements: Start by identifying your specific document extraction needs. Consider the types of documents you will be working with (invoices, receipts, contracts, etc.), the volume of documents to be processed, and the specific data fields you need to extract. Understanding your requirements will help you evaluate whether a particular OCR platform can meet your needs effectively.
- Accuracy and Performance: Accuracy and performance are crucial aspects of an OCR platform. Evaluate the accuracy rates and error handling capabilities of different platforms. Look for technologies that offer high recognition accuracy, even for challenging documents with complex layouts, handwritten text, or poor image quality. Consider the platform’s ability to handle different languages and character sets, as well as its processing speed and scalability to handle large volumes of documents.
- Integration Capabilities: Assess the OCR platform’s integration capabilities with your existing systems and workflows. Determine whether it can seamlessly integrate with your document management system, content management system, or other relevant software. Compatibility with commonly used file formats, APIs, and the ability to automate the extraction process through APIs or SDKs are important considerations for efficient integration.
- Data Security and Privacy: When working with sensitive documents, prioritize data security and privacy. Ensure that the OCR platform adheres to industry-standard security protocols and offers features such as encryption, access controls, and compliance with data protection regulations (such as GDPR). Evaluate the platform’s reputation, certifications, and security practices to ensure the confidentiality and integrity of your documents and extracted data.
- Customization and Flexibility: Consider whether the OCR platform offers customization options to adapt to your specific requirements. Look for platforms that allow you to define and modify extraction rules, customize validation and verification processes, and tailor the output format to match your needs. Flexibility in adjusting the OCR engine to different document layouts and the ability to train the system for improved accuracy can significantly enhance the extraction capabilities.
- User Interface and Ease of Use: Evaluate the user interface and ease of use of the OCR platform. A user-friendly interface with intuitive controls and clear documentation can expedite the learning curve and streamline adoption. Consider whether the platform offers features such as batch processing, automated workflows, and error handling mechanisms to simplify the document extraction process.
- Support and Training: Assess the level of support and training provided by the OCR platform provider. Look for platforms that offer comprehensive documentation, user guides, and access to a knowledgeable support team. Consider whether they provide training resources, tutorials, or webinars to assist in maximizing the platform’s capabilities and addressing any technical challenges.
- Pricing and Scalability: Lastly, evaluate the pricing structure of the OCR platform. Consider whether it aligns with your budget and provides a cost-effective solution. Additionally, assess the scalability of the platform to handle your future document processing needs without compromising performance or incurring significant additional costs.
Few document AI technology provider that you can consider
- AWS: They have a powerful OCR technology that can extract data from documents. However they do not provide any interface for end-users. If you have a team of inhouse developers you can use their API within your application.
- Azure: Accuracy of Azure document AI is not as good as AWS; they have APIs for document processing. Sometimes it is difficult to get hold of their support. You need to have inhouse developers for integration.
- Intelgic: They have pre-trained AI models for invoices and receipts and they also provide a user-friendly interface for document processing. They also have an RPA platform for repetitive task automation. If you are looking for end-to-end automation, they can be the right choice for you.
By carefully evaluating these factors, businesses can choose an OCR platform that meets their specific document extraction requirements, drives operational efficiency, and accelerates their digital transformation journey.
