How AI is Changing the World of Corporate Finance and Accounting

As it is in many other industries, AI is making a profound impact on corporate finance.

Just a few short years ago, most wouldn’t have guessed the scope of this impact. 

However, since the release of ChaptGPT in November 2022, AI’s transformative influence has become increasingly apparent. 

By February 2023, a short three months after its release, ChatGPT had reached 100 million users. This broke the record for the fastest-growing user base, exceeding the user bases of TikTok, Facebook, and Google when they were first launched. 

In August 2024, it had 180.5 million users, and this number is still growing daily. 

Conversations around AI have ranged from the altruistic – how AI can improve the lives of people around the world, to the personal impact – how this will 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 AI and Machine Learning (ML) and how AI in predictive analytics can facilitate effective decision-making in corporate finance.

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: 

  • Facial recognition
  • Online product recommendations
  • 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. 

Corporate finance teams rely on predictive analytics in finance to collect insights from data patterns. They use these insights to exploit opportunities and identify risks to manage or further mitigate.

With predictive analytics models, FP&A professionals can model scenarios and the resulting impact on: 

Ultimately, ML predictive analytics allows businesses and finance leaders to predict decision outcomes. This is done with pattern assessments and statistical and regression techniques.

With a clearer view of future outcomes, FP&A professionals can rest assured their decisions are based on the best information available. 

Uses for AI in Corporate Finance

Below are four examples of critical corporate finance functions AI-enabled predictive analytics can make more efficient, accurate, and valuable: 

1) Financial Statement Analysis

Financial statement analysis involves the analysis of a company’s four primary financial statements to glean insight into its financial health over time:  

This is typically done through horizontal, vertical, and ratio analysis. 

FP&A Analysts typically manually input financial data from the financial statements into spreadsheets. These spreadsheets help them calculate trends, account relationships, 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 financial statements to the AI FP&A platform. Then, the platform calculates the desired outputs in a fraction of the time and with greater accuracy than if the task were manually completed. 

2) More Realistic Forecasts

Forecasting is a major function of finance and accounting.

It is integral when: 

Standard forecasting is labor-intensive and typically requires input from multiple levels of an organization. This makes it time-consuming, often less accurate or unrealistic, and more error-prone.

Further, standard financial forecasting doesn’t typically consider other non-financial information influencing the forecast, such as inventory levels, supply-chain data, weather, and geographic regions.

AI applications can more accurately aggregate, analyze, and derive valuable insights from financial and non-financial data. Again, they can do so at a far greater speed than a human could.

AI can also find relationships between seemingly unrelated data sets. This narrows down the main drivers behind certain numbers and uses statistical methods to predict outcomes for different 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 with the level of effort they typically require, a business will soon be without the cash needed to run its day-to-day operations. 

Over time, this leads to insolvency and, eventually, bankruptcy.

AI can provide financial managers with insights, which can be learned by 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, and revenues. 

It can also use expenditures for each season and geographic region to provide insight into what cash can be expected and when. 

With this information, corporate finance teams can determine whether external financing is required or what to do with excess cash that would otherwise be in a low-interest-bearing account. 

4) Mergers and Acquisitions (M&A) Due Diligence 

When businesses plan to invest in or purchase an existing business, they have their corporate finance teams assess the financial information. 

This information is initially provided in what’s commonly called a Confidential Information Memorandum (CIM) or Offering Memorandum (IM).

FP&A analysts scrutinize the business’ current financial position as part of the initial financial assessment.

This analysis includes: 

  • Comparing the company’s financial results to previous 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 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. 

Due Diligence

If both parties agree on a deal, the financial due diligence activities are orders of magnitude greater than the financial assessment stage. 

They often involve third-party lawyers, accountants, and other consultants to assist with 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 used to build the financial statements provided. 

Artificial intelligence programs could confirm accurate financial statements and provide valuable insights almost instantaneously. 

This would include the three topics covered earlier in the article:

  • Financial Statement Analysis: Perform vertical, horizontal, and financial ratio analyses and compare the results to internal and market benchmarks.
  • More Realistic Forecasts: Using 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. Predict when the business will need external financing or have excess cash.

Below are two further use cases 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. This is commonly called a Definitive Purchase Agreement or Stock Purchase Agreement. 

These contracts are immense, with hundreds of pages to read and understand. These extensive contracts aren’t simply reviewed and worked by the finance team and legal counsel. 

Leaders from corresponding teams will review the impact of contract provisions on several business areas and provide insight into the practical implications. 

These insights into the terms and conditions of the agreements are vital for the finance team to consider.

The effort required across an organization to provide this review and analysis is extensive, and AI can make this process more efficient. 

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 AI identifying and summarizing the critical areas of the contracts, finance and business leaders can devote more time to higher-value activities, including the 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 offer opportunities to be realized. Depending on the business size, it could be a party to numerous material agreements that must be reviewed.

Similar to definitive purchase agreements noted above, AI streamlines review and analysis by identifying important clauses, obligations, and risks. 

It can then compare this information to internal standards, best practices, and market benchmarks.

Will AI Replace Human Roles in Corporate Finance?

Considering the many uses of artificial intelligence in corporate finance, many are understandably concerned about what this means for the human workers who traditionally perform those tasks.

Fortunately, AI will likely complement manual work in corporate finance disciplines rather than replace it.

Here’s how:

  • Accounting: Automating transactions and improving cash flow management to free up humans for strategic and compliance roles.
  • Data management: AI can process data much faster and more accurately and can be used to eliminate a lot of human input. However, it still requires human supervision to make strategic decisions about its use.
  • Planning and strategy: AI improves forecasting and risk management, but humans are necessary for strategic thinking and scenarios.
  • Controlling: AI detects discrepancies in data and monitors real-time performance while humans perform advanced analytics and corrective action. 
  • Analysis: Computers rapidly synthesize and model data, but humans think critically and implement AI’s recommendations.

Conclusion: What’s Next for AI in Corporate Finance?

There is little debate on AI’s impact on all aspects of business, including corporate 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.
If current projections are correct, this will happen rapidly: 

  • ​​By 2025, the global AI in the financial market is expected to reach $26.67 billion.
  • 69% of banks already use AI for data analysis and customer service.
  • Over 40% of financial institutions plan to increase AI investments in the next two years.
  • 83% of financial institutions anticipate AI will create new roles within their organizations in the next three years.

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 industry laggards seem to eventually adopt technology when the value proposition is clear, and they don’t want to be left behind. 

If you’re interested in exploring the possibilities and power of AI in corporate finance, book a Datarails demo today. 

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