By Paul Wnek, founder, CEO and principal solutions architect at Expand AP
A recent World Economic Forum survey showed 85 percent of financial services organizations were using some form of AI and 77 percent believe that AI will become an essential part of their business in the next two years. The finance industry is primed for AI integrations, which can automate repetitive work processes and conserve resources across a range of use cases, streamlining workflows with high accuracy and allowing workers to focus on more complex tasks. For example:
- Data aggregation and visualization can assist analysts in making informed decisions based on real-time data.
- Fraud prevention can be streamlined through AI-identity and document verification services.
- Backend office tasks that require significant time and data entry, such as accounts payable, may be offloaded to programs that can take care of expense reporting and budget tracking and show where the budget is in real-time.
To maximize AI’s potential, finance and accounting teams will need to adapt; this includes learning to use AI-powered software, adjusting workflows from data entry to data interpretation and building other essential skills to stay relevant in the workplace. Five essential skills that accounting professionals should prioritize are technical expertise, data analysis, quality control, communication and industry knowledge.
Technical Expertise
AI-powered software tools vary widely, but nearly all AI tools will employ some version of machine learning algorithms, natural language processing and predictive analytics. A general understanding of these AI components helps accounting and finance professionals to get the most out of their AI tool, enabling deeper insights and data-driven decision-making. For example, knowledge of machine learning algorithms enables finance professionals to develop and deploy models that can automate intake, enhance fraud detection and optimize financial forecasting.
Using AI’s ability to identify anomalies helps finance professionals to quickly detect fraud, flag costly errors or tag other outliers. Knowing how to structure prompts, select the best AI model and merge datasets is key to producing the most valuable insights and recognizing trends.
Tip: To jumpstart time-to-value for a new tool and shorten the time it takes for employees to acquire technical expertise, companies should select software that integrates with platforms they are already using.
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Data Analysis
AI improves the speed and efficiency of data collection and interpretation, providing insights that finance professionals can use to forecast, make decisions for their business, improve investment strategies, manage risks and reveal deeper insights into customer behaviors and preferences.
Statistical methods help finance professionals to identify trends, test hypotheses and draw meaningful conclusions, then develop financial forecasting models that predict market movements, assess investment risks and evaluate performance. Stakeholders prefer clear and actionable insights to spreadsheets and reports. Using AI outputs and their business acumen, finance professionals can extract valuable recommendations and communicate those recommendations to stakeholders in a meaningful way. This new process turns complex yet compelling data into an engaging story with concise, strategic and actionable steps that ultimately drive the company’s success.
Quality Control
Some AI functions offer their own version of quality control, while others require more oversight. For example, while AI is adept at accuracy when dealing with numbers and math, generative AI needs training and supervision as it learns to answer prompts and as the user learns which prompts to ask.
To practice quality control with AI platforms, finance professionals should first ensure that the data they are feeding into the model is correct and that it is formatted correctly for the AI algorithm, which helps to yield better results the first time. Next, validate the outputs a generative AI program creates, looking for discrepancies and adjusting prompts as needed. Effective prompt engineering is a key skill differentiator when using generative AI.
Beyond accuracy, there are ethical implications and regulatory requirements that finance teams should be aware of. AI tools must comply with relevant accounting standards and regulations, such as Generally Accepted Accounting Principles (GAAP) or International Finance Reporting Standards (IFRS). Finance professionals also need to be mindful of bias and data privacy issues when handling sensitive data. Data governance and risk management practices ensure that all data an AI tool collects is secure, that the organization has a clear understanding of the risks involved with using AI and that it is taking steps to manage those risks.
Communication
In addition to developing the skills to write prompts so that they deliver the desired output, finance professionals need to effectively explain complex concepts and data-driven insights to non-technical stakeholders. They must be adept at summarizing and presenting AI’s findings in a clear and concise way through data visualizations and reports.
Communication with peers and colleagues is also important for keeping projects on track. Project management and communication tools like Slack, Asana and Teams help facilitate collaboration.
Industry Knowledge
AI tools can be taught accounting and finance principles, but they lack the industry expertise that finance professionals develop with experience. While AI provides insights, the finance professional gives them meaning. In addition to following the latest industry trends, finance professionals should keep up with the latest in their industry and consider online training through the Association of International Certified Professional Accountants (AICPA) to identify process/system gaps.
As the accounting and finance industries embrace AI, professionals must also embrace new skills to deliver value and stay relevant in today’s changing landscape. With critical thinking backed by AI’s insights, finance professionals can establish themselves as valued advisors leading their companies toward greater performance and success.
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