Why Autonomous AI is (finally) Disrupting Corporate Finance for Good

Print Friendly, PDF & Email

In this special guest feature, Kunal Verma, CTO and Co-Founder of AppZen, discusses how technology like RPA and AI are having their breakout moments as company leaders realize they are no longer nice to have, but a business-critical tool to stay ahead of the competition. Kunal is responsible for the company’s product vision as well as overseeing the company’s R&D and data science teams. Kunal co-founded AppZen in 2012 when he developed its core artificial intelligence technology. Previously, he led research teams at Accenture Technology Labs that were responsible for developing AI-based tools for Fortune 500 companies. He earned his Ph.D. in Computer Science from the University of Georgia with a focus on semantic technologies.

The need for accuracy, speed,and cost optimization has thrust automation into the spotlight as one of the most significant digital transformation drivers—especially given the events of the past year. When you look at the numbers, technology  like RPA and AI are having their breakout moments as company leaders realize they are no longer nice to have, but a business-critical tool to stay ahead of the competition.

AI is helping organizations execute tasks that were previously hard or in some cases impossible to achieve efficiently, effectively, and accurately by leveraging valuable insights from copious amounts of structured and unstructured data. And it’s thanks to this ‘big data’ that democratizing data has become a reality—one where you remove any gatekeepers that create a bottleneck and limit access to important information. You no longer need to be a data scientist to access and understand what the data is telling you because the logic as to how you use the data is independent of the process of data generation.  The advent of big data has really driven the rise of AI and where it is today, companies have to have good historical data to ingest into their AI platforms.  The old adage is true – good data in – good results out.

This approach enables cross-team functionality and ease of use when interacting with other parts of the business through things like data visualization and dashboards, which can be easily shared and understood by the C-suite all the way down. 

Despite several companies already leveraging automation, nowhere is AI currently having the biggest impact than on corporate finance departments, disrupting how finance teams operate and work—both within, and outside of, the organization. Finance teams have traditionally been plagued by manual processes, human oversight, as well as legacy technology. AI is changing all of that by removing barriers and making data much more accessible. Finance teams can now automate complex financial and compliance processes such as auditing documents—from expense reports and invoices, to packaging slips and receipts. 

Three AI technologies crucial to corporate finance

In order to become a truly autonomous finance team, it’s vital to leverage three crucial AI technologies simultaneously —Computer Vision (CV), Natural Language Processing (NLP), and Semantic Analysis (sometimes called Semantic Understanding). This combination ensures the system can understand structured and unstructured data, while continuing to learn from billions of transactions, data points, and user feedback.

Over the past few years, advancements in AI have enhanced Computer Vision technology to the extent where we can now easily read text from receipts—even if they’re barely legible like the ones you receive from yellow cabs. When auditing financial documents, deep learning based CV models are running behind the scene to extract information, while state of the art Natural Language Processing techniques from various research institutions help us understand the language. NLP is used in our everyday lives when we use virtual assistants like Siri and Alexa, but businesses are starting to explore applications to speed productivity.  For example, natural language processing technology is being used to transcribe conversations in real time, which can then be used to extract data, allowing for AI to make decisions based on this information.

With Semantic Analysis, you’re able to understand and build relationships between disparate, extracted data like dates, prices, discounts, payment terms, and line-level spend categories, removing the need for manual intervention to review otherwise unknown or unclassifiable pieces of data. For example, let’s say you receive an invoice from a colleague who took a client out to dinner a few nights ago—by leveraging semantic classification to draw inferences from the data, the system will be able to read and understand the receipt and that you ordered filet mignon, which is a type of meat, which is a type of food, but also that it is something that can be expenses according to company policy.

Autonomous AI is driving true digital transformation

There’s also a lot of emphasis being put on automation with technologies like Robotic Process Automation (RPA), which can easily handle repeatable tasks, manage structured data (only), and requires a good amount of human interaction. While RPA is a beneficial technology and works well with AI, corporate finance teams need something more that allows them to harness (both structured and unstructured) big data to become truly autonomous, which can only be done with AI. With accuracy requirements being pretty high in finance (e.g. compliance, audits, etc.), AI adoption has been somewhat challenging, but autonomous AI (and the three core AI technologies) has been the ultimate disrupter.  

Being able to process invoices autonomously—from PDF to paper formats—allows approvals and decisions to be made without time-consuming manual human review that has historically taken weeks to accomplish. Modern Finance teams require autonomous AI-based solutions that do the heavy lifting and save time-consuming human review only for exceptions. Your team can instead focus attention on issues that require resolution, investigation, or nuanced decision making instead of sorting through mountains of expenses and invoices. They can also spend more time on what they do best: forecasting and supporting the company’s long-term, strategic financial goals and objectives.

So, at the end of the day our journey to truly harnessing the power of AI is inextricably linked to big data and the ability to have a platform that can understand it to allow organizations to make valuable business decisions, driving efficiency, cost-savings and more.

Sign up for the free insideBIGDATA newsletter.

Join us on Twitter: @InsideBigData1 – https://twitter.com/InsideBigData1

Speak Your Mind