Artificial Intelligence (AI) has been an endless topic of discussion since OpenAI released ChaptGPT in November 2022. By February 2023, a short three months since its release, ChatGPT reached 100 million users, a growth rate far exceeding that of TikTok, Facebook, and even Google when they were first launched.
Conversations around AI have ranged from the altruistic – how AI can improve the lives of people around the world, to the personal impact – how will this affect my job and the activities of my day-to-day life. And of course the potential negative implications – will AI pose an existential threat to humanity.
This article will first review what AI and Machine Learning (ML) is and how AI Predictive Analytics enables better decision-making.
AI and Machine Learning
AI is a broad category of artificial intelligence that includes many artificial systems and categories. Fundamentally, AI is the idea that a computer can imitate and go beyond human intelligence and capabilities and automatically perform tasks without human involvement. Machine Learning (ML), on the other hand, is a subset of AI and uses algorithms to recognize patterns, learn insights, and apply this knowledge to make progressively better decisions. Examples of machine learning include, but are not limited to, facial recognition, online product recommendations, and predictive text.
AI Predictive Analytics – Enabling Better Decision-Making
Predictive Analytics attempts to predict and forecast future outcomes by mining large data sets, ML, and statistical modeling. Businesses use predictive analytics in finance to gain insights through identifying patterns in the data to exploit potential opportunities and identify potential risks that need to be managed or further mitigated.
Using predictive analytics models, FP&A professionals can model scenarios and the resulting impact on corporate budgeting, cash flow, expenses, realized risks, gross margin, net profit, and so on. Ultimately, ML predictive analytics allows businesses and finance leaders to predict decision outcomes using pattern assessments and statistical and regression techniques.
With a clearer view of future outcomes, FP&A professionals can be confident they are making optimal decisions with the best information at hand.
Below are four examples of critical finance functions AI-enabled predictive analytics can make more efficient, accurate, and valuable:
1) Financial Statement Analysis
Financial statement analysis involves analyzing a company’s financial statements (Income Statement, Cash Flow Statement, Balance Sheet, and Statement of Shareholders’ Equity) to gain insight into the company’s financial health over a period of time. This is typically done through a horizontal, vertical, and ratio analysis.
FP&A Analysts would typically manually input financial data from the financial statements into spreadsheets that would calculate trends, relationships between accounts, and important financial ratios. However, this manual process is labor intensive and can result in human error.
AI programs allow FP&A analysts to simply upload the financial statements to the AI FP&A platform, and it will calculate the desired outputs in a fraction of the time with greater accuracy than if it was manually completed.
2) More Realistic Forecasts
Forecasting is a major finance and accounting function required to build budgets, determine cash and capital requirements, and support business decisions. Standard forecasting is labor-intensive and typically requires input from multiple levels of an organization, making it time-consuming, often less accurate or unrealistic, and prone to human error. Further, standard financial forecasting doesn’t typically consider other non-financial information that will influence the forecast, such as inventory levels, supply-chain data, weather, geographic region, etc.
AI applications can aggregate, analyze, and derive valuable insights from financial and non-financial data more accurately and at a far greater speed than a human could do. It can find relationships between data sets of seemingly unrelated information to help narrow the main drivers behind certain numbers and use statistical methods to predict outcomes for various scenarios.
3) Liquidity and Working Capital Management
Liquidity and working capital management are critical financial management functions of any business. If these functions aren’t managed at the level of effort they typically require, it won’t be long before a business finds itself without the cash needed to run its day-to-day operations. Over time, this can lead to insolvency and, eventually, bankruptcy.
AI can provide financial managers insights learned through collecting and analyzing large data sets to better understand their current and future cash positions. AI machine learning can determine the timing of the average collection periods from clients, average payments to suppliers, revenues, and related expenditures for each season and geographic region to provide insight into what cash can be expected and when. With this information in hand, financial managers can determine if external financing is required or even what to do with excess cash that would otherwise just sit in a low-interest-bearing account.
4) Mergers and Acquisitions (M&A) Due Diligence
When businesses are looking to invest in or purchase an existing business, they will have their FP&A professionals assess the financial information initially provided in what’s commonly called a Confidential Information Memorandum (CIM) or Offering Memorandum (IM). As part of the initial financial assessment, FP&A analysts will need to analyze the current financial performance of the business, which includes, but is not limited to:
- Comparing the company’s financial results to prior periods to determine the growth rate and identify any trends.
- Determine a reasonable basis to forecast future financial performance.
- Compare the current and projected financial results to private and public benchmarks to evaluate the business’s performance to its market peers.
The noted points above are not an exhaustive list of initial M&A transaction financial assessment activities when evaluating a transaction; however, these noted activities alone can take hundreds if not thousands of hours to complete, and this is only the initial evaluation. If both parties agree on a deal, the financial due-diligence activities are orders of magnitude greater than the financial assessment stage and often involve third-party lawyers, accountants, and other consultants to aid in the due diligence activities and close the deal.
AI can assist in nearly all aspects of due-diligence activities, including M&A. It can compile and analyze vast amounts of financial transaction information that was used to build the financial statements provided. The AI program could confirm the financial statements are accurate and provide valuable insights in a fraction of the time it would take a team of finance professionals to complete. This would include the three topics covered earlier in the article:
- Financial Statement Analysis: Vertical, horizontal, and financial ratio analysis and compare this to internal and market benchmarks.
- More Realistic Forecasts: Using both large sums of both financial and non-financial data to predict a more realistic forecast.
- Liquidity and Working Capital Management: Determine data-backed assumptions for cash inflows, and outflows, and predict when the business will need external financing or will have excess cash.
Below are two further examples of where AI can make due-diligence activities more efficient, accurate, and valuable.
Contract Management – Definitive Purchase Agreement
AI can assist businesses in reviewing and analyzing the final agreement between themselves and the buying or selling entity, which is commonly referred to as a Definitive Purchase Agreement or Stock Purchase Agreement. These contracts can be extensive, with hundreds of pages that need to be read and understood. These extensive contracts aren’t simply reviewed and worked by the finance team and legal counsel. With contract provisions affecting different areas of a business, numerous business area leaders will also need to review and provide insight into the practical implications of the terms and conditions of the agreements for the finance team to consider.
The level of effort across an organization to provide this review and analysis is extensive, which can be made more efficient with AI. An AI application can, in part, identify important clauses, obligations, and risks and compare this to internal standards, best practices, and benchmarks within the market. With the critical areas of the contacts identified and summarized by an AI, finance, and business leaders can spend more time on higher value activities such as strategic implications of the pending transaction.
Contract Management – Major Agreements
Another due-diligence activity is to review the material contracts a business is a party to. Whether the deal is to purchase, merge with, or invest in a business, it’s critically important to understand the contractual obligations of the business. These major agreements may result in risks that need to be managed, or they may offer opportunities to be realized. Depending on the business size, they could be a party to numerous material agreements that must be reviewed.
Similar to Definitive Purchase Agreements noted above, AI can make the review and analysis much more efficient by identifying important clauses, obligations, and risks. It can then compare this to both internal standards and best practices, as well as benchmarks within the market.
Conclusion
There is little debate on AI’s impact on all aspects of business, including finance. The only question is how fast AI will develop to meet the needs of businesses and the speed at which businesses implement and adapt to AI. Finance and accounting functions within the business, and firms that provide financial services to these businesses, have historically been open to implementing new technology. Even laggards in the industry seem to eventually adopt technology when its value proposition is clear, and they don’t want to be left behind. One thing is clear for finance and accounting professionals, AI is here to stay, and early adopters will gain a material advantage over their competitors that will only grow over time.