AI In Financial Reporting

Artificial intelligence and AI tools are revolutionizing industries, including the financial industry. Every day, it seems as if a new AI tool is announced that promises to streamline and simplify, so business can move at a faster pace. While technologies are still in development, there are clear benefits to using AI in the finance industry. Organizations that understand the power of AI for financial reporting, including the outlook for the future, can identify and implement the right solutions.

How AI Can Improve the Financial Reporting Process

Before we discuss the future of AI in financial reporting, consider the underlying reasons that drive companies to consider financial reporting automation.

The pace of information moves quickly. Companies are tasked to do more, whether it's reporting on ESG, making necessary SEC filings or keeping up with ever-changing regulations. These tasks require a significant output of labor from employees. Workers must source data, vet it for accuracy, fit the right data into the report, check for errors, keep track of deadlines and rule changes — just to name a few of the steps necessary for reporting. The traditional financial reporting processes are labor-intensive, time-consuming and expensive. No wonder that companies are interested in solutions promising to streamline the process, such as AI tools.

Generative AI prompts for finance are making financial reporting faster and more effective by automating numerous routine tasks, enabling deeper data analysis and improving the overall decision-making process. According to recent reports, roughly 97% of leaders within the financial reporting space plan to make greater use of generative AI within the next three years. With human experts overseeing the process to mitigate inaccuracies, this technology can help with drafting reports, identifying risks and enhancing the accuracy of data.

Benefits of using AI in corporate finance include:

• Automation: Financial reporting automation allows workers to set rules and processes that will be run on automation on a predetermined schedule. This is quicker and more error-free than relying on a team of people to perform the same process.
• Improved quality and compliance: Since AI can be trained on industry-specific regulations, compliance is built into the system. The end result of an AI-automated process is data with fewer errors.
• Enhanced data analysis: AI has a distinct advantage over people when it comes to analyzing patterns, spotting trends and making decisions based on predictive analysis. To understand why, consider how fast a machine can parse a 1,000-row spreadsheet versus an employee. The machine can search the data, pick out patterns and analyze while the human is still filtering information. In the fast-moving corporate landscape, enhanced speed and accuracy are critical.
• Increased security and fraud protection: Since AI tools can monitor data in real time, they're superior at security and fraud prevention. When it comes to protecting financial data with AI, a properly trained tool can spot the signals of a potential security breach or fraudulent activity and sound the alarm. As a result of this advanced warning, the company can respond more quickly and protect its assets.

How to Implement AI for Financial Reporting in Your Company

• Assess Your Current Processes: Before adopting artificial intelligence in financial reporting, it’s best to look at your current processes and find tasks that could benefit from automation. Common examples include data extraction, validation and reporting.
• Select the Right AI Tools: Make sure the tools you choose align with your reporting needs, such as natural language processing for analyzing large datasets or predictive analysis for forecasting.
• Ensure Data Quality and Governance: Having quality data is critical for getting the best results, so be sure you have strong governance policies to ensure the accuracy of what you feed the AI.
• Train and Upskill Your Employees: Although using AI for annual reports can’t replace human judgment, it can enhance your employees’ decision-making, enabling them to focus on higher-level tasks. Train your finance team to use these tools so they get the most benefit from them.
• Monitor and Refine: You should always be watching the performance of the AI tools you use, updating them regularly to reflect changing business conditions and regulatory requirements to ensure ongoing accuracy and relevance.

Use Cases of AI in Financial Reporting

There are many exciting use cases for financial reporting with AI. Here are three of the most compelling examples and benefits of AI in accounting and finance.

AI for Financial Statement Analysis

You may think of audits as a fairly standardized process, reliant on a highly trained human figure to perform the audit. While that remains true, one of the most exciting developments for AI tools is in the financial statement analysis and audit processes.

A study of artificial intelligence in the audit process found that companies that include AI are more accurate in their audits. On the whole, auditing firms that leveraged AI workers with technical rather than accounting backgrounds as part of the process had fewer restatements and SEC inquiries relating to inaccuracies. For example, AI can cross check balance sheets and financial statements to make sure that information is reported accurately. There's also the potential for fraud prevention when using AI to screen internal data.

Results were more dramatic with established companies that tend to have a large corporate repository full of documents requiring audit oversight or analysis. AI tools sifted through the virtual data bank, returned relevant information and helped the auditor to do the part of the job best left to a skilled worker.

The auditing study provides an optimistic outlook for the future. Companies that invest in AI tools, from SEC reporting software to consumer-facing chatbots, see strong results.

Financial Forecasting

One of the most compelling uses of AI for financial analysis is financial forecasting. Remember that AI operates by rules and learns from past behavior. A well-trained AI tool can scan social media posts, press releases, financial statements and financial headlines for early detection of everything from trends to risks. With a different dataset, the same tool can apply predictive logic to market futures data. While markets are notoriously difficult to predict, AI may be able to offer greater precision.

Natural Language Processing

Whether it's Chat GPT in finance or AI chatbots, natural language processing is an exciting trend to watch.

Natural language processing tools allow users to request data in plain language, such as "find me financial statements from the last five years."

The AI tool then compares or processes the standard language request against its internal database. Think of this as translating from one language to another. Once the AI identifies the matching term, it is able to satisfy the user's request.

Customers and employees alike have familiarity with natural language processing from AI assistants such as Siri or Alexa. As a result, they're able to adapt to the new technologies quickly and without a significant learning curve.

Increased Focus on AI Governance

As the financial reporting sector increases its adoption of AI technology, risk management and governance become that much more important. This means implementing the proper AI frameworks, ensuring you have the right controls in place and engaging in third-party audits to ensure transparency and regulatory compliance.

The Future of AI in Financial Reporting

It is still early days in the adoption of AI in finance industry. There are important risks to mitigate before AI tools can take on new challenges. A properly trained AI can perform requested tasks with accuracy, including repetitive tasks. While there is potential for increased accuracy and speed over a human employee, workers still need to take responsibility for training the AI, keeping up with regulatory updates and other changes. Otherwise, the tool will become inaccurate. For the same reason, someone needs to ensure the software is performing as intended with no skips or shortcuts. Challenges like these must be worked out for AI-enabled tools to achieve widespread adoption and fully support the financial industry.

Despite these drawbacks, early results are impressive. The AI tool takes repetitive, time-intensive tasks off employees' hands, freeing them up to do the tasks that only a human can do. Financial firms that leverage these tools can better handle the large amounts of data produced, simplify and organize reporting workflows, and keep up with new developments.