Document Annotation Services are Helping Companies to Manage their Finances

Developments in machine learning are being felt in a diverse range of sectors. Computer vision based models which have been utilized in transport and the manufacturing industry are now starting to be introduced in areas which have not traditionally been associated with tech innovation, such as corporate accountancy and contract creation.

Systems incorporating machine learning algorithms can help to make basic accounting and legal tasks more efficient and more accurate. This technology supports staff by removing the burden of repetitive and time consuming manual work, freeing up human resources to be used in more complex contexts.

This blog will look at three ways in which AI is being imported into company processes. These use cases are made possible through document annotation. By highlighting key parts of various financial documents, annotators create training datasets that allow computer vision models to learn. Professional annotation services provide unique advantages that support innovation in this promising sector.

Calculating expenses

Interpreting and calculating expenses forms is time consuming and repetitive. In large organisations the day to day processing of these forms can represent a significant workload burden. AI systems can help to accelerate this common task by automatically processing expenses forms.

In order to achieve this machine learning models have to be trained with annotated examples of expenses documents. This enables them to recognize important parts of forms, such as individual costs and totals claimed. Once key information has been extracted forms can be processed and data can be stored. Automating this procedure can save departments thousands of labor-hours in aggregate.

Analyzing receipts and invoices

Printed receipts and submitted invoices contain large amounts of information that can be useful for a variety of organisations. Processing these documents is also a laborious job that can keep staff from more important tasks. However, automating receipt meta-data collection and invoice processing is a difficult challenge. Both come in many different configurations and can be hard for machine learning algorithms to parse.

High quality document annotation can help to overcome this. Annotators can use bounding box tools to locate important document information, including relevant text and numerical data. Computer vision models need datasets that are both large and varied, this allows AI systems to function well when faced with the complexity of invoices and receipts in the real world.

Accelerating contract creation and review

Firms tend to employ standard templates for bespoke contracts, following established conventions and containing sets of acceptable terms. AI can be used to construct contracts following these sets of criteria. Document annotation also allows contracts to be checked for levels of compliance with established company policy.

AI systems can cross reference new contracts with paradigmatic examples and identify if there are missing clauses or errors. This ensures that all contracts created within a company conform to an agreed upon standard, whilst also making the process of creation and review much faster.

Document annotation helps this technology to be successful by locating important areas of text in training data contracts. This helps machine learning models to locate important data in diverse documents.

Finding the right document annotation service

Keymakr is a data annotation service that provides advantages to developers in search of document annotation.

• Efficient annotation platforms: The right annotation platform can significantly improve the speed and accuracy of document annotation. Keymakr’s proprietary annotation tools boast unique workflow options and advanced analytics capabilities.
• In-house annotation teams: Crowdsourced annotations from a remote workforce can be cheap, but they often come with errors that can affect model performance. Keymakr has an in-house annotation team, led by experienced managers. This structure leads to more precise annotation, better communication, and effective trouble-shooting.
• Controlling costs: Outsourcing to annotation services helps computer vision developers keep annotation costs down, whilst retaining flexibility.