From chatbots to facial recognition, artificial intelligence (AI) is already a part of our daily lives. As it becomes more sophisticated, AI offers incredible potential for Finance departments: it can help automate manual tasks, support faster reporting, and analyze data at scale, making Finance teams more predictive than reactive.
Despite the potential, most Finance departments remain in the early-adoption phase, if they’ve approached implementing it at all.
To learn more about the state of AI in Finance as well as the drivers and roadblocks toward greater usage across the field, we talked to Theodora (Theo) Lau. Theo is the founder of Unconventional Ventures, which seeks to transform the banking industry to better address the needs of all consumers. Between her consulting, thought leadership, and speaking engagements, she’s well-equipped to answer our burning questions about AI.
AI in Finance Today
If you’ve done any sort of personal or business banking in the last few years, you’ve probably noticed more AI-supported processes, many of which are being integrated into Finance functions too. Virtual assistants and algorithms to detect fraud are a few of the more common applications.
Finance departments are also using AI to put statements together or to pre-fill forms with data that employees can review. However, “AI can do much more than that,” says Theo. “But the easier use cases will be the tasks you can easily automate – let’s get the lower hanging fruit.”
Shoring up security is one area of untapped potential, especially with the increasing incidences of ransomware attacks. While tight security is vital across the enterprise, it’s especially crucial for the Finance function as cyber-attacks get more sophisticated. “That’s where AI can do a lot – it can go through event logs and transactions much faster” to spot anomalies and help prevent breaches.
The Challenges of Implementing AI in Finance
What’s stopping Finance departments from taking AI to the next level? In part, it’s the time and effort it requires to implement. “It’s not like an OS, where you install it and off you go,” says Theo. Data may be fragmented across legacy systems and in organizational silos, making integrating AI a task that requires a lot of thought and planning.
As with all technology, there’s also the danger of buying into the hype and integrating AI just because it’s the thing to do – even if it’s not actually helping the Finance team be more efficient and productive. “Are you doing it because you see a real business need, versus because you see all your competitors doing it?” asks Theo. It’s important to consider where AI can provide the most value, and then carefully map out its functions and impact across the team.
A good starting point: ask what problem the team is trying to solve and who it is being solved for. “Those two questions will be your guideposts and mandate why you invest in technology,” says Theo.
Maintain Transparency and Humanity
While many think of AI as a “black box” that simply ingests data and spits out answers, this is a misconception. “Especially in Finance, humans still need to be in the loop,” says Theo. Finance departments need to embrace and enforce transparency around how the technology works, including being able to explain the models it uses, ensuring it is compliant with applicable laws and regulations, and validating the results.
It’s also important to remember, and to reassure employees, that using automation to handle mundane or repeatable tasks doesn’t necessarily mean that people will be replaced. AI can help increase operational productivity by freeing employees to focus on more critical and creative tasks. “It just means that what you’re doing might be different,” Theo emphasizes.
Looking Ahead to the Near Future
AI may be instrumental in helping Finance departments evolve by allowing team members to learn higher-level skills and take on new responsibilities, resulting in greater output per person. This is a more efficient approach to operational growth and productivity than simply adding employees. “Another way to do it is to get more output per person, and to enable people to work longer into the future,” Theo says.
Ultimately, the insights from AI-supported data analysis in the back offices will especially help small- and medium-sized businesses make decisions faster and more easily. Automating functions like risk assessment – such as deciding which loans to underwrite – can improve efficiency and allow smaller Finance departments to scale quickly without the need to find, hire, and train large numbers of new employees.
Don’t Delay on AI
Finance departments should start integrating AI now to maintain a competitive edge in the marketplace and keep up with consumer expectations. The resulting increases in efficiency and productivity, as well as the insights that data can bring to reporting and forecasting, will put the team in a powerful position to help guide your organization to success.