In this special report, Kunal Verma, CTO and co-founder of AppZen, discusses how technologies such as RPA and AI are experiencing watershed moments as business leaders realize they are no longer nice to have, but are an essential tool for staying ahead on 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 his 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 important drivers of digital transformation, especially given the events of the past year. When you look at the numberstechnologies such as RPA and AI are experiencing breakout moments as business leaders realize they are no longer nice to have, but are an essential tool for staying ahead of the game. competition.
AI helps organizations perform tasks that were previously difficult or, in some cases, impossible to perform efficiently, effectively and accurately by leveraging valuable insights from large amounts of structured and unstructured data. And it’s thanks to this “big data” that the democratization of data has become a reality, a reality in which 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 the data is telling you, because the logic of how you use the data is independent of the data generation process. 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 comes in – good results come out.
This approach allows for cross-team functionality and ease of use when interacting with other parts of the business through things like data visualization and dashboardswhich can be easily shared and understood by the C suite all the way.
Although several companies are already leveraging automation, nowhere is AI currently having the greatest impact than in enterprise financial services, disrupt the operation and work of finance teams– both inside and outside the organization. Finance teams have always been plagued by manual processes, human oversight, as well as 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, from expense reports and invoices to packing slips and receipts.
Three crucial AI technologies for corporate finance
To become a real autonomous finances team, it is essential to leverage three crucial AI technologies simultaneously: computer vision (CV), natural language processing (NLP), and semantic analysis (sometimes called semantic understanding). This combination ensures that the system can understand both 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 in that we can now easily read the text of receipts, even if they are barely legible like the ones you get from yellow cabs. When auditing financial documents, deep learning-based resume templates work behind the scenes to extract insights, all while being state-of-the-art. natural language processing techniques from various research institutions help us to understand the language. NLP is used in our daily lives 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 this information.
With Semantic analysis, you are able to understand and establish relationships between disparate extracted data such as dates, prices, discounts, payment terms and expense categories at the line level, eliminating the need for manual intervention to review otherwise unknown or unclassifiable data elements. For example, suppose you receive an invoice from a colleague who invited a client to dinner a few days ago. By leveraging semantic classification to draw conclusions from the data, the system will be able to read and understand the receipt and what you ordered. filet mignon, which is a type of meat, which is a type of food, but also that it’s something that can be an expense depending on company policy.
Autonomous AI is driving true 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 allows them to leverage big data (both structured and unstructured) to become truly empowered, something that can only be done with AI. Because accuracy requirements are quite high in finance (e.g. compliance, audits, etc.), AI adoption has been somewhat challenging, but autonomous AI (and the three AI technologies base) was the ultimate disruptor.
Being able to process invoices autonomously from PDF to paper format enables approvals and decisions to be made without the tedious manual manual review that previously took weeks to accomplish. Modern finance teams need autonomous AI-powered solutions that do the heavy lifting and save time on human review just for exceptions. Instead, your team can focus their attention on issues that require resolution, investigation, or nuanced decision-making instead of sifting through mountains of expenses and bills. They can also devote more time to what they do best: planning and supporting the company’s long-term strategic financial goals and objectives.
So, ultimately, our journey to truly harness the power of AI is inextricably linked to Big Data and the ability to have a platform that can understand it to enable organizations to make valuable business decisions. , improve efficiency, cost savings and Suite.
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