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Both auditors, management must prepare for AI impact

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1 year ago

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As AI works its way into more and more business processes, it has become increasingly important for auditors to understand where, why, when and how organizations use it and what impact it is having not only on the entity itself but its various stakeholders as well. 

Speaking at a virtual conference on AI and finance hosted by Financial Executives International, Ryan Hittner, an audit and assurance principal with Big Four firm Deloitte, noted that since the technology is still relatively new it has not yet had time to significantly impact the audit process. However, given AI’s rapid rate of development and adoption throughout the economy, he expects this will change soon, and it won’t be long before auditors are routinely examining AI systems as a natural part of the engagement. As auditors are preparing for this future, he recommended that companies do as well. 

“We expect lots of AI tools to inject themselves into multiple areas. We think most companies should be getting ready for this. If you’re using AI and doing it in a way where no one is aware it is being used, or without controls on top of it, I think there is some risk for audits, both internal and external,” he said. 

Robot Audit
Elevated View Of Robotic Hand Examining Financial Data With Magnifying Glass

Andrey Popov/stock.adobe.com

There are several risks that are especially relevant to the audit process. The primary risk, he said, is accuracy. While models are improving in this area, they still have the tendency to make things up, which might be fine for creative writing but terrible for financial data reporting. Second, AI tends to lack transparency, which is especially problematic for auditors, as their decision making process is often opaque, so unlike a human, an AI may not necessarily be able to explain why it classified an invoice a particular way, or how it decided on this specific chart of accounts for that invoice. Finally, there is the fact that AI can be unpredictable. Auditors, he said, are used to processes with consistent steps and consistent results that can be reviewed and tested; AI, however, can produce wildly inconsistent outputs even from the same prompt, making it difficult to test. 

This does not mean auditors are helpless, but that they need to adjust their approach. Hittner said that an auditor will likely need to consider the impact of AI on the entity and its internal controls over financial reporting; assess the impact of AI on their risk assessment procedures; consider an entity’s use of AI when identifying relevant controls and AI technologies or applications; and assess the impact of AI on their audit response.  

In order to best assist auditors evaluating AI, management should be able to answer relevant questions when it comes to their AI systems. Hittner said auditors might want to know how the entity assesses the appropriate of AI for the intended purpose, what governance controls are in place around the use of AI, how the entity measures and monitors AI performance metrics, whether or how often they backtest the AI system, and what is the level of human oversight over the model and what approach does the entity take for overriding outputs when necessary.

“Management should really be able to answer these kinds of questions,” he said, adding that one of the biggest questions an auditor might ask is “how did the organization get comfortable with the result of what is coming out of this box. Is it a low risk area with lots of review levels? … How do you measure the risk and how do you measure whether something is acceptable for use or not, and what is your threshold? If it’s 100% accurate, that’s pretty good, but no backtesting, no understanding of performance would give auditors pause.” 

He also said that it’s important that organizations be transparent about their AI use not just with auditors but stakeholders as well. He said cases are already starting to appear where people unaware that generative AI was producing the information they were reviewing. 

Morgan Dove, a Deloitte senior manager within the AI & Algorithmic Assurance practice, stressed the importance of human review and oversight of AI systems, as well as documenting how that oversight works for auditors. When should there be human review? Anywhere in the AI lifecycle, according to Dove. 

“Even the most powerful AIs can make mistakes, which is why human review is essential for accuracy and reliability. Depending on use case and model, human review may be incorporated in any stage of the AI lifecycle, starting with data processing and feature selection to development and training, validation and testing, to ongoing use,” she said. 

But how does one perform this oversight? Dove said data control is a big part of it, as the quality and accuracy of a model hinges on its data stores. Organizations need to verify the quality, completeness, relevance and accuracy of any data they put into an AI, not just the training data but also what is fed into the AI in its day to day functions. 

She also said that organizations need to archive the inputs and outputs of their AI models, without this documentation it becomes very difficult for auditors to review the system because it allows them to trace the inputs to the outputs to test consistency and reliability. When archiving data she said organizations should include details like the name and title of the dataset, and its source. They should also document the prompts fed into the system, with timestamps, so they can possibly be linked with related outputs. 

Dove added that effective change management is also essential, as even little changes in model behaviors can create large variations in performance and outputs. It is therefore important to document any changes to the model, along with the rationale for the change, the expected impact and the results of testing, all of which supports a robust audit trail. She said this should be done regardless of whether the organization is using its own proprietary models or a third party vendor model. 

“There are maybe two nuances. One is, as you know, vendor solutions are proprietary so that contributes to the black box lack of transparency, and consequently does not provide users with the appropriate visibility … into the testing and how the given model makes decisions. So organizations may need to arrange for additional oversight in outputs made by the AI system in question. The second point is around the integration and adoption of a chosen solution, they need to figure out how they process data from existing systems, they also need to devote necessary resources to train personnel in using the solution and making sure there’s controls at the input and output levels as well as pertinent data integration points,” she said. 

When monitoring an AI, what exactly should people be looking for? Dove said people have already developed many different metrics for AI performance. Some include what’s called a SemScore, which measures how similar the meaning of the generated text is to the reference text, BLEU (bilingual evaluation understudy), which measures how many words or phrases in the generated text match the reference text, or ROC-AUC (Receiver Operating Characteristic Area Under the Curve) which measures the overall ability of an AI model to distinguish between positive and negative classes.

Mark Hughes, an audit and assurance consultant with Deloitte, added that humans can also monitor the Character Error Rate, which measures the exact accuracy of an output down to the character (important for processes like calculating the exact dollar amount of an invoice), Word Error Rate, which is similar but does the evaluation at the word level, and the “Levenshtein distance,” defined as the number of single character edits needed to fix an extracted text to see how far away the output is from the ground truth text. 

Hittner said that even if an organization is only just experimenting with AI now, it is critical to understand where AI is used, what tools the finance and accounting function have at their disposal to use, and how it will impact the financial statement process. 

“Are they just drafting emails, or are they drafting actual parts of the financial statements or management estimates or [are] replacing a control? All these are questions we have to think about,” he said. 

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Accounting

AI-Driven Automation and Continuous Accounting Frameworks

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6 days ago

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August 15, 2026

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The accounting profession is undergoing a fundamental structural transition as enterprise finance departments shift from periodic month-end closes toward automated continuous accounting models. By integrating specialized machine learning algorithms directly into enterprise resource planning (ERP) platforms, chief accounting officers are transforming financial reporting from a retrospective exercise into a real-time operational asset.

The Shift from Periodic Close to Continuous Financial Reporting
Traditional accounting workflows heavily relied on manual data reconciliation, spreadsheet calculations, and multi-week closing cycles at the end of each fiscal period. In contrast, continuous accounting frameworks utilize automated software agents to process, validate, and post transactional data in real time as business activities occur.

Automated bank reconciliation tools cross-reference incoming bank feeds, invoice records, and purchase orders automatically. By resolving transactional variances instantly throughout the month, corporate accounting teams eliminate the traditional workload spikes associated with quarterly and annual closes.

Machine Learning in Audit Trails and Anomaly Detection
Advanced natural language processing (NLP) and machine learning tools are redefining internal audit and financial control environments. Automated systems analyze 100% of general ledger entries, identifying anomalous transactions, duplicate payments, and unauthorized journal entries in real time.

Rather than relying on random statistical sampling, corporate internal auditors can focus their attention on high-risk flags automatically surfaced by algorithmic monitoring platforms. This continuous risk assessment strengthens internal controls over financial reporting (ICFR) and significantly reduces fraud risk.

Evolving Roles for Accounting Professionals
As routine data entry and manual reconciliation tasks become fully automated, the skill set required for accounting professionals is shifting toward data analysis, system design, and strategic business advisory.
– Systems Governance: Accountants are increasingly responsible for monitoring algorithmic accuracy and managing data integration pipelines.
– Business Partnership: Finance professionals leverage real-time financial dashboards to advise operational leaders on margin management and working capital allocation.
– Regulatory Compliance Management: Accounting teams utilize automated platforms to ensure compliance with dynamic tax codes and international accounting standards.

Core Implementation Recommendations
1. Deploy Automated Reconciliation Tools: Integrate continuous transaction processing modules into existing enterprise ERP architectures.
2. Establish Algorithmic Governance Controls: Implement strict internal testing protocols to ensure automated accounting rules comply with GAAP/IFRS standards.
3. Reskill Accounting Teams: Invest in training finance staff on data analytics, workflow automation, and predictive financial modeling.

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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2 weeks ago

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August 9, 2026

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Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.

Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.

In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.

Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.

Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.

Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.

Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.

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Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

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2 weeks ago

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August 5, 2026

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Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.

The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.

In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.

AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.

When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.

Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.

This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.

Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.

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