What is financial data extraction?
A critical part of effective decision-making for businesses is financial data extraction. Though challenging, conducting operations conducive to the business’s financial health is crucial.
Financial data extraction technology can address the challenges: it can help avoid many errors that we may be prone to manually. This guide will look at automating financial data extraction and how businesses can use it.
What is financial data extraction?
Financial data extraction refers to the process of capturing, extracting, and transforming relevant financial information from various sources, such as invoices, receipts, bank statements, financial reports, and other financial documents.
It involves converting unstructured or semi-structured data into structured data that can be easily analyzed, processed, and utilized for financial analysis, reporting, and decision-making.
The global data extraction market was valued at $2.14 billion in 2019 and is projected to reach $4.90 billion by 2027, growing at a CAGR of 11.8% from 2020 to 2027. This fast-growing market is evidence of the gains that financial data extraction can bring to a business.
Why should you automate financial data extraction?
Traditionally, financial data extraction has been performed manually, with employees manually inputting data from documents into spreadsheets or accounting systems.
However, this manual approach is time-consuming, error-prone, and inefficient, especially when dealing with large volumes of financial documents. It increases the risk of data entry errors, hampers productivity, and limits the ability to leverage data for strategic insights.
With the advancements in technology, automated solutions using Intelligent Document Processing (IDP) and AI have emerged, revolutionizing the way financial data is extracted.
Who is automated financial data extraction for?
Automated financial data extraction is for businesses of all sizes that need to extract data from financial documents. This includes businesses in the following industries:
- Accounting: Accounting firms use automated data extraction to audit financial statements, prepare tax returns, and manage accounts payable and receivable.
- Banking: Banks use automated data extraction to process transactions, manage risk, and comply with regulations.
- Insurance: Insurance companies use automated data extraction to process claims, manage risk, and underwrite policies.
- Investment: Investment firms use automated data extraction to track portfolios, analyze investments, and make trading decisions.
- Manufacturing: Manufacturing companies use automated data extraction to track production data, manage costs, and improve efficiency.
- Retail: Retail companies use automated data extraction to track inventory levels, manage sales data, and make pricing decisions.
- Telecommunications: Telecommunications companies use automated data extraction to manage customer accounts, track usage data, and bill customers.
- Healthcare: Healthcare organizations use automated data extraction to track patient records, manage billing, and comply with regulations.
Automated financial data extraction can be used by any business that needs to extract data from financial documents. The benefits of automated data extraction make it a valuable tool for businesses of all sizes.
What are the benefits of automated financial data extraction?
There are many benefits to automating financial data extraction, including:
- Cost savings: Manual data extraction is a time-consuming and labor-intensive process. By automating this process, businesses can save on the costs of hiring and training data entry clerks.
- Improved accuracy: Automated data extraction is more accurate than manual data entry. This is because the software can be programmed to identify and extract data consistently and accurately.
- Reduced risk of errors: Manual data entry is prone to errors. Businesses can reduce the risk of errors in their financial data by automating this process.
- Increased efficiency: Automated data extraction can help businesses improve their efficiency by freeing up employees to focus on other tasks.
- Improved decision-making: Automated data extraction can help businesses make better decisions by providing them with access to real-time and accurate data.
- Compliance: Automated data extraction can help businesses comply with regulations by ensuring that their financial data is accurate and up-to-date.
How can Nanonets help you automate financial data extraction?
Nanonets is a powerful AI-powered platform that can help you automate financial data extraction. Here are some of the ways Nanonets can help you:
- Intelligent document processing: Nanonets uses intelligent document processing (IDP) to extract data from financial documents. This means that Nanonets can understand the layout and structure of different document types and extract the relevant data fields accurately and reliably.
- Customizable extraction models: Nanonets allows you to create custom extraction models that are tailored to your specific needs. This means you can train the system using your own document samples, ensuring the extracted data is relevant to your unique document formats.
- End-to-end automation: Nanonets offers end-to-end automation for financial data extraction. This means that the entire process can be automated, from document ingestion to data extraction and validation, eliminating the need for manual intervention. This saves time, reduces errors, and improves overall operational efficiency.
- Integration and workflow optimization: Nanonets can seamlessly integrate with your existing systems and workflows. This means that you can easily transfer data between Nanonets and your other systems and optimize your workflows to improve efficiency.
- Scalability and performance: Nanonets is designed to handle high volumes of financial documents easily. This means that you can scale Nanonets to meet your needs and be confident that it will perform reliably even when processing large amounts of data.
- Data security and compliance: Nanonets prioritizes data security and compliance. This means that robust security measures protect your data and that Nanonets complies with industry standards and regulations, such as GDPR.
Automating financial data extraction with Nanonets can significantly reduce manual effort, improve data accuracy, and enhance overall productivity. The streamlined workflow and accurate extraction of financial data enable better decision-making, faster processing times, and improved operational efficiency.
What is financial data extraction?
Financial data extraction refers to the process of extracting relevant financial information from various sources, such as invoices, receipts, bank statements, and financial reports.
How do you pull financial data?
You can pull financial data from a variety of sources, such as websites, APIs, and databases.
What are the three types of financial analysis?
The three types of financial analysis are vertical analysis, horizontal analysis, and ratio analysis.
What are the methods of financial data?
Financial data can be obtained through primary sources like financial statements or invoices, and secondary sources like academic journals and financial reports.