Revolutionizing Retail Invoicing: How Automated Data Extraction Can Boost Efficiency and Save 80% Time 

In the highly competitive retail sector of today, time is of the essence. Manual data extraction processes are laborious, error-prone, and consume valuable resources that could be better utilized elsewhere. But this is where automated invoice data extraction comes to the rescue. 

By harnessing the power of AI technology through automated data extraction, retailers can revolutionize their invoice processing, unlocking significant time savings and boosting overall efficiency. Invoice processing—which normally takes days to complete—can now be done within a couple of minutes. 

Currently, the average time for processing invoices manually stands at 10.9 days per invoice. For retailers, longer invoice lifecycles beget account payable and inventory delays. Additionally, most organizations find the cost of invoice processing to be between $15 and $40 per invoice. With these numbers in retrospect, let’s look at how a large retail chain has cut down their invoice lifecycle by nearly 80% using automated invoice data extraction.  

 

What is Automated Invoice Data Extraction? 

A retailer receives multiple invoices against purchase orders every day. The invoices shared by vendors or suppliers are first processed through the accounting and finance departments. They pull out the necessary data—ofttimes manually entering it into enterprise databases—and process payments accordingly. Similarly, other departments like Supply Chain need invoices to update their own inventory records.  

Automated Invoice Data Extraction is a process that uses either logical templates or Artificial Intelligence (AI) to automatically extract data from invoices, including purchase order numbers, vendor information, and payment terms. The more modern AI-driven extraction autonomously identifies, extracts, validates and then stores data without any manual intervention—eventually reducing invoice processing time to mere minutes. 

 

Adding Automation to Invoice Processing: Success Story of a Retail Chain 

A US-based supermarket chain, with several branches spread across North America, receives a little over 300 invoices from different suppliers each day. Processing these invoices in a timely manner, most of which are still shared in PDF or paper format, posed a real challenge to the retailer. On top of that, each invoice document—with its own distinct layout—carried long lists of goods being ordered for broad categories of products.  

The retailer had a ten-person team responsible for extracting information, such as order numbers, vendor information, dates, shipping details etc., and entering it into the system manually. On average, it took the retailer 15 days (about 2 weeks) to process the invoices—from data extraction to payment.  

Consequently, the inefficient process was time-consuming and error-prone, causing delays in account payables, data quality discrepancies, and supply-chain disruptions. To make matters worse, there was a growing trust deficit with the suppliers due to late payments and ofttimes incorrect invoicing details. 

How did the retailer circumvent this challenge?  

They replaced manual invoice processing with an automated invoice data extraction solution. And the results were magical!  

By implementing automated data extraction, they were able to replace the manual invoice processing approach with an agile one. What was otherwise being done manually by multiple resources is now handled by a single AI-driven solution. It automatically recognizes relevant fields on the invoice and extracts and loads data for consumption. This has significantly reduced the time and resources needed to process invoices.  

 

Saving Time and Improving Efficiency through Automated Data Extraction 

An internal audit had earlier revealed that the supermarket retailer’s inefficient invoice processing was inadvertently causing the company thousands of dollars each year.  

This changed with the implementation of automated invoice data extraction. The invoice processing lifecycle—which was initially taking 15 days to complete—was reduced to a mere 2 days. That is a near 85% reduction in the time spent on invoice data extraction, loading, and the eventual payment processing. This has led to timely account payments, satisfied vendors, and zero stalk-outs due to seamless inventory management.  

But that is not it. With AI-driven data extraction in place, invoice processing has become nearly self-serving. The resources manually extracting data from invoices are now focusing on more important, less redundant tasks e.g., financial analytics and supply chain management. Additionally, the retailer is reporting a decrease in data discrepancies and quality issues. This is precisely because the new data extraction solution eliminates human errors and validates the data automatically before loading into the database.  

 

How the Retailer Implements AI-driven Invoice Data Extraction 

The supermarket chain is revolutionizing retail invoicing by going automated! The benefits in terms of 80% time-savings, inventory management, and data quality are unprecedented in the retail sector. Let’s take a look at how our retailer leverages AI and automation for invoice data extraction. 

• Invoice Capture: 

 

The retailer receives hundreds of invoices each day in different formats. For example, some vendors share PDF invoices while others email images or text files. The first step is to capture these invoices automatically, recognize their distinct format, and convert them to optimized electronic copies. Electronic copies are better for data extraction purposes. Here, the system is running on self-service which means that invoices are captured automatically as they arrive digitally through email alerts.  

 

• AI-Template based Data Extraction: 

It then uses AI-template based data extraction for pulling data from captured invoices, irrespective of their layouts. Here, self-serving AI utilizes Natural Language Processing (NLP) to automatically generate a template based on the fields or data required by the user. 

For example, the retailer identifies the fields it needs data for—such as order number, vendor information, dates, shipping details, etc.—and the AI itself extracts relevant data from any type of invoice in no time. It autogenerates flexible templates based on the different layouts eliminating the need for creating new templates for every distinct invoice.  

And you know what’s best? Since AI-based templates are created through machine learning algorithms, they are highly accurate and reliable. For example, they recognize even small discrepancies in invoicing terms and still be able to extract relevant data. 

 

• Data Quality and Validation: 

Once the data is extracted from the invoices, it is validated and cleansed for consumption. The retailer’s automated data extraction uses pre-built checks to automate the process of data validation and cleaning. The cleansed data is then converted to JSON to ensure compatibility with the retailer’s other data processing systems.  

 

• Invoice Processing Approval: 

Next, the invoice is shared with the retailer’s finance department for approval and processing of account payable. Rather than sending extracted data manually, the retailer has set up a workflow which automatically alerts the approval section of the finance department whenever a new invoice is received. The workflow routing is based on AI algorithms that seamlessly share the data with relevant stakeholders. Finally, after due approval, the account payable for that invoice is cleared by finance. 

  

• Integration With Other Systems: 

The retailer couples the ability to intelligently recognize data with seamless integration with other systems in the invoice data pipeline. Their AI-driven data extraction solution provides native support for popular cloud databases or financial management systems such as Quickbooks, SAP, Oracle, Snowflake etc. This means that the retailers can automatically pass on the invoice information to relevant systems/databases through self-serving workflows. 

Here, deep interoperability with other systems ensures that the retailer’s invoice data is not processed in isolated silos. Other departments, such as Supply Chain or Auditing, are also able to access this data for meaningful analytics.  

 

Astera ReportMiner: The Ultimate AI-Driven Invoice Data Extraction Tool for Retailers 

Retailers deal with large volumes of invoices every day. With an exponential increase in their business activities, manual processing of these invoices is affecting their efficiency and productivity. Here, an AI-powered data extraction solution offers to revolutionize invoice data extraction and processing.  

Equipped with Astera ReportMiner, retailers can follow in the footsteps of the aforementioned retail chain and reduce their invoice processing lifecycle by more than 80%. Our way of invoice data extraction provides several advantages over other manual methods, including improved accuracy, consistency, speed, and flexibility. 

Essentially, Astera ReportMiner empowers retailers to extract data from unstructured invoices using cutting-edge AI capabilities. With advanced AI Capture technology, our tool enables you to build reusable extraction templates in seconds, extract relevant data, and process it using robust data pipelines or workflows. 

Want to experience Astera ReportMiner’s magic? Sign up for a free 14-day trial today and gear up to revolutionize retail invoicing.