The use of algorithms and artificial intelligence within financial and accounting functions has become increasingly widespread and is no longer limited to data analysis or management reporting. Organizations use algorithmic models to forecast revenues, assess credit risk, detect fraud, analyze customer behavior, estimate certain accounting items, and support pricing and investment decisions. AI applications have also begun to enter directly into financial closing processes, reconciliations, and the preparation of disclosures.
This transformation creates a significant opportunity to increase operational speed and improve the ability to process massive amounts of data, but at the same time, it raises a question more complex than simply asking: Is the algorithm accurate? The more important question is: Is the figure produced by the algorithm unbiased and reliable enough to support financial decision-making?
A system may produce a mathematically accurate figure, yet that figure may remain financially unreliable if the data on which it was built is biased, if the assumptions underlying the model are inappropriate, or if the relationships identified by the model do not reflect the actual economic relationship. This particular point makes algorithmic bias an issue that goes beyond technology to become a matter of financial information quality, governance, and control.
The Organisation for Economic Co-operation and Development (OECD) indicates that machine-learning-based models may inherit biases present in the data and may even introduce new biases as a result of hidden relationships between variables or the use of proxy variables that indirectly reflect sensitive characteristics. The issue is also not limited to data; bias may emerge during model development, application, or interpretation of its outputs.
Where Does Algorithmic Bias Begin Within the Financial Environment?
The common mistake is to treat algorithmic bias as a problem that occurs only when the data is “bad.” The reality is more complex. Data may be historically accurate and statistically complete, yet still produce biased outputs if it reflects historical decisions or practices that were not originally neutral.
For example, if a model is trained to predict default risk using historical data reflecting a period during which certain customer segments received different credit terms than others, the model may learn these patterns as natural indicators of risk. When it is later used to make new decisions, it may reproduce the same historical pattern, even if that pattern does not reflect the actual economic risks at the present time.
Here, one of the most dangerous characteristics of algorithmic bias emerges: the model may not be wrong in interpreting the data; rather, it may read the data correctly and derive an unfair or inappropriate outcome for the decision we seek to make.
The problem becomes more complex when the model uses variables that appear neutral on the surface but function as indirect proxies for other sensitive variables. This makes bias more difficult to detect, particularly in complex models that rely on a large number of variables and interconnected relationships.
How Can Bias Move from the Algorithm to the Financial Figure?
Algorithmic bias does not need to result in an incorrect accounting entry in order to affect the financial statements. Its impact may be more gradual and less obvious.
If an algorithm is used to estimate default probabilities, bias in these probabilities may flow into estimates of expected credit losses. If the model is used to forecast demand, deviations in forecasts may affect estimates of revenues, inventory, and production capacity. If algorithms are incorporated into asset valuation or impairment models, bias in the assumptions or data used may be reflected in the carrying value of assets, provisions, and financial results.
Here, it is important to distinguish between calculation error and bias in inputs or methodology. A calculation error can often be detected through recalculation or consistency testing, whereas bias may produce a mathematically correct figure because it results from an equation operating exactly as designed. The problem, however, lies in the fact that the design itself or the inputs on which it relies do not properly reflect the economic reality.
For this reason, modern auditing practices emphasize the need to view algorithms as part of the information environment that may affect account balances, estimates, and disclosures, rather than simply as technical tools outside the scope of financial controls. Auditing practices recommend examining data quality, assumptions, potential bias in inputs, the algorithm’s methodology, and the technological environment in which it operates.
Accounting Estimates
Algorithmic bias becomes increasingly important when model outputs enter areas that already require a degree of professional judgment.
Figures related to provisions, asset impairment, fair values, credit losses, financial forecasts, and other estimates are not always generated by a single equation whose accuracy can be mechanically verified. Instead, they depend on a combination of assumptions, data, and forward-looking estimates.
If an algorithm becomes part of this process, the risk does not lie only in the model being “wrong,” but also in the model giving management the impression that the estimate has become more objective simply because it was produced by an advanced system.
This can be described as “the illusion of algorithmic objectivity.”
A figure generated by a mathematical model may appear more neutral than a direct human estimate, but in reality, it reflects the data selected, the variables included, the relationships the model was designed to identify, and the assumptions on which it was built. Therefore, moving human judgment into a model does not eliminate judgment; rather, it may make that judgment less visible.
This issue becomes particularly important in a financial reporting environment because the audit committee, auditor, and management do not only need to know the final result; they also need to understand the basis that led to it. Therefore, modern approaches to controlling the use of artificial intelligence within financial reporting emphasize the importance of documentation, human review, traceability of outputs, and demonstrating how results affecting financial information were approved.
The Problem Does Not End with the Model: The Data Itself May Be the Source of Risk
An organization may invest significant resources in purchasing an advanced model and then treat the data feeding it as a given. This is where the actual problem may begin.
Financial and operational data do not automatically reach the model in a neutral form. There may be missing periods, differences in recording methods between departments, changes in variable definitions, historical data that no longer reflects the current environment, or excessive reliance on a single source of information.
In changing business environments, historical data may become less capable of explaining the future. A model that learned from market behavior under certain conditions may produce inappropriate forecasts when prices, customer behavior, supply chains, or the regulatory environment change.
Here, the concept of Model Drift emerges; that is, a model that performed well when it was developed may become less suitable over time as a result of changes in data, economic conditions, or the nature of the relationship between variables. Therefore, testing the model at launch is not sufficient. A financial model requires continuous monitoring, periodic testing, and a review of whether the assumptions on which it relies remain valid.
Modern regulatory discussions around artificial intelligence in the financial sector indicate that data and model risks cannot be separated from governance risks, because the difficulty of interpreting some models makes it more complicated to detect inappropriate data or incorrect relationships.
Can Auditing Detect Algorithmic Bias?
The answer is not simple, because traditional auditing may establish that a process followed the specified procedures without necessarily detecting that the logic underlying the model contains bias.
For this reason, the introduction of artificial intelligence into financial processes requires an expansion in the scope of risk assessment. It is no longer sufficient for an auditor to ask: Was the transaction approved? Are there supporting documents? The auditor may also need to know where algorithms are used, what data they rely on, who is responsible for the model, how it was tested, what changes have been made to it, and how its outputs are monitored.
Modern professional practices indicate the need to include the use of artificial intelligence within financial reporting risk assessments and internal control assessments, particularly when its outputs affect accounting entries, reconciliations, estimates, or disclosures. The existence of a clear audit trail, appropriate human review, and continuous model testing has also become essential to building confidence in outputs that enter the financial environment.
This does not mean that auditors must become data scientists. Rather, it means that risk assessment must change when an algorithm becomes part of the financial information production chain.
Governance: The Real Line of Defense
It is a mistake to treat algorithmic bias as the sole responsibility of the information technology function. If the outputs of a model enter financial figures or decisions that affect the financial statements, the resulting risks become part of the responsibility of governance and financial controls.
This is where the role of the board of directors, audit committee, executive management, and internal audit becomes important in clearly defining responsibilities. It must be known who approves the use of the model, who determines its purpose, who reviews its data, who tests its accuracy, who decides when it should be stopped or retrained, and who is accountable when its outputs influence a material financial decision.
In its latest work on AI oversight in the financial sector, the OECD indicates that the difficulty of determining accountability increases as systems become more autonomous. Therefore, clear lines of accountability and governance remain essential even as increasingly autonomous systems develop.
The issue, therefore, is not simply having someone “responsible for AI,” but having clear accountability for the decision that relies on AI.
Why Is Human Review Alone Not Enough?
The solution may appear simple: assign an employee to review the algorithm’s outputs before they are approved. However, the presence of a human at the end of the process does not necessarily mean that effective control exists.
If the reviewer does not understand how the model arrived at the result, trusts the system excessively, or reviews only exceptional cases without systematic testing, their presence may become a formal procedure rather than an effective control.
Modern professional approaches to AI auditing emphasize that human review and accountability must remain at the center of governance, particularly in processes that affect financial reporting.
Model Accuracy and Reliability of Financial Figures
A model may be accurate according to the performance measures for which it was designed, but this does not automatically mean that the figure it produces is suitable for use in financial reporting.
There is a difference between Model Accuracy and Financial Reliability.
The first asks: Does the model predict or classify effectively according to the data and criteria on which it was selected?
The second asks: Can its outputs be relied upon when they become part of financial information on which an investor, board of directors, or creditor makes a decision?
This distinction is what organizations must elevate to the governance level. Model testing should not stop at statistical performance. It should also include data suitability, model stability, potential bias, interpretability of outputs, financial impact, and the existence of controls that prevent the model from being used outside the scope for which it was designed.
Algorithmic Bias Is Not Just a Technology Risk
As the use of artificial intelligence within organizations expands, it is no longer appropriate to place bias risks solely within the category of “technology risks.”
If an algorithm affects a financial estimate, the issue may become an accounting problem. If it affects an internally controlled process, it becomes a control issue. If accountability for it is unclear, it becomes a governance issue. If it results in unreliable disclosure, it becomes an issue related to investor and market confidence.
For this reason, modern practices are moving toward integrating AI risks into existing risk and control frameworks rather than creating an entirely separate system. Professional practices recommend linking the use of artificial intelligence to enterprise risk management frameworks and internal control assessments, while identifying uses that may materially affect financial reporting and subjecting them to a higher level of oversight and testing.
In Conclusion …
Algorithmic bias does not mean that artificial intelligence is unsuitable for use in accounting or finance, nor does it mean that human judgments are inherently more objective. The real risk begins when an organization treats algorithmic outputs as neutral simply because they are generated by a mathematical model.
Financial figures do not become more reliable because they were produced by an intelligent system, just as they do not become less reliable simply because part of their production process has become automated. The true standard is the quality of the data, the soundness of the methodology, the appropriateness of the assumptions, model testing, traceability of outputs, the presence of genuine human oversight, and, most importantly, clear governance that defines who owns the decision and who is accountable for it.
As artificial intelligence moves from support tools to systems that directly participate in producing financial information, the question organizations should be asking is no longer: Does the algorithm work?
It has become: Can we explain what it produces, test it, hold those who rely on it accountable, and ensure that the figure reaching the financial statements actually reflects economic reality?
