It’s nearly impossible to exist in the business world without hearing terms like “artificial intelligence,” “AI” or “machine learning.” For businesses and finance professionals called upon to use these technologies, their unfamiliarity with the tech can impede trust and ultimately lead to its rejection.
However, AI offers very real benefits in financial analysis and innovation that go overlooked as a result. By demystifying the concept of AI, it can go from a buzzword to a strategic business partner so that all businesses can build a strategy around it.
What these buzzwords really mean
Machine learning is a subset of AI where inputs are given to an algorithm that then produces an output. Many different techniques exist to produce these outputs, but for now, think of them as tools to solve complex problems that humans cannot easily solve alone with such large amounts of data.
To become more accurate and useful, these algorithms must be trained. Algorithms generally fall into two categories: supervised or unsupervised. In a supervised algorithm, a human tells it what to predict before it trains, while an unsupervised algorithm is given inputs with no instruction on what to predict. From there, it starts its training. The algorithm will look at past data from a large data set to detect patterns using techniques that vary by algorithm.
For businesses, these patterns ultimately lead to insights that finance can use and analyze to drive decisions.
Evolving skills to build trust
Machine learning can uncover hidden patterns in data that might have previously taken hours. For example, a forensic accountant may need days or weeks to review numerous documents and data to uncover fraud. Now, machine learning techniques can analyze this data in mere seconds to a few hours.
To be most effective in this new paradigm, forensic accountants need both old and new skills. They should already have domain expertise — a key skill required in data science that helps integrate disparate data sources. A second and possibly new skill is a basic understanding of statistics. This, paired with basic training in AI, can lead to a better understanding of — and trust in — how the algorithm makes its decisions.
What AI in finance is not
Many misconceptions exist due to AI becoming more of a buzzword than an actual science and discipline. These most common misconceptions keep businesses in the dark on AI’s potential benefits:
- AI is a luxury: Your competition is likely already using — or planning for — AI in its business decision making.
- AI and ML are the same thing: Although used interchangeably, AI is an umbrella term, which includes machine learning and other intelligent technology, like natural language processing.
- Machines learn on their own: The algorithms must be trained on quality data fed to them by humans.
- AI is objective: Inaccurate or biased data leads to biased outcomes, which is why AI is not always objective.
- AI will replace only mundane tasks: AI has shown it can assist humans in eliminating mundane tasks and elevating human decision making in more complex functions.
- Businesses don’t need an AI strategy: Every business should identify how AI will impact its work, and finance and accounting professionals should understand how to leverage these technologies themselves.
AI is already embedded in business technology with far-reaching impacts. Rather than view AI as a threat or luxury, finance teams can build trust through greater literacy and collaboration in its integration.
AI implementation challenges
While every business should have an AI strategy, maximizing ROI requires being prepared for its challenges. Many finance and accounting professionals who have attempted to adopt AI have seen failures or underperformance. Having proper understanding of AI helps set expectations of performance upfront.
Understanding AI is not just the responsibility of data scientists or experts, and over-reliance on these people can set businesses up for failure. Although AI expertise is certainly required for success, too often, accounting or finance professionals see it as outside their responsibility to participate in the building of the algorithms. Without adding the specific domain knowledge, context and expertise of an accounting or finance professional, the result has the potential to miss expectations.
Finally, businesses should be aware of the “black-box” problem. Many professionals see these algorithms as a black box that’s unexplainable, leading to poor adoption or distrust. These technologies do have limitations, and it’s important for human judgment and transparency to be built into the process of using them.
The key to AI adoption
Even basic education in data science and AI can help build trust and understanding in AI models. Online learning services, books and other resources can help professionals further demystify AI and grasp its benefits and limitations so that businesses have a greater chance of successfully implementing and testing it.