AI in Accounting: Trends, Tools, and Integrations

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Artificial intelligence (AI) is great with numbers. That’s why adoption by two of the most number-based professions — auditing and accounting — was just a matter of time. In fact, when Thomas Reuters surveyed US, Canadian, and UK accounting professionals, close to half believed that ChatGPT or similar tools should be applied to their work. And 35% of respondents expected their companies to deploy AI-based solutions within six months.

AI in Accounting

However, the adoption of AI in accounting is held back by high cost, technical complexity, bureaucratic barriers, and lack of understanding. In this article, we explain AI technology for accounting, its potential uses and benefits, and major concerns surrounding its implementation.

What types of AI are used in accounting?

You'll be surprised by how many areas of AI are increasingly being used in accounting. Thanks to machine learning (ML), natural language processing (NLP), robotic process automation (RPA), predictive analytics, and deep learning, accounting departments can improve their efficiency and accuracy. Here are some tasks where AI in accounting can help:

  • Automated data input and processing. ML algorithms can process large volumes of transactions and automate data entry. For example, GenAI can scan PDF invoices and receipts for relevant information, automatically extract it, and enter it into other documents.
  • Reconciliation. RPA bots can automatically reconcile accounts by comparing internal records with external statements.
  • Document processing. NLP can extract information from invoices, receipts, and other financial documents, making entering and categorizing data easier. By extracting key terms and numbers, it can summarize information, saving a tremendous amount of time.
  • Chatbots and virtual assistants. These digital helpers can handle routine queries from clients or employees, providing instant answers and saving accountants time.
  • Tax compliance. RPA can handle the preparation and submission of tax forms, ensuring compliance with legal requirements.
  • Anomaly or fraud detection. ML models excel at identifying patterns and detecting anomalies in financial data to improve fraud detection. They can learn from historical data to detect unusual transactions and discrepancies that simpler rule-based systems might miss.
  • Budgeting. Predictive analytics can help create more accurate budgets by recognizing spending patterns and forecasting future expenses.
  • Risk assessment. AI-based systems can assess the risks associated with various financial decisions, helping to mitigate potential losses.

With AI handling a variety of data entry and bookkeeping tasks, one starts to wonder…

Can AI replace people in accounting jobs?

The widespread belief that AI can easily replace accounting professionals originated from a 2013 study by Oxford University and Deloitte that estimated the probability of job computerization for accountants and auditors at 94%. The assessed probability was even higher for bookkeeping and payroll tasks, reaching 97-98%.

However, developments in the AI field over the next decade proved that the prognosis was overly simplistic as it discounted the complexities of auditing work. To date, there’s consensus among the Big Four accounting companies that, in its current state, AI technology cannot replace human professionals. 

All these firms (KPMG, Ernst & Young [EY], PricewaterhouseCoopers [PwC], and Deloitte) are investing in custom-made AI solutions, but these are intended to be used as a tool and free staff time for more value-added tasks. As Wes Bricker, the Vice Chair of PwC, put it, “Accounting is not simply about knowledge recall — judgement plays an incredibly important role… We're able to use the best of AI and technology to enable us to focus on critical skills, areas of judgment, emotional intelligence, skepticism, and experience.”

Employees have the same view as management. In Karbon’s recent survey of nearly 600 accounting professionals, 58% expressed no concerns about AI replacing them. Thomson Reuters surveyed 771 tax professionals across the US, Canada, and the UK and found they remarked on the time-saving benefits of AI frequently:

  • 88% of corporate tax debt professionals and 100% of tax firm professionals agreed that AI saved them time.
  • 75% of corporate tax debt professionals and 58% of tax firm professionals agreed that AI freed them up to focus on higher-value work.
  • 73% of all respondents felt that generative AI could be applied to their work, and 51% believed it should be applied.

The benefits of AI in accounting remain underexploited. Thomson Reuters surveyed 771 tax professionals across the US, Canada, and the UK and found that only 11% of accounting professionals were already using generative AI extensively. Furthermore, only 4% had provided their employees were provided with any kind of AI training. A considerable gap between the promise of AI and actual implementation has been characteristic of the industry in the past few years. The forces shaping this situation include inaccuracies in generic AI systems’ responses, lack of features that would make a difference to users, and the complexities of developing custom-made AI systems.

Overall, in 2024, AI implementation in the accounting industry remains low, lagging far behind promises and intentions. One reason is that the functionality of generic AI platforms is a poor match for the complexities of accounting and auditing work. To solve this problem, each Big Four company has been developing its own AI-based solutions. Their vision is to augment the workforce, not to replace it. 

AI in accounting: 7 promising use cases

At the current stage of development, AI tools are ill-suited to take over tasks that require informed judgment, refined analytical skills, experience, empathy, and understanding of a client’s needs. However, some very promising uses have been identified.

1. Tax research

AI holds the most promise for tax research, according to the Thomas Reuters survey of industry professionals: 79% of tax firm employees and 83% of corporate tax department professionals were interested in using AI for this purpose. This is unsurprising, considering the web search prowess of generative AI.

AI in Accounting

With ChatGPT 4’s web-browsing feature, it became a viable alternative to search engines. Instead of navigating a long list of links yielded by search engines like Google, users can now type their query as a ChatGPT 4 prompt and get a concise and to-the-point response, linking to recent tax legislation updates. However, considering the unresolved problem of the model’s “hallucinations,” users must take caution when applying results to tax situations.

2. Bookkeeping

Bookkeeping, with its highly structured nature, is easily automated. When Karbon surveyed accounting professionals, 59% believed that bookkeeping would be the function most profoundly transformed by AI.

AI-powered systems can efficiently handle bookkeeping tasks such as data entry from digitized receipts, invoices, and bank statements, transaction categorization, and bank reconciliation. Models complete these tasks with a high degree of accuracy, which reduces the need for human supervision. Instead of entering and checking numbers, your employees can focus on interpreting the data in a broader context.

3. Fraud detection

AI performs better than humans when it comes to fraud detection. Manually detecting suspicious transactions is tedious and time-consuming, but machine learning algorithms effortlessly analyze large amounts of data in real-time to find questionable transactions. 

Moreover, they use incoming data to continuously learn and automatically adapt to detect new fraud patterns that arise. In contrast, rule-based algorithms remain static and require manual updates. The continuous learning of ML AI makes it easier to keep up with the constantly evolving threats from bad actors.

For one of our FinTech clients, ElifTech deployed a fraud detection platform with AI capabilities using Google Cloud’s Vertex machine learning service. We developed the custom built AI solution based on a generic one to improve accuracy and add extra levels of data security. After the system was deployed, the client reported significant improvements in speed, accuracy, and efficiency in detecting suspicious activity. 

4. Auditing

Like with fraud detection, ML’s ability to analyze large amounts of data, identify patterns, and spot abnormalities is useful for financial audits. Companies such as EY and PwC are incorporating AI into their auditing processes to enhance their audit quality and efficiency. Although they don’t disclose specific numbers, these companies have reported a positive impact of AI on their auditing operations.

5. Customer service

NLP is good at answering people’s questions in terms people can understand. That makes it useful as a customer service tool in the accounting industry. A GenAI-based bot can provide an accurate, yet simply worded response whenever someone has a question for your accounting department.

Unlike rule-based systems, NLP can handle complex queries without losing track of the conversation. By having AI respond to questions, your accountants will be able to focus on their workflows without getting distracted.

6. Communication

Generative AI tools, such as ChatGPT, have been widely adopted by professionals across industries to draft business emails. Such tools generate polite and error-free messages in seconds, which can be a huge time-saver for anyone who needs to prepare and send dozens of emails during their typical workday.

Communication also topped the list of current AI uses in the accounting industry. In Karbon’s survey, 59% of respondents reported using AI to compose emails. Relying on AI for business communication might become the new standard in the near future.

7. Workflow automation

AI can automate a broad range of mundane and repetitive accounting tasks, such as:

  • Invoicing: the entire workflow, from generating and sending invoices to tracking payments and sending reminders
  • Payroll: payroll calculations, tax deductions, benefits administration, payment processing, and paystub generation
  • Vendor management: creating purchase orders, tracking deliveries, and processing payments
  • Document management: organizing financial documents, making them easy to retrieve
  • Financial report generation: automatic data entry and report creation
  • Real-time insights and analytics: providing information to make more informed decisions

Due to their versatility and efficiency, AI-based solutions are becoming widely adopted for workflow automation. Software examples incorporating this functionality include QuickBooks, Botkeeper, and OneUp.

At ElifTech, we developed an NLP system that reads PDFs and recognizes invoices for one of our clients. Users can upload a PDF file or a link to such a file, and after analyzing it, this tool can answer questions about the file’s content. For example, it can recognize an invoice from its content, regardless of its format or structure. This function is handy for companies working with clients or vendors from multiple locations that use different invoice formats.

Mitigating challenges of AI in accounting

Your accounting department can gain an edge by becoming an early AI adopter. However, integrating AI in accounting processes comes with a number of challenges and caveats.


The accuracy of responses remains a major concern when using generative AI. All developers of major AI systems warn that generative AI can make mistakes and recommend that a qualified human double-check the output.

In particular, generative AI is known to have the issue of “hallucinating”: making up information instead of admitting gaps in its knowledge or even providing a false response despite having access to the correct information. Rare as such occurrences are (around 3% for ChatGPT and somewhat higher for alternative systems), in the context of accounting services, they can still cause massive financial damage if an incorrect statement makes its way down the pipeline.

When an experiment pitted ChatGPT against accounting students solving conversational exam problems, ChatGPT scored a meager 47%, while the humans averaged 77%. The researchers interpreted the AI’s mediocre result as a byproduct of it being essentially a predictive tool. Among all the possible answers to a problem, ChatGPT would lean towards the one that was most frequently used in a variety of contexts across its training data set. Unfortunately, in many tax-related scenarios, the most frequent answer might not always be the correct one.

A workaround is to either keep a human in the loop to verify the accuracy of AI responses or to make the AI double-check itself to reduce the amount of error to negligible. Developing a custom-made AI model with additional functionality would accomplish the latter. Even if based on a generic AI platform, a custom model can demonstrate superior performance in terms of accuracy, efficiency, and security.

Data security and privacy

Accounting departments deal with sensitive information and go to great lengths to protect it, from managing access rights to elaborate cybersecurity protocols. Generative AI tools like ChatGPT typically don’t adhere to the same security standards. It would be unreasonable to expect them to; after all, generic AI tools weren’t developed for professional use or for handling sensitive information in the first place.

In addition, all data uploaded to open-source models, including ChatGPT, is stored and used to train future iterations of the model. This is another reason that accounting professionals approach AI with caution. Karbon’s survey found that 76% of respondents were concerned about AI solutions leaking sensitive data. About 20% of accounting firms have addressed this issue by banning their employees from using ChatGPT at work. 

However, missing out on the opportunities provided by generative AI might not be the wisest course of action. Instead, you may want to consider custom-made AI software that combines the benefits of generative AI with enhanced security to keep your accounting data safe.

Managing change resistance

Even the biggest accounting companies struggle to manage AI-related change. Only 4% of firms surveyed by Thompson Reuters provided employees with any training related to using ChatGPT or other AI tools. According to Karbon, 46% of professionals are concerned they might fail to keep up with rapid advancements in AI.

This means you would need to explain the AI adoption process to address this problem and prevent potential employee resistance to change. It’s important to stress that no one is getting fired because of AI; rather, it’s a tool to help them with mundane tasks. Next, your staff will need training to use the new AI tools. A good AI vendor provides this kind of training.

Transparency and accountability

One reason stakeholders might oppose adopting AI for accounting tasks is that they don’t understand how it works. Make sure to explain how your accounting processes will be augmented by AI and how you will manage this change and address data accuracy and security.

You need to establish clear accountability for decisions made by AI systems. Who will be responsible for the quality of AI output and any possible errors? By answering this question, you can ensure that you don’t end up in a damaging situation with no one taking responsibility.

Data integration

Accounting data often resides in various systems and formats. Integrating diverse data sources into an AI system can be complex and time-consuming. Also, you don’t want your workflows disrupted by the transition to AI, so you’ll need to take care to make it seamless. Pay attention to your vendor's capabilities, e.g., whether they can integrate your existing software, and their experience with AI systems for accounting.

Key takeaways

Adoption rates of AI in accounting are currently low. However, this situation is likely to change soon — the majority of accounting and tax professionals believe that generative AI and ML could enhance their work by automating routine tasks to boost efficiency and save time. In particular, accounting firms can use AI for tax research, automated bookkeeping, and instant fraud detection. 

Furthermore, adopting AI in accounting is unlikely to cause massive job losses in the industry since accounting is much more than number-crunching. Tasks that require critical judgment, experience, and building rapport with clients will continue to depend on human professionals.

Adopting generic AI platforms for accounting is not the best course of action. Professionals have raised numerous concerns about their accuracy, transparency, and data security. Instead, major accounting companies are investing in custom-made solutions that leverage generative AI with enhanced security and tailored functionality.

At ElifTech, we’ve delivered AI based fraud detection and workflow automation solutions that produced significant operational efficiency gains for our clients. If you’re ready to start integrating AI into your company’s accounting processes, contact us for a quote and a start of a fruitful collaboration.

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