In today’s digital age, the ability to extract relevant data efficiently from financial statements is paramount for businesses seeking to streamline their processes and gain valuable insights into their financial health. Automating this task can save significant time and resources while reducing the potential for errors associated with manual data entry. In this article, we will explore how to automate the extraction of data from financial statements effectively. InferIQ, a Generative AI-Powered Intelligent Document Processing (IDP) platform, facilitates automation efficiently.  

  1. Choose the Right Tool 

Selecting the appropriate software or tool is crucial for automating data extraction from financial statements. Look for solutions specifically designed for this purpose, such as optical character recognition (OCR) software or data extraction platforms. These tools are equipped to recognize and extract data from various financial statement formats, including PDFs, scanned documents, and electronic reports. InferIQ, designed for data extraction, recognizes and extracts data from various financial statement formats like PDFs and scanned documents. 

  1. Define Extraction Parameters 

Clearly define the data points you need to extract from the financial statements. This may include values such as revenue, expenses, net income, and balance sheet figures like assets, liabilities, and equity. By specifying data points like revenue, expenses, and balance sheet figures to ensure accuracy in extracted data. 

Refer to the below screenshot to see how InferIQ extracted the data from financial statements. 

  1. Train the Models 

Many data extraction tools allow for training or customization to improve accuracy. Train the models to recognize the layout and structure of your specific financial statements, and this may involve providing sample documents and manually verifying the extracted data to refine the extraction algorithms. InferIQ allows customization for improved accuracy. Train it with sample documents and verify extracted data manually to refine algorithms. 

  1. Financial Account Mapping Standardization  

Once the data is extracted and extraction accuracy is good, the solution needs to map the financial accounts to standardized GAAP-based accounts. This step will greatly improve the time taken to process the financial statements.  

  1. Implement Automation Workflows 

Integrate InferIQ into existing workflows to automate the entire process seamlessly. This may involve setting up scheduled extraction tasks or integrating the extraction tool with other software applications, such as accounting systems or data analytics platforms. 

  1. Validate Extracted Data 

While automation can significantly streamline the extraction process, it’s essential to validate the accuracy of the extracted data. Implement validation checks to compare the extracted values against source documents or known benchmarks. Address any discrepancies promptly to maintain data integrity. 

  1. Monitor Performance and Refine 

Continuously monitor the performance of your data extraction system and identify areas for improvement. Analyze extraction accuracy, processing speed, and any issues encountered during the extraction process. Refine extraction parameters and workflows as needed to optimize efficiency and accuracy. 

By following these steps, businesses can effectively automate the extraction of data from financial statements, saving time, reducing errors, and unlocking valuable insights into their financial performance. Embracing automation in data extraction not only enhances efficiency but also empowers organizations to make data-driven decisions with confidence. Schedule an InferIQ demo today and experience the power data extraction.