Intelligent Document Processing (IDP) enables organizations to automate document-based business processes—reducing errors, improving efficiency, and increasing productivity. With IDP solutions, companies can extract valuable data from unstructured documents, such as invoices, receipts, contracts, and forms, and use it to streamline workflows, make informed decisions, and enhance customer experiences.
However, not all IDP solutions are created equal, and finding one that best fits your organization can be a daunting task.
So whether you’re looking to speed up claims processing, enhance account servicing, optimize document workflows, or improve compliance and security measures, this blog post will highlight four priorities to take into consideration when searching for the right intelligent document processing Solution.
1: How accurate is the IDP Solution?
An accurate IDP platform is critical for reducing costs, ensuring compliance, improving efficiency, and enhancing customer satisfaction. For customers, any errors made during the data extraction stage could result in life-altering ramifications—such as a mortgage loan being denied when it should have been approved, or a disability claim getting rejected when it should have been accepted.
Thankfully, recent advances in AI and machine learning have made it possible for IDP solutions to deliver high levels of accuracy for both typed and handwritten documents, Leading to less errors and more efficient document processing.
2: How easy is it to use the IDP solution?
Ease of use is a high priority when searching for an IDP platform because it can increase efficiency, shorten time-to-value (TTV), minimize costs, and increase adoption rates.
Efficiency: If an IDP solution isn’t user-friendly, it can take a lot of time and effort to train users to use the solution properly, reducing overall efficiency.
Time to value: Time-to-value is an essential factor when it comes to document processing solutions. A solution that is difficult to use will take longer to implement, and will be harder for users to learn—resulting in a longer time period before any value is realized.
Cost: A user-friendly solution can save costs associated with training, support, and maintenance. A solution that is easy to use requires less support and training, reducing the need for IT staff to be involved in the day-to-day operations.
User Adoption: Clunky, complicated solutions may lead to low user adoption, and can even push users back to manual processes. This can result in a lack of data consistency, errors, and decreased productivity.
For the reasons above, prioritizing a user-centric IDP solution that is easy to use can lead to a higher return on investment and improved business outcomes.
3: What about Human/AI Collaboration?
To ensure quality control, handle exceptions, provide continuous learning, and handle complex unstructured data, modern IDP solutions use intuitive human-in-the-loop (HITL) functionalities.
Human-in-the-loop machine learning is a method of incorporating human input and oversight into the training and decision-making processes of a machine learning model. This is done to ensure the model makes decisions that align with human values and objectives.
This also means that during any point in the automation process, the solution knows exactly when to involve an employee—but only when necessary. This human input is then used to finetune underlying machine learning models, continuously making the solution faster and smarter.
Without HITL, an IDP platform can suffer from lower accuracy or reliability, which can lead to errors in the data extraction process that will be costly to fix downstream.
4: Look for Proprietary Machine Learning Models
Proprietary machine learning models come with several advantages over their open-source counterparts.
Proprietary machine learning models can be more easily customized to meet the specific needs of your business or industry. These models are trained on your data and fine-tuned to meet your particular use case, leading to better accuracy.
With proprietary models, data never leaves the safety of your organization to access a public API. This keeps your sensitive data safe and ensures that your document data isn’t compromised.
Proprietary machine learning models can offer better performance and faster processing times than open source models, because they are often optimized for specific hardware and software configurations. This leads to more efficient processing.
Why choose Hyperscience?
When it comes to IDP, Hyperscience offers the most flexible intelligent automation platform for document processing and post-extraction data management, helping organizations improve customer satisfaction, increase revenue growth, and reduce costs.
With Hyperscience, proprietary ML models provide the best performance and highest data accuracy in the industry. To learn more about our IDP solution, check out a demo of the Hyperscience platform.
Want to learn more about all things intelligent document processing? Then download our Ultimate Guide to Intelligent Document Processing.