What decision-making rules should you follow for RPA-based invoice processing?

For RPA-based invoice processing, adhere to these rules: 1. Standardize templates for consistent data extraction. 2. Implement validation checks to ensure accuracy. 3. Prioritize exception handling for irregular cases. 4. Regularly update and refine automation rules. 5. Ensure compliance with data privacy regulations. 6. Conduct periodic audits to maintain efficiency. 7. Foster collaboration between RPA and human oversight for continuous improvement.

Si bien la tecnología OCR puede mostrarse confiable para un bot robusto que procese de manera masiva las plantillas de factura, es importante comprender que casos borde podríamos tener, teniendo en cuenta que se estandarice la información que se procesa, en donde la intervención humana es esencial para un funcionamiento correcto. A su vez, la intervencion de las areas correspondientes a donde se designen dichas facturas para proporcionar validaciones adicionales siempre se considera una buena practica para el proceso.

In addition to all the items above, it is important for you to consider the cost centers to which each invoice will be allocated and who the managers of these are. More important than automation at this time, is approval and correct allocation in the company's books. This way you can maximize the power of RPA.

Categorizing the invoice document types will help us gain a clear picture of what we'll be processing. This approach will also assist in preparing a well-balanced dataset for model training.

- Begin by identifying the types of invoices your company receives. This could range from purchase orders, credit notes to utility bills. - Each invoice type has unique data fields and layout, understanding these differences is essential to design an RPA solution that can handle all types. - Create a catalog of all the invoice types and their unique data fields for the RPA to recognize and process.

Identifying the data Input Data and Output data is a critical need in invoice processing. Steps below can be followed :- 1. Identify the Necessary fields from the invoice which needs to be extracted. ?? 2. Identify the ERP systems where the data needs to be fed which is extracted from the Invoice fields. ???? 3. The Documentation needs to be signed-off by the process SME's so that accurate navigations and implementation can be done for Bot ??

The schema is essential for both training the model appropriately and configuring it within the automation framework to ensure accurate extraction and processing. Each field within the schema will have a corresponding confidence value, guiding our decision-making regarding automated processing or the need for human intervention for bot training.

- Clearly define what data the RPA will extract from each invoice type, this will be your input data. It can include information like vendor details, invoice number, date, and total amount. - Determine the output data, the format, and where it will be stored. Typically, the output data is structured and ready for further processing or reporting.

OCR models would be the first technical step in the document processing framework, where the document is converted to a machine-readable format using an AI engine that recognizes text from an image, again with a confidence level. Every OCR engine has its own performance characteristics and features. We need to test them with the specific documents we receive to pick the right one. A hybrid model can also be implemented, and the solution should be flexible enough to switch engines when required.

- The OCR tool should be able to accurately convert different invoice types into digital data. It should handle different document formats and languages if required. - Test different OCR tools to see which best fits your needs. Consider factors like accuracy, speed, scalability, ease of integration with RPA, and cost.

Even when document extraction yields high confidence scores, issues relating to grouping, formatting, or minor OCR errors can still arise, preventing entirely accurate results. This necessitates an additional validation module to meticulously verify the data's structure and format, ensuring its integrity and alignment with expectations. While value-based checks may not be required at this stage, the validation process should encompass appropriate handling mechanisms, either through manual intervention or the application of business rule exceptions.

- Implement checks to validate the data extracted by the OCR tool. This can include checking for missing fields, incorrect data formats, and inconsistencies in the data. - Design exception handling processes for when the RPA encounters an error. This can include alerting a human operator or rerouting the invoice for manual processing.

AI and use of IQ Bot can be another way of looking at optimizing processes with high variation and cognitive thinking. Multiclass classification or clustering using Machine Learning can enable quick categorization and training of RPAs to handle exceptions.

Data validation and exception handling are crucial for accurate and reliable RPA-based invoice processing. Data validation ensures data quality and consistency, while exception handling defines actions for errors or anomalies. These mechanisms optimize efficiency and accuracy, preventing errors and inconsistencies in the workflow. Overall, implementing these mechanisms is essential for successful RPA-based invoice processing.

- Continuously monitor and analyze the RPA performance to identify areas of improvement. - Regularly test and update the RPA algorithms to ensure they are adapting to changes in the invoice types. - Use analytics to identify bottlenecks and optimize the workflow for speed and accuracy.

Dealing with unstructured or semi-structured data is one of the main challenges of RPA-based invoice processing. OCR tools play a crucial role in converting scanned or PDF invoices into text that RPA bots can read and process. Choosing the right OCR tool is essential to ensure accuracy, speed, scalability, and integration with your RPA platform. Testing and training the OCR tool is also important to improve its performance and reliability. Overall, OCR tools are a critical component of RPA-based invoice processing, and selecting the right tool can significantly improve the efficiency and accuracy of the process.

Use of operational reports, capturing error images, error messages facilitate classifications and to optimize the RPA workflow using 80/20 rule with an effective exception management process. Dashboards are important to identify special causes and address them to increae performance of the RPA.

To ensure successful RPA-based invoice processing, it's important to comply with industry, regional, and organizational regulations and standards. ?? Compliance involves meeting legal, ethical, and professional requirements, such as GDPR, PCI-DSS, or ISO standards for data protection, security, and quality. ??? To comply, follow best practices and guidelines for RPA governance, documentation, and auditing. ?? This ensures that your RPA solution meets all necessary requirements and operates within legal and ethical boundaries. ??

- Ensure the RPA solution complies with all relevant regulations and standards. This can include data protection laws, financial reporting standards, and industry-specific regulations. - Regularly review and update the RPA solution to stay in compliance with any changes in the regulations and standards.

A dedicated 'Centre of Excellence' is important for regular maintenance of RPA and to proactively train BOTs to minimize exceptions. Also other co-creation methods with external partners is recommended for organizations do not have the required skills or is in early stages of technology adoption.