Why autonomous AI is (finally) disrupting corporate finance


In this special guest post, Kunal Verma, CTO and Co-Founder of AppZen, discusses how technologies like RPA and AI are going through watershed moments as business leaders realize they are no longer nice to have, but an essential 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 it developed its core technology into artificial intelligence. Previously, he led research teams at Accenture Technology Labs that were responsible for developing AI-based tools for Fortune 500 companies. He obtained his PhD. in Computer Science from the University of Georgia with an emphasis on Semantic Technologies.

The need for precision, speed and cost optimization has put automation in the spotlight as one of the most important digital transformation drivers, especially given the events of the past year. When you look at the numbers, technologies like RPA and AI are going through watershed moments as business leaders realize that they are no longer nice to have, but are an essential tool for business. company to stay ahead of the competition.

AI helps organizations perform tasks that were previously difficult or, in some cases, impossible to perform effectively, efficiently, and accurately by leveraging valuable information from large amounts of structured and unstructured data. And it is thanks to this “big data” that the democratization of data has become a reality, where you remove all the 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 data is telling you, because the logic of how you use data is independent of the process of generating data. The advent of big data has really driven the rise of AI and where it is today, companies need to have good historical data to ingest into their AI platforms. The old adage is true – good data coming in – good results going out.

This approach enables cross-team functionality and ease of use when interacting with other parts of the business through elements such as data visualization and dashboards, which can be easily shared and including C until the end.

While several businesses are already taking advantage of automation, nowhere does AI currently have the greatest impact on corporate financial services, disrupting the functioning and work of finance teams, both within and within. outside the organization. Finance teams have always been plagued by manual processes, human oversight, and legacy technologies. 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, expense reports and invoices to packing slips and receipts.

Three critical AI technologies for corporate finance

In order to become a truly autonomous finance team, it is essential to simultaneously leverage three crucial AI technologies: computer vision (CV), natural language processing (NLP), and semantic analysis (sometimes referred to as semantic understanding). . This combination ensures that the system can understand structured and unstructured data, while continuing to learn from billions of transactions, data points and user feedback.

In recent years, advances in AI have improved computer vision technology to the extent that we can now easily read the text of receipts, even though they are barely legible like the ones you get from yellow taxis. . When auditing financial documents, deep learning-based resume templates run in the background to extract information, while state-of-the-art natural language processing techniques from various research institutions help us understand language. NLP is used in our daily life when we use virtual assistants like Siri and Alexa, but companies are starting to explore apps to speed up productivity. For example, natural language processing technology is used to transcribe conversations in real time, which can then be used to extract data, allowing AI to make decisions based on that information.

With semantic analysis, you are able to understand and relate to disparate extracted data such as dates, prices, discounts, payment terms and expense categories at the line level, eliminating thus the need for manual intervention to examine otherwise unknown or unclassifiable data elements. For example, let’s say you get an invoice from a coworker who invited a client to dinner a few nights ago. By taking advantage of the semantic classification to draw conclusions from the data, the system will be able to read and understand the receipt and that you ordered the filet mignon, which is a type of meat, which is a type of food, but also that it is something that can be an expense depending on company policy.

Autonomous AI is the engine of a real digital transformation

There is also an emphasis on automation with technologies like robotic process automation (RPA), which can easily handle repeatable tasks, handle structured data (only), and require a fair amount of human interaction. While RPA is a beneficial technology and works well with AI, corporate finance teams need something more that enables them to leverage big data (both structured and unstructured) to truly become autonomous, which can only be done with AI. With fairly high accuracy requirements in finance (e.g. compliance, audits, etc.), AI adoption has been somewhat difficult, but autonomous AI (and the three core AI technologies ) was the ultimate disruptor.

Being able to process invoices autonomously, from PDF to paper format, allows approvals and decisions to be made without the tedious manual review that historically took weeks to accomplish. Modern finance teams need stand-alone AI-powered solutions that do the heavy lifting and save tedious human scrutiny just for exceptions. Rather, your team can focus on issues that require resolution, investigation, or nuanced decision-making instead of sorting through mountains of expenses and bills. They can also spend more time doing what they do best: forecasting and supporting the long-term strategic financial goals and objectives of the business.

So, at the end of the day, our journey to truly harness the power of AI is inextricably linked with big data and the ability to have a platform that can understand it to enable organizations to make valuable business decisions, d ‘improve efficiency, savings and more.

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