If you want to sum up work within the finance function in 2023, you could probably do it in just two letters: AI. Early this year, artificial intelligence, automation, and machine learning grabbed headlines and vied for attention. Just two months after OpenAI introduced ChatGPT to the press and public, it became the fastest-growing consumer software application ever, blowing past 100 million users in January — and leaders raced to invest in AI, implement new tools, and upskill employees to stay ahead of it all.
How AI Is Transforming the Finance Industry
For the finance sector, where process optimization and data-driven decision-making initiatives have topped priority lists for decades, AI is much more than a buzzword; it's a game-changer. The year's advancements in AI technology and tools represent a leap forward for everything from routine process automation to predictive analytics for strategic FP&A.
In 2023, accounting departments and financial institutions everywhere deployed AI solutions to do everything from reducing operational costs to making impactful business decisions. Leveraging AI's capabilities has been transformative, turning accounting professionals into amateur data scientists and transforming some key areas of the finance function.
For finance teams and accounting departments everywhere, understanding and adopting AI can mean streamlined operations, a renewed focus on high-value accounting tasks, and finance professionals empowered to drive strategy forward — all at significant cost savings. Robotic Process Automation (RPA) is already at the indisputable forefront of these transformations.
The ability to automate manual tasks that are tedious, time-consuming, and error-prone has been a boon to finance teams already. RPA streamlines repetitive, rule-based tasks with little or no value to add, like data entry and extraction, invoice matching, or exception handling, reducing inaccuracies, cost, and time spent on drudgery.
2023 introduced the concept of Machine Learning, or ML, to many finance teams. A subset of AI, ML enables systems to learn and improve their performance over time. This is particularly useful in predictive analytics, where algorithms can accurately analyze historical data to forecast future financial trends.
Natural Language Processing (NLP) is another powerful AI tool making waves in finance. NLP allows machines to understand and interpret human language, facilitating tasks such as sentiment analysis of financial news, contract reviews, and customer interactions. This not only enhances communication but also aids in making informed decisions based on textual data. For instance, by applying NLP to the transcript of an earnings call, finance leaders can perform sentiment analysis and identify key themes within the transcript to discover strategically important subject matter.
Generative AI, or GenAI, entered the lexicon in tandem with ChatGPT's meteoric rise. GenAI models can create entirely new content, from uncannily conversational chatbot responses to writing executable code. Language models (like ChatGPT) use NLP techniques to understand language structure and patterns and are trained on massive datasets. All have their place in the finance function, and each has played a role in transforming it this year.
The Benefits of Artificial Intelligence in Finance
AI's importance to the finance function lies in its transformative capabilities. It's revolutionizing efficiency, accuracy, and insights, and the strategy access to those insights empowers. Finance leaders and their teams can tap into those capabilities and the near-endless potential use cases they represent. Fraud patterns identified in seconds. In-depth reports generated automatically and accurately. Investment opportunities tailored to an organization's unique risk or resource profile.
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Reduced Costs and Increased Efficiency: AI can automate many repetitive tasks in the finance function, such as data entry, reconciliation, and invoice processing. This frees up human resources for more strategic work and can lead to significant cost savings.
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Automated Financial Reporting and Analysis: AI can generate financial reports and perform complex financial analyses much faster and more accurately than humans, reducing the time between access to information and taking action to drive strategy forward.
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Improved Data-Driven Decision Making: AI can analyze vast amounts of financial data to identify trends and patterns that would be difficult or impossible for humans to see. This allows finance teams to make data-driven decisions that are more likely to be successful.
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Enhanced Customer Service and Engagement: AI can personalize customer experiences and provide 24/7 support. For example, chatbots can answer customer questions about their accounts, and AI-powered recommendation engines can suggest financial products and services tailored to individual needs.
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Greater Transparency and Auditability: AI can track and monitor financial transactions in real-time, improving transparency and making it easier to detect fraud. AI can also automate the audit process, making it faster and more efficient.
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Innovation and Development: AI can be used to develop new financial products and services that are more tailored to the needs of individual customers. For example, AI can be used to create personalized investment portfolios or to develop new insurance products.
How AI is Being Used in the Finance Function: 3 Real World Examples
Enhanced Fraud Detection and Prevention: AI-powered systems can perform real-time analysis on vast amounts of data, clocking patterns and sniffing out anomalies that might point to potential fraud. This summer, Google Cloud launched an anti-money laundering (AML) AI that uses machine learning to help global financial institutions identify and address the $2 trillion problem better than legacy technology. Rather than rely on rules-based automation to flag potential laundered transactions for manual review (at a 95 percent false positive rate, no less), Google's AML AI adapts its risk-scoring model as it operates for precision, accuracy, and efficacy.
Streamlined Regulatory Compliance: Financial regulations are complex and in constant flux, which makes complying with them a time-consuming process prone to errors and, potentially, steep penalties. Risk assessment and reporting are both ripe for RPA, and we're starting to see AI tools that integrate with existing governance, risk management and compliance (GRC) software or act as standalone platforms. That includes Avery, a tool released back in October by RegVerse, which includes — among other things — a repository that aggregates state and federal regulatory sources that can map compliance gaps within an organization's finance function and automatically identify risk, control, and audit deficiencies.
Improved Customer Experience and Personalization: AI within the finance function isn't limited to back office and team-facing tasks. AI can personalize financial products and services to individual customers based on their financial goals, risk tolerance, and other factors. Intelligent document processing (IDP) is hardly a new concept, but the rise of generative AI has allowed software companies like Kofax to leverage large language model (LLM) capabilities to include content summarization, localization, and generation — including data models — within document analysis processes.
What CFOs Should Know About Incorporating AI Into Finance Function Workflows
For finance leaders, determining what to address first and where to begin with AI and machine learning is like trying to drink from a firehose. AI holds a lot of promise — packaged with a lot of hype. It can be dizzying, especially when so many conversations on the topic skip straight to a utopian vision of a well-matured AI-powered finance function and spend much less time on "little" details. For instance, the complexity of the infrastructure cloud platforms must be maintained to offer the products that speed that maturation, the costs associated with that, and the challenge of realizing the ROI those costs necessitate. And that's just one high-level challenge.
Dig a little deeper, and there's more to address like the resources upskilling finance professionals with access to AI innovations will require or how to ensure data quality at the outset to limit any potential negative impacts to working capital. For many CFOs and companies, developing an AI strategy wasn't top of mind until this year, to say nothing of how to procure the resources, talent, and expertise to execute such a strategy, all while considering the ethical implications of moving forward.
CFOs who wish to create value through cost savings and improved efficiency gain a strategic advantage by empowering their teams to focus on high-level accounting tasks or hone a competitive edge with AI for financial planning and analysis will benefit from AI implementation. They will also find familiar challenges, like change management concerns, talent resource gaps, and reliable, high-quality data availability.
More: 52 Statistics That Show How Much Accounting Has Changed In the 21st Century (So Far)
The upshot is that while AI is a white-hot topic right now, its considerations are not entirely novel. The technology is unprecedented. The approach is not. By fostering a culture of communication and innovation, building a solid infrastructure, monitoring performance, and leveraging the expertise of expert partners, CFOs can realize the most benefit and maximize AI's impact on finance.
Getting Started with Artificial Intelligence in Finance
Adopting AI as a strategic driver in the finance function with the right approach doesn't have to be the daunting task described above. Here's how to break it down to set your team up for success.
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Assess Your Needs: Identify specific areas in your finance function that could benefit from automation and AI. Whether automating invoice processing or enhancing risk management, a targeted approach yields the best results. Choose a use case that's easy to pilot.
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Invest in the Right Technology: Choose AI solutions that align with your organization's goals and requirements. You'll need to consider factors like scalability, integration capabilities, and vendor support when you're shopping around.
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Provide Upskilling Opportunities: Equip your finance team with the necessary skills to leverage AI tools effectively. Training programs and workshops can bridge the knowledge gap and smooth the transition.
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Start Small: Implement AI in phases, starting with less complex tasks. This allows your team to adapt gradually and gain confidence in using AI tools.
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Monitor and Evaluate: Regularly assess the performance of AI applications and adjust your approach when you see the opportunity to improve. Continuous monitoring ensures that the technology aligns with evolving business needs.
Finally, it's important to be realistic about its capabilities to fully realize the results you envision for AI in the finance function. For all the potential they possess, they cannot and should not take the place of human expertise. After all, AI is only as good as its inputs, whether that includes the datasets used to train the model or the expertise you leverage to unlock the results you want.
