This article is for informational purposes only and does not constitute audit or legal advice. You should consult with a qualified professional before making any decisions about the use of AI in your firm.

---

How Should I Assess AI Vendor Tools for Audit Compliance?

Your AI vendor will sell you a dream: a world of frictionless audits, unparalleled insights, and radical efficiency. They will show you a slick dashboard and a compelling ROI calculation. What they will not show you is the FRC investigator asking you to defend the tool's output when it has missed a multi-million-pound fraud. Assessing an AI vendor is not a procurement exercise; it is a critical component of your audit quality management.

Choosing an AI tool for your audit practice is one of the most significant decisions you will make this decade. The right tool, properly governed, can enhance audit quality. The wrong tool, or the right tool used improperly, can be a gateway to regulatory sanction, litigation, and reputational ruin.

Your standard vendor due diligence checklist is not fit for this purpose. You need a new, more rigorous approach, one that is grounded in the realities of audit regulation and the unique risks of AI.

Beyond the Demo: A Framework for Audit-Specific AI Due Diligence

Your assessment of an AI vendor must go far beyond their sales pitch. It needs to be a deep, critical, and sceptical examination of their technology, their processes, and their understanding of the audit profession. Here is a framework to guide your assessment, structured around the key questions an engagement quality reviewer or a regulator would ask.

Due Diligence Domain	Key Assessment Criteria and Questions to Ask
1. Alignment with Auditing Standards (ISAs)	Does the vendor understand your world? Can they articulate precisely how their tool helps you meet the requirements of specific ISAs? For example, how does it support the risk assessment process under ISA 315? How does it help you gather sufficient, appropriate evidence under ISA 500? If they cannot speak your language, they are a risk.
2. Data Governance and Integrity	Garbage in, gospel out? What are the tool's data ingestion capabilities and limitations? How does it ensure the completeness and accuracy of the data it analyses? Can it reconcile the data it has processed back to the client's trial balance? You need to be confident that the tool is looking at the right data.
3. Algorithmic Transparency and Explainability	Can you defend the "black box"? You do not need the vendor's source code, but you do need a clear explanation of how the algorithm works. What type of model is it? What are the key features it looks for? Crucially, what are its known limitations and blind spots? If the vendor cannot explain it to you, you cannot explain it to the FRC.
4. Validation and Back-Testing	Where is the proof? Has the vendor conducted independent, third-party validation of their model's effectiveness? Can they provide you with the results of back-testing against historical data sets where the outcomes are known? You need to see evidence that the tool works in practice, not just in theory.
5. Customisation and Context	Is it one-size-fits-all? Every client is different. Can the tool be tuned or customised to the specific risks and context of your client's industry and business? A tool that is brilliant at spotting fraud in a retail business may be useless in a construction company. You need to understand the tool's adaptability.
6. Security and Confidentiality	Is your client's data safe? This is non-negotiable. The vendor must be able to demonstrate enterprise-grade security controls, including data encryption, access controls, and a robust incident response plan. You need to understand their data residency and hosting arrangements. A breach of their system is a breach of your duty of confidentiality.
7. User Training and Support	How will they help you succeed? Does the vendor provide comprehensive training for your audit staff, not just on how to use the tool, but on how to interpret its outputs and maintain professional scepticism? What level of technical support is available during the audit engagement?

The Red Flags You Cannot Ignore

During your assessment, certain vendor behaviours should set alarm bells ringing:

• Evasiveness on methodology: A vendor who is unwilling to discuss their model's methodology, citing "proprietary IP," is a major red flag.
• Overstating capabilities: Be wary of vendors who claim their tool can "eliminate" risk or "guarantee" fraud detection. This demonstrates a fundamental misunderstanding of the audit process.
• Lack of audit expertise: If the vendor's team does not include experienced auditors, they are unlikely to have built a tool that truly understands the nuances of the profession.
• Inflexible, "one-size-fits-all" solutions: A vendor who tells you that their standard model is perfect for all your clients is a vendor to avoid.

The Bottom Line: You Are Buying a Capability, Not Just a Tool

When you select an AI vendor, you are not just buying a piece of software. You are integrating a new capability into the heart of your audit process. You are trusting that vendor to help you deliver a high-quality audit.

This is a decision that must be owned by the audit leadership, not delegated to the IT or procurement department. It requires a deep, sceptical, and audit-focused assessment.

Choosing the right AI vendor can be a powerful catalyst for improving audit quality. Choosing the wrong one can be a fast track to the front page of the Financial Times for all the wrong reasons.

---

Take the Next Step

If you are ready to move from theory to action, I can help. My AI Audit gives you a comprehensive assessment of your firm's AI readiness, identifying the gaps in your governance, the risks in your current tooling, and a clear roadmap to get you where you need to be.

Book a Discovery Call → or learn more about the AI Audit.