AI in Accounting at Enterprises is Boosting Efficiency

AI in accounting is becoming increasingly essential as businesses generate vast amounts of financial data from digital transactions and cloud-based systems. In addition, internal and external threats, such as ransomware, are rising, with accounting errors and data breaches posing significant financial risks - the average data breach cost now stands at $4.45 million. 

“The reality is that the audit industry is undergoing a significant but necessary transformation,” says Isaac Heller, CEO and co-founder of Trullion, an artificial intelligence (AI)–powered audit and accounting platform. 

“These macro-trends are leading all firms to point in one direction: toward AI.” As the industry evolves, how is AI used in accounting? By automating tasks like data entry, transaction classification, and report generation, AI can help reduce errors and make it possible for accountants to focus on strategic activities such as financial analysis, budgeting, and advising on growth. It also improves fraud detection and enhances forecasting through trend analysis to make accounting more efficient and reliable.

In fact, with a market projected to reach $26 billion by 2029, AI in accounting is set to transform many financial processes. For example, a recent survey showed that 73% of accountants reported an increased workload due to new regulations. AI can help manage this burden by automating the tracking of tax rules and generating accurate returns, making compliance more efficient and reducing the risk of errors. In this article, we’ll discuss how AI in accounting redefines financial processes, from automating routine tasks to enhancing fraud detection and forecasting. We’ll then walk through how companies like Danske Bank, Ernst & Young, and Legendary Entertainment are using AI to tackle challenges, streamline compliance, and boost efficiency.

AI in Accounting is Used in Key Areas of Financial Processes

AI in accounting is changing how financial tasks are managed, with one of the most significant benefits being data entry automation. AI platforms use OCR (Optical Character Recognition) technology to pull data from invoices, receipts, and bank statements with minimal human effort. This not only cuts down on errors but also saves time. By scanning documents and turning them into digital text, AI systems can quickly process and analyze the information. With machine learning at its core, these AI platforms continuously adapt, enhancing their precision and efficiency. For instance, AI can scan financial transactions, flagging unusual patterns that suggest potential fraud. With over 105 million victims affected by data compromises by the end of Q3 2022 and $3.1 trillion lost in fraudulent transactions in 2023, AI’s ability to learn from past data is crucial in spotting suspicious activities and protecting finances.

Generative AI in accounting takes this further by analyzing past financial data to predict future trends, providing businesses with a more accurate and comprehensive financial outlook. 66% of finance leaders believe that generative AI will have the most immediate impact on explaining forecast and budget variances. Beyond forecasting, generative AI's ability to handle large amounts of data also makes it helpful in conducting audits more efficiently. 41% of chief audit executives (CAEs) say they are already using or planning to use generative AI tools for auditing.

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Such AI-driven accounting automations can help finance teams focus on more valuable work, as over 40% of their efforts can be redirected to tasks such as analyzing financial trends, developing long-term budgeting strategies, creating detailed financial forecasts, identifying cost-saving opportunities, and offering data-driven insights to support executive decision-making and business growth. Jenn Ryu, CFO at RGP, highlighted this shift: "We’re looking for more well-rounded accountants. And accountants with better communication and storytelling skills as well,” as AI takes over routine tasks. She emphasized that with AI handling much of the manual work, there’s now a greater need for accountants who can interpret data, communicate insights, and contribute to the company’s strategy, making their role more strategic and impactful.

Danske Bank Enhances Fraud Detection Using AI Innovations

Financial institutions and banks worldwide have reported a staggering increase in fraud attempts, with each firm losing over $1 million in the past 12 months. Traditional methods, like manual transaction monitoring and rigid rule-based systems, are costly and restrictive and tend to drive away legitimate customers who find frequent investigations of their transactions frustrating. This significantly impacts customer experience, with 70% of customers reporting anxiety, stress, displeasure, or frustration when alerted about potential fraud. Many firms and banks like Danske Bank are using AI to avoid these issues.

Denmark's Danske Bank encountered significant challenges in effectively monitoring transaction fraud, as they detected approximately 1,200 false positive cases daily. Upon further investigation, 99.5% of these cases were confirmed to be false positives, resulting in a considerable waste of time for investigators reviewing such a high volume of transactions. The bank implemented a more advanced approach using AI and machine learning to address this inefficiency. After evaluating various anti-fraud software solutions, they developed their in-house AI system for fraud detection. Nadeem Gulzar, the Head of Global Analytics at Danske Bank, stated, “We know in the long run fraud will increase as we become increasingly digital.”

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Using machine learning techniques, Danske Bank managed to reduce false positive cases by 35% and achieved a similar improvement in detecting true positives (actual fraud). The bank’s AI system also collects data on how customers complete online forms. It identifies potential fraud if a form is filled out four times faster than usual, indicating it may not be the actual customer. The bank is exploring further methods to measure mouse movement speeds and analyze typing patterns for more accurate customer authentication. By incorporating deep learning, they nearly doubled their success, achieving a 60% reduction in false positives and a substantial enhancement in detecting genuine fraud cases. 

Enhancing Audit Quality and Efficiency with AI-Powered Tools at EY

While the rise in financial and accounting data can add pressure to auditing tasks, it also creates new opportunities for audit teams. AI, especially machine learning, helps auditors use advanced pattern recognition to analyze unstructured data like contracts, invoices, and images more efficiently. It also makes it easier to handle large datasets and identify potential fraud risks. That’s why firms like Ernst & Young are starting to use AI to streamline their auditing process.

Ernst & Young (EY), one of the largest multinational professional service networks, encountered difficulties with lease accounting, particularly with the need for ongoing monitoring of estimates, changes, and compliance. With frequent accounting updates, they needed to meticulously review their lease agreements and report financial obligations, a process that traditionally relied on manual reviews that are often time-consuming and error-prone. To combat these concerns, EY developed AI pilot programs that allow the analysis of a large number of lease documents in a much shorter timeframe.

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By using AI, EY could reduce the chance of human errors, analyze larger samples more efficiently, and provide better services to their clients. EY’s global accounts committee assurance lead, Jeanne Boilet commented, “These pilots show that AI tools would make it possible to review about 70% - 80% of a simple lease’s contents electronically, leaving the remainder to be considered by a human. With more complex leases (in real estate, for instance), that figure would be more like 40%, but as the tools improve and the machines learn, it is likely that more complex contracts and data can be read, managed, and analyzed.” 

Legendary Entertainment's Shift to AI-Powered Financial Management

Legendary Entertainment is an American mass media and film production company that has contributed to creating iconic movies like The Dark Knight and Interstellar. Legendary’s accounting team manages multi-million dollar projects. With money going in and out at a fast rate globally, they needed software solutions that could assemble data, organize budget details, and provide insights aligned with global accounting standards. Their legacy systems required the team to extract relevant data manually, which was time-consuming. Dawn Castro, the chief accounting officer at the company, explained, “It was apparent right away that the accounting software that we were currently using was inadequate. The general ledger for film costs alone had 150 account numbers for each project.”

Legendary Entertainment turned to AI-powered accounting software, specifically Sage Intacct, to manage their finances more effectively. Sage Intacct used AI-driven dimensional tagging, which allowed the system to replace the 150 account numbers per project with just four. This tagging system automatically categorizes expenses and revenues based on various dimensions such as project, location, and category, making it easier for the team to track and manage each film's costs accurately.

Sage Intacct's AI capabilities also enabled the software to analyze data in real time, identifying any unusual patterns or discrepancies that could indicate errors or potential fraud. This automated analysis meant that the system could flag inconsistencies much faster than manual reviews, ensuring greater accuracy in financial tracking. As a result, Legendary’s accounting team could quickly generate detailed financial reports, monitor budgets, and check spending with just a click, allowing them to focus more on analyzing project success and making informed financial decisions for the company.

Investing in AI and Talent: Key to Accounting's Evolution

AI is helping accountants move beyond routine tasks like data entry, fraud detection, and financial forecasting, allowing them to provide deeper insights and better support for decision-making. However, many finance organizations are still in the early stages of AI adoption, and there's a need for a more strategic approach to implementation. It makes it crucial for finance leaders to take proactive steps in embracing this change. As Marco Steecker, Senior Principal in the Gartner Finance Practice, emphasizes, “As they begin to chart out a plan for how best to prioritize that additional investment, CFOs should partner with their finance leadership teams to compare their current progress against their peers’ and identify concrete recommendations from early adopters on how best to accelerate AI use in their function.” By doing so, they can ensure their organizations remain competitive and continue to deliver high-value financial insights in an increasingly digital world.

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