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Managing Unstructured Data Collection in Finance Departments

Laura Bulmer Author: Laura Bulmer   |    On: Wednesday Jun 12, 2024

Managing unstructured data collection in finance departments can often fall to the bottom of the list as finding the most efficient way to handle both structured and unstructured data is a challenge.

Structured data will be represented by documents such as purchase orders and invoices and is usually prioritised when it comes to automated data capture. Unstructured data is more complex and can come from sources such as financial reports, contracts, customer feedback, emails, bank statements, orders and audit documentation.

Whilst the effective management of unstructured data collection is more complex, it is crucial that organisations find a way to handle this if they want to optimise financial operations to ensure compliance, and make informed decisions.

Considerations When Tackling Unstructured Data Collection

Finance departments face several challenges with unstructured data:

  • Diverse Data Sources - Financial data comes from various sources, making consolidation difficult. Think about the various types of unstructured data your organisation handles and how well it is being managed currently. How well is it being processed compared to structured data?
  • Regulatory Compliance - Ensuring unstructured data collection and analysis comply with financial regulations is critical.
  • Data Sensitivity - Financial data often includes sensitive information that requires secure handling. Do you have bespoke user permissions enforced on this sensitive data?
  • Real-time Processing - Financial markets need real-time data analysis, which can be challenging with unstructured data. If you have a large proportion of unstructured data, is this being left out of reporting?
  • Complexity of Financial Technology - Analysing data related to complex financial processes requires sophisticated models and software. Do you have the right people to handle this?

Solutions for Unstructured Data Collection

invoice data capture_reduce error prone typingFinance leaders generally know that a technology solution will be needed for tackling both structured and unstructured data collection, as it will be more accurate, reliable, and quicker than alternative methods. But how do they know what to choose?

Adopting new technology can seem daunting or expensive, but over time staff will see the benefits of the right solution, and a return on investment is guaranteed. Knowing the types of data you need to work with is critical when choosing a data collection tool.

Document Management Software

DocuWare is a robust document management and workflow automation solution that now has the capability to process structured and unstructured data collection.  DocuWare has  been able to accurately capture structured data using machine learning for years, but has been developed further to handle unstructured data as well.  

With development and advances in technology, DocuWare is now armed with inbuilt AI which is makes it capable of handling unstructured data capture and collection. With this inbuilt AI, unstructured data from an email or compliance certificate can be handled in much the same way that DocuWare has always handled structured data sources like invoices.

A document management system provides a centralised repository for all document types such as invoices, contracts, emails, and financial reports. The web-based storage system makes unstructured data easier to manage and access.

Learn More About Document Management

Smart Indexing

DocuWare automatically indexes documents using metadata and OCR (Optical Character Recognition), converting unstructured data into searchable, manageable information.

The OCR and AI data capture tools provide the ability to handle both structured and unstructured documents. They can extract specific words or a string of numbers from invoices, emails, scanned documents and images, transforming important data into a usable format. Key information from documents is quickly captured, removing manual data entry and associated errors.

There is a huge time saving to be made by employees that no longer have to look through documents to source the information they need and then enter that into finance or ERP packages.

Automation improves customer serviceWorkflow Automation

With automated document workflows, documents become part of pre-configured processes such as invoice approvals, expense report handling, and contract management.

The extracted data is used to pre-fill store dialogues, which are used to store documents from a tray, into the system.  The data can also be used for integration purposes where DocuWare is integrated with your ERP or finance solution.

Integration

DocuWare’s seamless integration with financial systems ensures a smooth data flow and reduces document duplication.  Both structured and unstructured data can be captured and pushed to other systems without the need for manual data entry.

Using AI for Managing Unstructured Data Collection

DocuWare’s AI functionality leverages artificial intelligence, particularly natural language processing (NLP) and machine learning (ML), to handle unstructured data.
It uses sophisticated Optical Character Recognition (OCR) technologies to extract text from documents, which now includes handwritten information.

NLP capabilities give the software the ability to analyse meaningful information from text data, understanding and interpreting context and intent.  Information collected from unstructured data can automatically be categorised based on its content, reducing the need for manual sorting.

DocuWare’s AI tool identifies patterns and trends within unstructured data which could otherwise be missed or take time to find. The unstructured data that’s collected can be processed in real-time, which is particularly useful for time-sensitive financial data. This enables continuous learning as AI tools improve accuracy and efficiency over time.

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Cut data entry timePractical Applications of AI and DocuWare in Finance

By using new AI technologies for unstructured data collection, a variety of business processes can be made more efficient.  In our experience so far, there are several practical applications for the use of AI in finance:

Invoice Processing

DocuWare automates the capture, indexing, and approval of invoices, speeding up the entire accounts payable process.  AI is used to extract data from invoices, categorise them, and validate information against supplier lists, POs or proof of delivery notes.

Your Guide to Automated Invoice Processing

Expense Management

DocuWare can be used as expense management software, managing the submission, approval, and documentation of expenses, with employees able to enter expense information straight into a digital form. AI extracts and analyses data from emails, contracts, receipts and expense reports, automating validation and categorisation.

Financial Reporting

DocuWare centralises and manages financial reports, making them easily accessible and searchable.  The AI functionality within the system can analyse unstructured data from various reports to extract insights and generate summaries.

Contract Management

DocuWare organises and tracks contract lifecycles, with automated reminders for renewals. For contract management, AI extracts key terms and dates from contracts, (unstructured data collection), ensuring compliance and timely renewals.

Conclusion

DocuWare’s inbuilt AI is a powerful tool for handling unstructured data collection in finance departments. DocuWare excels in document management and workflow automation, providing a structured approach to handling documents and data.

It’s inbuilt data capture tools leverage AI and machine learning to extract, analyse, and automate processes involving unstructured data. By utilising these tools, finance departments can significantly improve their efficiency, accuracy, and compliance in managing unstructured data.

If you’d like to chat to us about how we can help with unstructured data collection then a member of our team will be more than happy to help.

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