Labelling invoices: optimize your process through supervised learning
💡 Annotate your invoices to optimize your issuance and recovery processes (among others): what are the challenges?
When it comes to managing invoices in a business, the task can quickly become complex and tedious. That's where artificial intelligence and supervised learning come in. By using these advanced technologies, you can automate and optimize the invoice labeling process, allowing you to gain efficiency and accuracy. In this article, we explore how artificial intelligence and supervised learning can help you label your invoices more quickly and accurately. For even more efficient processes!
What is invoice labelling?
Invoice labeling is the process of categorizing and classification of the various pieces of information present on an invoice, such as the supplier, date, amount, etc. This step is essential to organize and process invoices effectively in the business management system. Note that this article and the principles it describes can be applied to other textual sources, such as payslips or newspaper articles.
💡 The use of artificial intelligence in the labelling of invoices offers numerous advantages. By using supervised learning algorithms, AI can analyze large amounts of data to identify specific patterns and characteristics in invoices. This makes it possible to create classification models capable of automatically labelling invoices accurately and consistently.
The stages of supervised learning
Supervised learning is a machine learning technique used to train AI models in labeling invoices. Here are the key steps in this process:
1. Data collection
Gather a set of representative invoices that will serve as training data for your AI model.
2. Data annotation
Manually label invoices in your training data set by specifying the relevant categories (supplier, date, amount, etc.).
3. Model training
Use annotated data to train the AI model to recognize the patterns and characteristics associated with each invoice category.
4. Validation and adjustment
Evaluate model performance using a validation data set and make the necessary adjustments to improve its accuracy.
5. Use in production
Once the model is trained and validated, it can be used to automatically label new invoices Incoming in your system.
Benefits of using AI for invoice labelling
Introduction of AI into the invoice labelling process offers numerous advantages:
1. Time saver
Automating invoice labeling significantly reduces the time needed to process each invoice manually.
2. Increased precision
AI models trained with supervised learning are able to label invoices with great precision and consistency.
3. Scalability
AI can handle a high volume of invoices, making it easier to manage workflows and optimize collections processes
4. Reduction of errors
By avoiding human errors when manually labeling invoices, AI helps to minimize processing errors and problems associated with misclassified invoices.
Choosing the best invoice labelling tool to train your models: what are the options for annotating your data?
When it comes to labeling invoices, it is essential to have the right tools to optimize the data processing process. Fortunately, there are now several advanced software solutions specifically designed for the annotation of images or texts, including invoices regardless of their format (PDF, Word, PNG or JPG, etc.).
Here is a list of the main labeling tools existing on the market and used by Data Scientists for Use Cases associated with the labeling of invoices in business:
Label Studio
A flexible and customizable solution for creating specific labeling tasks for invoices. This tool offers the possibility to create annotation schemes adapted to your needs and to customize categories and attributes for each invoice.
Prodigy
A versatile data labeling tool that can be used to annotate invoices. It offers advanced features such as team collaboration, integration with machine learning models, and interface customization.
Doccano
A platform open-source data labeling that can be used to annotate invoices. It offers a user-friendly interface and supports text labelling, offering flexibility to adapt categories and attributes to your needs.
Tagtog
An online data labeling tool that can be used to annotate invoices. It allows the creation of custom annotation schemes and offers team collaboration features to facilitate the labeling process.
Snorkel
A machine learning platform that can be used for semi-automatic labeling of invoices. It allows you to create labeling models based on rules to generate initial annotations, then refine them with the help of human annotators (also called “Data Labelers”).
KiLi
A powerful and user-friendly tool for labeling data, including invoices. It supports the labelling of images and texts, offering an intuitive interface to quickly and effectively annotate invoices.
Amazon SageMaker Ground Truth
An Amazon Web Services (AWS) service specially designed for data labeling. It offers advanced functionalities for labeling invoices, including automated labeling and quality review.
Conclusion
Labeling of invoices is an essential process for the effective management of financial documents within a company. With artificial intelligence and supervised learning, you can automate this process, improve accuracy, and save time. By exploiting these advanced technologies, you can optimize your processes, reduce errors, and focus on higher value-added tasks. Want to know more? Do not hesitate to contact Innovatiana for any questions relating to your use cases!
