How Auditoria.AI Is Building AI-Powered Smart Assistants For Finance Teams


Be it marketing, sales, or even security, most departments in large organizations today have a range of SaaS tools at their disposal to help make their work more efficient. But people in corporate finance and accounting have been underserved in that respect. Their days are still spent on routine, mundane tasks that keep them away from more stimulating work. Auditoria.AI aims to change that by automating those functions with AI and natural language technology.

We strive to improve the lives of finance and accounting professionals by automating the routine, repetitive, and laborious parts of the finance function, such as copy-and-pasting data, validating documents, and checking for errors on spreadsheets, freeing these teams to focus instead on providing valuable, strategic insights to the business. 

To that end, the problems we’re solving affect three major finance functions:

  • Accounts Payable, responsible for sending money out of the company, such as bill payments. 
  • Accounts Receivable, responsible for bringing money into the company, such as invoicing for services. 
  • General accounting, a broader term consisting of functions of the general ledger team and the CFO, including closing books. 

Historically, making these processes more efficient entailed dedicating more personnel to them. But this didn’t necessarily mean more work was done faster and to the highest standards. Many finance professionals are often overworked, dedicating extra hours, weekends, and sometimes holidays to process invoices, collect payments, and close the books on time. 

We created solutions for these three finance functions, with our SmartBots taking care of the back-and-forth communications between finance teams, vendors, and customers. In large companies, these micro-transactions add up to thousands per day, resulting in finance professionals spending entire days reading inquiries, interpreting requests, looking for relevant information, and answering as many as possible. But with our AR helpdesk, for example, accounts receivable tasks, such as a request for a copy of an invoice, get automated. Our technology reads emails and attachments to understand what is being requested. Then it connects to the Enterprise Resource Planning (ERP) software to grab the relevant information and attach it to the email, so the recipient gets a response within 60 seconds. 

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Building the smart assistant that finance teams need

In our automation flow, we constantly handle different types of documents, from invoices and tax forms to receipts and email messages. But processing the interaction between computers and human language is complex. You must detect intent and facts, and understand the context before finding the specific slots of information extraction that may be relevant to specific processes and requests. Our solution adds value by extracting the right information in the right context, from the right document, for the relevant finance function. Instead of building everything from scratch, we turned to Google Cloud’s Document AI to support extracting data from unstructured documents to understand and analyze them. 

Document AI comes with pre-built models that help analyze specific parts of our post-production lifecycle. For example, Invoice Parser extracts text and values from invoices, including invoice number, supplier name, invoice amount, tax amount, and invoice due date, all of which are necessary for our SmartBots to execute an extraction workflow. These out-of-the-box features significantly accelerate our own product development process and time-to-market, which are critical for the performance of a startup such as ours. 

To ensure a high quality of information extraction, we used to do document readings in-house. Having automated some of that with Document AI, we’re at 85% accuracy extracting files, and with some additional customization efforts, we will achieve 95%+ extraction accuracy.

Meanwhile, we have now streamlined internal processes, which ultimately translates into faster services for our customers. For example, assuming all the information has been provided, it generally took up to 15 minutes to process a tax form. We now do that in seconds. 

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The value of automation doesn’t stop there. Using DocumentAI to automate structured data extraction from documents, we have managed to:

  • Speed up the collection of general ledger entries by 90%+
  • Reduce errors and omissions by 85%+
  • Close books 20% faster
  • Improved the productivity of full-time employees by 60%+
  • Reduce process workload by 75%+
  • Improve vendor serviceability by 75%+
  • Reduce vendor risk and fraud by 50%+

Leveraging automation to focus on more innovation

Automating some of our processes with Document AI also means we have more time to focus on developing new features and further improving our solution. 80-90% of the time used for extracting custom fields from documents has now been automated with an OCR metadata library. 

With Document AI taking care of standard extraction, we focus on the intelligence we add to post-extraction. For example, when an invoice comes in from a vendor, our application needs to figure out which vendor it is to match it to the correct records in the ERP. But variations in the documents could interfere with that extraction process. The vendor’s trading name might be slightly different from the company’s name registered in our internal system, delaying the process. With more time on our hands, we’re now working on features enabling our models to leverage logos and other elements extracted from documents to swiftly match them to the correct company registered in our systems.  

With the benefits we’ve seen thus far, we look forward to accelerating our international growth. We’ll be relying on Google Cloud’s Document AI to automate operations, potentially in different languages, as we continually remove friction from the work lives of finance and accounting people worldwide.  

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By: Adina Simu (Chief Commercial Officer, Chief Product Officer, and Founder, Auditoria.AI)
Originally published at Google Cloud


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