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Accounting

AI evolves for CFOs and accountants

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While generative artificial intelligence is the hot conversation topic these days, we must not forget a long and successful history of using nongenerative AI, sometimes called legacy AI, especially for numerical and structured data. Uses such as forecasting of customer demand or revenues or the detection of patterns such as fraud or money laundering are important examples relevant to CFOs and accountants.

These tools and use cases improve in their capability every year and provide tangible business value.

Legacy AI uses

These nongenerative AI systems can also provide significant assistance in meeting compliance and regulatory requirements and preparing analytical reports for those purposes. Matching methods to detect which invoices and payments belong together, especially in cases of partial disparity, are in almost universal usage today and rely on AI.

Many of the more sophisticated management dashboards and systems underlying both accounting and enterprise resource planning software ultimately rely on such AI systems, for example inventory management and planning. Complex processes like just-in-time or just-in-sequence could not function without legacy AI backbones.

Limitations of generative AI

Turning to the oft-hyped topic of generative AI, we acknowledge that many claims are hype. Any tool, for instance, has an intended scope of use for which it is helpful and provides value. Beyond that scope, it is not helpful and may cause harm. Large language models are intended to manipulate language, not numbers, and so are generally not successful at dealing with numbers where we expect absolute accuracy.

A case in point is the analysis of a company’s annual report. If we do so using LLMs, we will get answers that are “enhanced” by information extraneous to the report, or we might get numbers that are not grounded in the report. Such uses are not appropriate and misleading. So what can we use them for?

Multimodal uses of generative AI

A step change forward of generative AI is its multimodal facility — the ability to work with text and images at once. Imagine taking a mobile phone snapshot of your latest restaurant bill and it’s automatically filed in the travel expense form of your company. What a time and hassle saver! This is quite accurate and thus also prevents human error. The same holds for invoices, receipts and other paper forms.

In case a legacy AI model discovers some sort of mistake — such as fraud or a partially paid invoice — it is generative AI that can convert this discovery into a human-readable message that explains what is going on and what to do about it. We have talked about explainable AI for many years, and it is LLMs that can produce an explanation even if the content of that explanation may need other systems to weigh in.

Natural language dashboards

We have all been in board meetings where one person asks an analytical question to which no one has the right numbers. Oh horror. An analyst will have to be kept busy for a few days, the charts sent, and the result is not actionable for a protracted time. Gone are the days! Generative AI can translate a question from English into the language of databases, SQL, and obtain the table of numbers that results. This table is then translated into the codified language of dashboards and displayed as a graphical image to the human user.

All of this occurs in the blink of an eye. Most importantly, the result is not hallucinated by the LLM but comes directly from the database — the answer can be trusted. This allows further questions to be asked live in the board meeting, eventually getting to an actionable result in a short time. I was present at such a meeting where a sequence of eight pointed questions was asked and answered in less than 10 minutes, leading to novel insights and a board decision. It was an eye-opener.

Support services

Fielding questions by employees, customers and suppliers is a major strain on any accounting division. Generative AI can help by triaging the most common questions and providing correct and sensible answers automatically. From providing help with the dreaded expense reports to filing invoices, AI can largely automate the everyday process of accounting, including matching it to the right expense account and getting approvals.

Security is important, especially when money is involved. Generative AI supplies a new level of sophistication for the detection of a variety of attacks such as phishing and hacking.

Some uses where AI, generative or not, can help in the realm of accounting have been listed here. Beyond the management of a company’s finances, the CFO also has to make many decisions for the rest of the company. AI can help analyze scenarios, help find reference data, and contextualize the situations and offerings of competitors or other vendors. It can help to objectify and compare the benefits of multiple options so that the CFO can better decide which to choose.

In conclusion, generative AI delivers genuine business value to the CFO organization after all the hype has been subtracted. The most impressive is the generation of dashboards on the basis of human-language questions. If you do nothing else, have a good look at that.

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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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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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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Accounting

Automated Tax Compliance and Global Regulatory Harmonization in 2026

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Corporate tax accounting departments are navigating an era of unprecedented regulatory complexity as global tax harmonization frameworks take full effect alongside real-time digital tax reporting mandates. Tax directors and accounting teams are adopting cloud-based tax compliance automation tools to manage multi-jurisdictional tax liabilities and satisfy stringent reporting rules across international jurisdictions.

Implementation of Global Minimum Tax Provisions
The implementation of international tax reform agreements—notably the Pillar Two global minimum tax framework—has reshaped multinational corporate tax planning. Multinational enterprises with consolidated revenues exceeding established thresholds must ensure an effective tax rate of at least 15% across every jurisdiction in which they operate.

Accounting teams are implementing specialized tax calculation modules integrated directly into enterprise resource planning (ERP) platforms. These automated tools calculate effective tax rates per country, identify top-up tax liabilities, and generate standardized compliance documentation required by national tax authorities.

Real-Time Digital Invoicing and E-Reporting Mandates
Tax authorities across Europe, Latin America, and Asia-Pacific have enacted mandatory electronic invoicing (e-invoicing) and continuous transaction controls (CTC). Under these systems, corporate transaction data must be submitted electronically to government portals in real time at the point of sale or invoice issuance.

This shift toward continuous digital tax reporting eliminates traditional annual tax audits in favor of ongoing automated compliance monitoring. Accounting departments are upgrading invoicing software to ensure seamless XML data formatting, digital signature authentication, and real-time validation against tax authority databases.

Automation and Data Analytics in Corporate Tax Strategy
To keep pace with dynamic tax legislation, tax departments are transitioning from reactive compliance teams to proactive strategic advisors. Machine learning algorithms analyze corporate transactional data to identify tax credits, research and development (R&D) incentives, and cross-border transfer pricing adjustments.

By automating routine tax return filings and calculations, corporate tax directors can focus on long-term capital structuring, evaluating the tax implications of corporate mergers, and optimizing international supply chain networks.

Strategic Priorities for Tax Executives
1. ERP System Upgrades: Ensure enterprise software is capable of generating real-time, granular tax data required for global minimum tax compliance.
2. E-Invoicing Integration: Implement scalable e-invoicing platforms to satisfy regional continuous transaction control regulations.
3. Strategic Tax Analytics: Utilize predictive tax modeling tools to evaluate structural changes in corporate operations and cross-border trade.

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