Press Release
By Anestis Dimopoulos*
As artificial intelligence (AI) systems are used more and more in core business models in multiple sectors such as finance, healthcare, technology, and human resources, ensuring their transparency, fairness, integrity and reliability is paramount. According to an EU survey in 2024, 11.21% of small enterprises, 20.97% of medium enterprises and 41.17% of large enterprises use AI. Auditing AI has emerged as a key mechanism for holding AI systems accountable, mitigating risks, and ensuring compliance with ethical and regulatory standards (e.g. EU AI Act).
At the same time, auditing with AI capabilities emerges as a powerful tool to enhance the audit process and provide significant advantages. A recent survey by ICAEA indicates that 69% of global participants exhibit a positive and proactive attitude towards utilizing AI for audit purposes, while 78% of participants consider audit software with AI features as the most suitable for leveraging AI technology in audit tasks.
The need for auditing AI systems
The rapid deployment of AI systems raises significant concerns related to bias, explainability, security, and compliance with legal frameworks. Some of the primary reasons for auditing AI systems include:
1. Bias and Fairness – AI systems can inadvertently amplify biases present in training data, leading to unfair outcomes. Audits help detect and mitigate such biases.
2. Transparency and Explainability – Many AI models, particularly deep learning systems, function as “black boxes,” making it difficult to understand their decision-making processes. Audits improve transparency by evaluating how models operate.
3. Security and Robustness – AI systems can be vulnerable to adversarial attacks and data poisoning. Audits assess the resilience of these models against security threats.
4. Compliance with Regulations – Emerging laws like the EU AI Act and the U.S. Algorithmic Accountability Act necessitate AI audits to ensure adherence to ethical and legal standards.
5. Trust and Public Confidence – Organizations that implement AI audits demonstrate a commitment to responsible AI usage, fostering trust among users and stakeholders.
Approaches to Auditing AI
Auditing AI can be conducted using a variety of approaches, each suited to different aspects of AI system evaluation. The main approaches include:
1. Technical Audits – These involve reviewing the AI system’s data, model architecture, and algorithmic performance. Methods include:
• Bias Detection Tools – Techniques like counterfactual fairness analysis and disparate impact testing assess bias in AI decisions.
• Explainability Techniques – Tools like SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) help interpret AI predictions.
• Security Testing – Adversarial testing methods probe vulnerabilities by introducing manipulated inputs to observe system responses.
2. Process Audits – These evaluate the governance processes surrounding AI system development and deployment, ensuring best practices are followed. This includes:
• Audit of relevant process documentation to verify transparency in AI development.
• Compliance checks against industry standards such as ISO/IEC 42001 (AI Management System Standard).
3. Outcome Audits – These analyze the real-world impact of AI decisions by assessing outputs for fairness, accuracy, and unintended consequences. This is particularly useful for hiring algorithms, loan approval systems, and predictive policing tools.
4. Third-Party Audits – Independent audits conducted by external organizations enhance credibility. These may be regulatory audits, ethical reviews, or certifications from AI ethics boards.
So, what about Auditing with AI?
AI is transforming auditing by automating complex tasks, enhancing fraud detection, improving risk assessments, and ensuring compliance with financial regulations. Traditional financial audits involve labor-intensive data analysis, sample testing, and compliance checks. AI enables auditors to analyze entire datasets in real time, identify anomalies, and provide deeper financial insights. AI can be used in the following ways to conduct financial audits effectively.
1. Automate data processing and analysis by handling large volumes of structured and unstructured data, including transactions, invoices, financial statements, and contracts through AI-powered OCR, NLP techniques and ML algorithms.
2. AI powered fraud detection and anomaly identification, through outlier detection, Benford’s law analysis and graph analytics
3. AI powered risk assessment by evaluating financial and other risks more accurately, by assessing financial health, predict future risks and automate risk scoring.
4. Compliance and regulatory auditing through real time compliance monitoring, automate regulatory reporting and identify policy violations.
5. Continuous auditing and real time monitoring through automated journal entries testing, real time transactions monitoring and audit trail analysis.
6. AI-Driven predictive analytics, to predict future financial trends and risks, including forecasting revenue and expenses, identifying future compliance risks, or evaluating market and economic factors.
Conclusion
AI auditing is crucial for ensuring ethical, fair, and responsible AI use. While current approaches provide valuable insights, auditing practices must continue evolving to keep pace with AI advancements. Standardized frameworks, automated monitoring, and interdisciplinary collaboration will shape the future of AI audits, fostering greater accountability and trust in AI-driven decision-making.
At the same time, AI enhances the auditing processes by improving accuracy, fraud detection, risk assessment, compliance monitoring, and predictive analytics. AI-powered tools enable auditors to analyze entire financial datasets in real time, reducing errors and increasing audit efficiency. As AI continues to advance, it will play a central role in shaping the future of financial auditing, ensuring greater transparency and trust in financial reporting.
Baker Tilly South East Europe continuously evolves its auditing methodologies, according to international standards, while at the same time builds solutions for providing assurance on AI systems, using the expertise of its highly experienced professionals and the overall global Baker Tilly network.
Concluding, auditing with AI capabilities is the inevitable next step of IT audit or financial and operational audits, while the auditing of AI is also inevitably needed to provide assurance to the digital environment implemented by organizations more and more recently. Audit with AI, also for auditing of AI!
*Anestis Dimopoulos is the Director, Head of Digital & Risk Advisory of Baker Tilly South East Europe
