By 2025, experts predict the total amount of data will reach an estimated 180 zettabytes. Your business, like many others, will be eager to invest in resources to understand that data for greater operational performance. At the heart of those future discussions are talks of how AI in finance will be an important part of your toolkit for gaining a competitive advantage within your market.
As a CFO, this guide will provide key context on the “why” behind AI in finance and some of its use cases for you and your FP&A team.
- Businesses have access to great amounts of financial and non-financial (operational) data that you must access to stay competitive in today’s commercial environment.
- CFOs and their FP&A teams are often the natural hub for this work because their pre-existing infrastructure traditionally emphasizes financial reporting.
- AI is a field of computer science dedicated to replicating human intelligence in programming and encompasses different subfields such as machine learning and natural language queries.
- The use case of AI in finance is endless, but we already see its value in supporting detailed forecasting, sifting through data sets, identifying usage trends and many other functions.
Why Will AI Be Essential for Optimizing the FP&A Functions of Your Business?
Two key issues are driving the need for AI in finance and the need for CFOs to become familiar with the abundance of AI tools at their disposal.
1. Modern Businesses Must Work With Large Amounts of Financial and Non-Financial Datasets
As our systems transition from analog resources to digital and electronic formats (cloud computing), an increasingly infinite pool of data can inform and guide your business. The historic limits of our traditional data systems prevented companies from obtaining certain details useful for financial and operational planning.
You likely relied on manual data entry into Excel or other systems, which forced companies to prioritize certain data that you would consider essential for future planning (i.e., mostly financial data necessary for reporting on things such as cash flow and profit/loss statements).
Thanks to so much of our business happening online and via electronic systems, we can capture endless amounts of metadata and other non-financial metrics that provide essential context to your financial reports.
The problem for companies now, however, is again being able to use this data volume and diversity (this time due to the human limits of reviewing and making sense of it.) The widely accepted solution for this constraint on human capital is using AI to provide the necessary support.
AI can already do incredible things for reading, processing and sharing your data in helpful ways, but as this technology matures, the use cases will only continue to grow.
2. Finance Groups Are Increasingly Responsible for Leading Strategic Functions
For CFOs, the bulk of this data analysis is falling upon the shoulders of your FP&A team or finance department, creating a further need for AI tools to reduce the burden of manual tasks. It makes sense that finance groups are the natural hubs for general company performance data because their core function already focuses on reporting and analysis, just with an emphasis on finance.
Regardless of your business size or the size of your finance team, AI will likely provide some utility that makes the most of your available resources.
What Do We Mean When We Talk About AI in Finance?
Generally, AI refers to the modeling of computer programming and machine systems to replicate the capabilities of natural human intelligence, such as problem solving, decision making and the ability to perceive (visual or audio comprehension). Particularly, you may think of two important subsets of AI that already have applications in finance: machine learning and natural language querying.
Machine Learning (ML)
Machine learning (ML) refers to statistical methods and algorithms that allow computers to classify data, discover trends and insights, and make other uses of data mining. In other words, machine learning relies on the vast amounts of data accessible to the program and uses that experience to achieve a designed outcome.
Natural Language Querying (NLQ)
Natural language querying (NLQ), sometimes called natural language processing (NLP), is a type of AI that allows computers to comprehend human speech and provide a natural response. You can think of NLQ capabilities as the bridge that allows us to quickly access the information your system has because of the work from its ML and other AI tools.
Source: Gartner (2020)
Some Use Cases and Examples of AI in Finance
Below are some of the many ways AI is already paying dividends for those CFOs and business data leaders who integrate it within their FP&A process:
- Translation of PDF files that hold data from tables, financial statements and other items into user-friendly Excel spreadsheets for further analysis
- The ability to tackle tough questions during an earnings call or other executive meeting through NLQ tools that provide almost immediate charts, graphs and other visual representations of queries (e.g., what territory had the best sales for X product?)
- Stronger forecasting abilities thanks to ML tools such as Power BI and Excel Ideas that can discover new insights from data sets that your data scientists or FP&A team may struggle to find
- The ability to link your financial and operational datasets to identify granular trends, data anomalies, patterns and distinguish correlation from causation. An easy example is data analysis on purchases of different products that AI can use to provide more beneficial customer recommendations on selling platforms
The Future of AI in Finance
It’s only the beginning for AI in finance, but now is the time to learn and adopt. Integrating AI tools and our FP&A software solutions is at the heart of our company’s mission to help your company Plan To Grow™.