The world of finance is jam-packed with data. As the landscape of commercial transactions continues to evolve, finance professionals must deal with an influx of invoices, receipts, and payments. To provide a unified view of invoice data, ensure timely payments and improve vendor relationships, enterprises are looking to adopt modern data extraction techniques. 

Fortunately, there’s a solution: automated invoice data extraction. By utilizing AI-based techniques, businesses can save up to 30–40% of the time typically spent on manual processing. Automating the capture and processing of invoice data allows finance teams to optimize their workflows, cut costs, and break down data barriers. This results in improved data visibility and better-informed decision-making, giving businesses a distinct competitive advantage. 

From Manual to Automated: How a Financial Services Company Reduced Costs and Boosted Efficiency 

A US-based global financial services organization handled over 500 invoices from multiple vendors and suppliers daily. The sheer volume of invoices meant their accounts payable team struggled to process them efficiently. Also, each invoice had a different layout, which made it challenging for their team to extract the relevant data accurately. 

Moreover, a data quality audit revealed that a significant portion of their financial data was incorrect due to human error in the data entry process. On average, the cost of fixing these errors was $53.50 per paper invoice, leading to losses that ultimately affected their bottom line. 

Recognizing the urgent need to enhance invoice processing efficiency and data accuracy, the company opted for an automated invoice data extraction solution. By implementing this solution, the company successfully automated the extraction of crucial metrics from their invoices, including invoice number, total amount, and due date. As a result of the solution’s capability to manage multiple vendor invoicing formats and layouts, their team was able to effortlessly extract precise data with efficiency. 

The results were staggering. The implementation of automated invoice data extraction enabled the company to process a significantly higher number of invoices without manual intervention, saving them time and resources.  

A Sneak-Peak into How the Company Reduced Error-Related Costs 

With costs associated with manual labor for invoice processing eliminated, let’s explore how the global financial services enterprise effectively reduced its expenses. Here’s a breakdown specifically highlighting the cost implications of errors: 

The company processed 15,000 paper invoices per month, resulting in a 2% error rate or 300 invoices requiring correction. This error mainly occurred due to manual entry of data. The average cost to rectify each error was $53.50, resulting in a total monthly cost of $16,050 for those 300 invoices. 

 However, after implementing the automated invoice data extraction solution, the company managed to reduce the error rate to less than 0.5%, leaving less than 75 invoices with errors that needed attention. Consequently, the new total cost of addressing errors for these 75 invoices amounted to $4,012.50 per month, showcasing a substantial 75% reduction in error-related expenses, representing yearly savings of roughly $48,000. 

This company’s success story serves as a compelling testament to the effectiveness of automated invoice data extraction solutions and highlights why it’s essential for any organization looking to stay ahead in the game. 

Automation & AI: How the Global Financial Services Company Achieved Success in Invoice Processing  

The US-based company has revolutionized its financial operations by harnessing the power of automation and AI, resulting in significant cost savings, improved accuracy, and increased efficiency. Let’s examine their steps to achieve this transformation and see how automation and AI can give companies a competitive edge. 

1. Receipt Capture and Conversion: To start off the automated invoice data extraction process involves capturing receipts and converting them into an electronic format. The company received invoices primarily in PDF format through email. They were processed using optimized email capture and conversion methods to ensure high-quality electronic copies. This enabled accurate and efficient data extraction from the invoices.

2. Data Extraction and Validation: This stage includes extracting and validating relevant information, including vendor name, invoice number, and total amount. The company employed state-of-the-art deep learning technology to automatically extract financial data, including handwritten text, from various sources. The extracted data was then converted into JSON format for seamless integration with other financial systems. To further enhance accuracy and speed, their invoice data extraction solution was integrated with OpenAI’s language processing models.

3. Matching with Financial Records: Moving on, the invoice data is then matched with supporting documents, such as purchase orders and contracts. This additional step ensures that the transactions are valid and authorized for payment. At the financial services company, this matching process was automated using AI-powered algorithms that can quickly and accurately match financial data with corresponding records, reducing the need for manual intervention.

4. Approval Routing: Here financial transactions are automatically routed for approval. This process involves sending the transaction to the payment department based on predefined rules and workflows. At the financial services company, this routing process was automated using AI-powered algorithms that can route transactions quickly and accurately, ensuring that the right people were involved in the approval process.

5. Posting to Financial Systems: Once the invoices are approved, they are automatically posted to the financial systems. This ensures that the financial data is accurately recorded and available for reporting and analysis. At the financial services company, this posting process was automated using AI-powered algorithms that can post transactions quickly and accurately, reducing the need for manual data entry.

6. Archiving for Audit and Compliance: Finally, the financial transactions are archived for future audit and compliance purposes. This involves storing the transactions in a secure and easily accessible location. At the company, this archiving process was automated using cloud-based storage solutions to securely store large volumes of financial transactions and make them easily accessible for reporting and analysis. 

Astera ReportMiner: The AI-Powered Solution for Automated Invoice Data Extraction 

Managing financial operations can be an arduous task, especially when extracting data from hundreds of invoices with different layouts and formats. With Astera ReportMiner, an AI-powered data extraction tool, financial organizations like the one we saw in this blog can easily extract necessary data from invoices containing different layouts. Our revolutionary Astera North Star feature leverages AI to create report models within minutes, allowing your team to focus on other high-value tasks. 

But that’s not all! Astera ReportMiner also offers streamlined data validation through its robust data quality rules transformation. This ensures that your data is always accurate and consistent, empowering you to make informed decisions and promoting compliance with regulations, thereby paving the way for operational efficiency. 

Don’t just take our word for it. Sign up for a free 14-day trial today and experience the power of Astera ReportMiner’s AI-driven data extraction for yourself!