AI-centric guide to Artificial Intelligence in Accounting

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  • Artificial Intelligence
AI-centric guide to Artificial Intelligence in Accounting
Date: June 26, 2024 Author: Konrad Budek 7 min read

Cunning as a fox, Benedetto Cotrugli took the best from his upbringing in a merchant family in the Republic of Ragusa. It was one of the most active and vibrant merchant republics of the 15th century. In his life as a merchant, diplomat, and scientist, Benedetto was the first known man to codify the rules of double-entry bookkeeping.

The world has changed since then, yet from the bookkeeping rules codified then, all modern accounting has evolved. 

What hasn’t changed is the data-richness of accounting, a blessing and a curse alike, making it prone to human errors. And a fertile ground for AI-based solutions. In fact, the accounting AI market is expected to reach $4.7 billion by the end of 2024

What is AI (in accounting)

Artificial Intelligence (AI) is a branch of computer science that focuses on creating systems or machines capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, language understanding, and decision-making. AI systems use algorithms and large amounts of data to recognize patterns, make predictions, and improve their performance over time.

Modern AI-based solutions find their application in multiple fields, with accounting being no exception.

Applications of AI in accounting

There are multiple examples of AI in accounting due to its ability to automate repetitive tasks, such as data entry and invoice processing. It enhances accuracy by significantly reducing human errors and can analyze large datasets quickly to identify trends and anomalies. 

Additionally, AI provides real-time financial insights and predictive analytics, which aid in strategic decision-making. Its pattern recognition capabilities improve compliance and fraud detection, making accounting processes more secure and reliable.

The most important AI applications in accounting include:

Automation of Routine Tasks

The stereotype of an accountant shows one who is diligent, patient, and shows an attachment to details. On the other hand, accountants are stereotypically not associated with volatility or unpredictability. The root of this stereotype comes from the nature of the work itself – full of repeatable and predictable tasks every day, basically the perfect ground for artificial intelligence in accounting and finance. 

  • Data entry and transaction processing – the day-to-day collection of documents, be they receipts, invoices, or any other payment documentation. OCR capabilities and unstructured data harvesting can be a great aid in day-to-day accounting and bookkeeping work.
  • Reconciliation of accounts – another easy to automate process, the reconciliation of accounts uses the pattern-recognition capabilities of Artificial Intelligence. The system may automatically match transactions from a bank account with transactions from the company books and systems, or flag those that are hard to identify. This use case is a perfect example of accounting automation with AI.

Fraud Detection and Risk Management

Risk-aversion in the accounting world has a serious reason behind it. Fraud, money laundering and the financing of terrorism are just the tip of the iceberg when it comes to the risks accountants need to keep at bay. And these become greater the bigger and more international the organization is. AI in accounting and finance can be used to support this work.

  • AI pattern recognition when digging through data – one of the key challenges is in staying vigilant when digging through documents and transactions. The key is in spotting particular patterns in data, no matter how obfuscated it may be. The AI system is trained on immense amounts of data, so it is highly probable that, no matter how deep the pattern is hidden, the system will recognize it.
  • Anomaly detection in financial transactions – AI has a tireless ability to spot anomalies and recognize patterns in large amounts of data, including the repetitive and human-unfriendly data seen in the accounting and financial sector. Early anomaly detection is one of the key elements of keeping a company secure and compliant.

Predictive analytics

The financial sector, including accounting, is data-heavy and rich in historical information. This is mostly due to the fact that the whole sector is centered around analyzing and keeping financial and fiscal information. If not for the benefit of the company, then for keeping compliance with existing norms. Yet historical data can be harvested for insights that can give the company a glimpse into the future.

  • Risk assessment – connecting existing historical data and external data, an AI-powered system can aid accounting specialists in delivering a risk assessment study. 
  • Strategic planning – gathering insights from existing and external data enables the company to not only think about mitigating risks, but also on applying the best solutions for the future. 

Benefits of AI in Accounting (examples)

The use cases listed above are only the tip of the iceberg – detailed use cases are dependent mostly on company readiness and their boldness regarding AI implementation. There are numerous advantages in applying AI in accounting software.

Increased Efficiency and Productivity

AI automates multiple tasks in accounting. In fact, NUMBER of tasks are expected to be automated by DATE. As such, AI solutions may increase the efficiency and productivity of accounting teams by dealing with dull and repetitive matters, leaving the specialist’s skills and knowledge free for more interesting tasks. 

Reduction of manual labor and errors

Handing such tasks to AI results in reducing the number of manual tasks that have to be performed – and thus not only repetitive work is reduced, but there are fewer opportunities for human mistakes. A simple typo hidden deep in a pile of documents can wreak havoc if unspotted, especially when dealing with taxes. (manual mistakes stats?)

Faster processing times

The job needs to be done – and AI systems are relentless. They can work 24/7 and need way less time to process data. Artificial Intelligence may be as transforming as spreadsheets have been to business processes.

Cost Savings

With jobs done faster, with fewer mistakes and with team’s skills redirected from the dull and repetitive to sophisticated and complicated processes, the company will show greater cost efficiency overall. 

Challenges and Considerations

The AI transformation is everything but cheap. Companies need not only to prepare their datasets and teams for implementing the new tech. The legal and regulatory environment is also volatile and needs to be closely monitored implementing Artificial Intelligence in accounting and auditing.


The financial sector is heavily regulated, with multiple laws in different jurisdictions, with the EU AI act being a perfect example. Also, there are international regulations regarding money laundering or the financing of terrorism, so AI needs to be strictly controlled and deployed in adherence to the established norms. 

Integration with Existing Systems

Corporate IT creates a web of interconnected and interdependent subsystems and components. Sometimes, these systems are just “too big to fail” and need to be maintained despite increasing technical debt. By that, adjusting them to the needs of a new technology meets with challenges impossible to ignore: 

  • Compatibility issues – first and foremost, if the system is not ready for implementing an AI-based system, or any new system, adding a sophisticated ML-powered component may be a challenge from an engineering perspective alone. 
  • Data migration challenges – even with full support from the engineering and management teams, the large datasets processed by accounting teams may be too big or too complicated to seamlessly migrate from one system to another, even a more AI-friendly one. 

Security and Privacy Concerns

AI systems are trained using tremendous amounts of data, the more and the bigger the dataset, the better. On the other hand, this approach comes with risks for users and for the company.

  • Privacy implications of AI use – AI-based systems have access to personal information and may use it in a way they are not intended to. A famous example comes from Amazon, where the system has excluded female programmers from hiring processes only because they are females.
  • Data security risks – an AI-based system may constantly improve itself using input data in the manner it was done in ChatGPT. Yet with this approach, the AI system can be a source of leaks, by revealing shared information in another conversation, for example. Samsung was a company that experienced a leak of this type. 

Skills and Training

AI-based solutions are considered super-easy to use, being either an invisible part of the system or being accessible via a conversational, chat-based interface. Yet this is not an excuse to leave the team without proper training and onboarding for the new solutions. 

  • Need for upskilling accounting professionals – accounting professionals are experts in accounting. Assuming that they will start using a new solution with 100% productivity from day one is bold and usually inaccurate. The active engagement of accounting teams as well as picking out thought leaders and early adopters from among the team is crucial for the product to succeed. 
  • Training programs for AI literacy – with AI infusing all processes within the organization, the company needs to address doubts and concerns across all teams. Delivering company-wide AI literacy training will get the teams ready for changes, reduce doubt and will help show the advantages. 

Generative AI in accounting

All the advantages and concerns listed above are applicable to traditional AI.

Applying generative AI in accounting comes with another set of challenges and benefits.

What is generative AI?

In its most basic form, generative AI differs from traditional AI in its goals. The traditional processes data and delivers precisely defined outputs. Generative AI produces the output in the form of a desired media type, be it a text, a sound, an image or a video. This makes these solutions extremely flexible, yet burdened with their own risks and concerns unseen anywhere else. 

The off the top of the head use cases for a generative AI that solves accounting problems include: 

  • Automating Report Generation – generative AI may gnaw through datasets and provide the user with a written interpretation in the form of a report. An accounting specialist can support his or her job with this AI solution with proper prompts and adjustments.
  • Adding comments and narratives – following the example above, generative AI can provide support in day-to-day work, for example by delivering on-the-go comments on the delivered or analyzed data. 
  • Streamlining communication – last but not least, generative AI may support the accounting team in external communication. The system may provide all communicational contributions, apart from expert input from the accounting team.

Yet the user needs to remember that generative AI systems are prone to hallucinations – a phenomenon where a neural network makes up plausible sounding information, with no real connection with ground truth. 

Recap of key points

Accounting and Artificial Intelligence are apparently a perfect pair. Yet it is crucial to implement solutions in close cooperation with specialists and teach them how to use AI in accounting. Assuming their acceptance and enthusiasm, a company may increase efficiency, reduce costs and find new opportunities.