AI in Financial Reporting: “Not quite there yet”

AI: Are we there yet?

Artificial Intelligence (AI) has been hailed as a panacea for various industries, promising revolutionary advancements in efficiency and accuracy. One area that has attracted considerable attention is financial reporting. Proponents of AI in finance argue that it can automate mundane tasks, detect fraud, and provide real-time insights. However, despite the hype surrounding AI, skeptics remain unconvinced that it has truly reached the point where it can revolutionize financial reporting.


In this article we explore current limitations and challenges that hinder immediate mass adoption, followed by a discussion on how, nevertheless, it is still likely that AI has a bright future in this critical domain.

Data Quality and Reliability

One major stumbling block for AI in financial reporting lies in the quality and reliability of the underlying data. AI algorithms heavily rely on vast amounts of accurate and consistent data to generate meaningful insights. In the realm of finance, data integrity is paramount. However, financial data is often complex, unstructured, and prone to errors, making it challenging for AI systems to decipher and interpret accurately. Even small discrepancies or outliers can significantly impact the output of AI algorithms, leading to flawed financial reports and unreliable insights.

Interpretation and Contextual Understanding

AI systems excel at pattern recognition and statistical analysis, but they often struggle with contextual understanding and interpretation. Financial reporting requires a deep understanding of complex accounting principles, industry-specific regulations, and nuanced financial analysis. While AI can process vast amounts of data quickly, its inability to comprehend the underlying meaning and context of financial information limits its ability to provide accurate and insightful reports. Human expertise and judgment are still necessary to ensure proper interpretation and application of financial data.

Lack of Accountability and Responsibility

When it comes to financial reporting, accountability and responsibility are critical. However, AI algorithms often operate as black boxes, making it challenging to trace the decision-making process and assign accountability. Without transparency and the ability to explain how conclusions were reached, AI-generated reports can lead to a lack of trust and regulatory concerns. Stakeholders, including auditors and regulators, require a clear understanding of the methodologies employed by AI systems, which currently remain elusive.


Compliance and Ethical Concerns

Financial reporting is subject to stringent regulations and ethical standards, and adherence to these guidelines is crucial for maintaining transparency and trust in the financial system. However, AI’s lack of ethical reasoning and potential biases pose significant risks. AI algorithms are only as good as the data they are trained on, and if the training data reflects any inherent biases or lacks diverse representation, it can lead to skewed outcomes in reports. The absence of comprehensive regulations specifically addressing the use of AI in financial reporting exacerbates these concerns and leaves room for potential misuse.

Adaptability and Flexibility

Financial reporting requirements evolve continuously, driven by changing regulations and industry practices. AI systems, on the other hand, often struggle with adaptability and flexibility. They are typically trained on historical data and struggle to incorporate new rules or regulations without extensive retraining. This lack of agility makes AI ill-suited to keep pace with the dynamic nature of financial reporting. Human professionals, with their ability to adapt and learn, continue to play a vital role in navigating complex reporting requirements.

The Case for AI: Challenges of Financial Reporting in the last 20 years

Even though challenges exist, when we examine the way in which commerce has changed globally over the last two decades, it is possible to see how likely it is that AI will help businesses produce meaningful financial reports more efficiently in the future. 

Over the past two decades, global producers have sought to improve efficiency by minimizing inventory levels through just-in-time production and locating their production facilities in markets with lower labor costs and skilled workers. This approach has had a significant impact on global supply chains. Traditional suppliers have had to adapt by establishing distributed operations, moving production facilities closer to customers, and either creating or acquiring local subsidiaries with existing infrastructure and skills. As a result, organizational structures have become more complex, especially for small- and medium-sized businesses (SMBs). Parent companies have had to invest in systems that can manage distributed operations and provide visibility into remote production and administrative processes on a global scale.


While these solutions excel in streamlining specific line-of-business processes, they often lack the necessary financial management applications to handle complex reporting compliance requirements across multiple global jurisdictions. Legacy systems designed for localized on-premise installations often lack global accounting functionality, making it difficult to generate accurate reports for the parent organization. Additionally, these systems struggle to keep up with evolving local disclosure requirements, resulting in a heavy reliance on manual processes and spreadsheets. These limitations lead to delays in providing critical information to management and regulatory authorities, increasing the risk of poor decision-making and undermining the overall value proposition for companies operating globally.

Where we are right now

Cloud technology presents an opportunity to develop a modern and reliable system for global financial reporting, utilizing the validated data from a customer’s existing ERP systems as a foundation. Solutions developed in this medium can consolidate data from multiple different systems in a way that traditional ERP vendors and systems cannot. And importantly, they do not require any modifications to the source system, safeguarding the significant ERP investment made by the customer at each facility.

The foundation for useful AI

The availability of this type of intuitive, flexible, accounting hub and financial reporting system provides an enhanced environment in which AI can potentially provide significant value.

The systems not only aggregate the data, but they also map it to central templates – such as charts of accounts or currencies – thereby normalizing it, improving the quality and reliability of information provided by it, both in manually generated reports and via AI. Having a consistent set of data makes it easier to undertake meaningful interpretation and providing contextual understanding by reporting from a level playing field.


All data held in these systems is transaction-level, meaning that there is complete auditability from any consolidated and reported figures right back to source information, increasing accountability and making the allocation of responsibilities easier. This level of detail also provides a strong foundation for the disclosure of environmental, social, and corporate governance data (ESG financial reporting) that may require levels of interpretation and individualization, which could allow AI to offer significant added value. 

The “bridge”  before we go Full AI

Human professionals, armed with their knowledge and critical thinking abilities, remain indispensable in ensuring accurate, reliable, and ethical financial reporting today.

Despite lofty promises surrounding AI in financial reporting, it will still require human input and input to remain useful at this time and is likely to remain limited to providing first draft analysis and interpretation which will still require human review and amendment.

Nevertheless, as users become more experienced in “educating” AI systems, the need for interpretation and correction is likely to reduce, meaning that more reliance will be placed on the raw information coming from AI as users become more confident in its ability to provide “correct” data and analysis.


Only time will tell how quickly AI is able to absorb changing circumstances, updated regulations, different management perspectives, and unusual business circumstances while still being able to deliver high quality output without additional and significant user intervention.  

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