Implementing AI in Financial Audits

Editor’s Note:
This article was originally published in 2023 and has been updated to reflect current AI governance, data security, and audit documentation considerations. As AI adoption has matured, audit teams are now focused on controlled deployment, validation, and oversight of AI tools.

Jimmy Bowles, CPA, CIA, CISA, is a senior audit manager at LBMC.

As we navigate an environment with rapidly changing technological advancements, the audit profession is actively integrating artificial intelligence (AI) into audit methodologies to enhance accuracy, efficiency, and insight — while maintaining professional judgment, skepticism, and compliance with audit standards. AI will be used as another tool by auditors as it will increase the ability to automate data analysis, detect anomalies and provide predictive insights.

As technology changes how audit procedures are performed, organizations still need the underlying financial reporting, controls, documentation, and audit evidence necessary to support a high-quality audit. LBMC’s audit and assurance services help organizations navigate financial reporting requirements, audit readiness, internal controls, and increasingly technology-enabled audit environments.

To successfully integrate AI into the audit process, firms need clear policies, procedures, governance standards, and implementation controls. LBMC’s AI and generative AI strategy services help organizations evaluate appropriate use cases, assess data readiness, establish governance, and develop an implementation roadmap aligned with business, security, and compliance requirements.

Originally published in the November/December 2023 issue of the Tennessee CPA Journal.

8 Steps for Successfully Implementing AI in Financial Audits

Step 1: Define Objectives and Goals

It is crucial to clearly define the objectives and goals for implementation of AI use. Auditors will need to define policies on use of data with AI to ensure that data used follows all privacy laws and existing data corporate policies. Identify areas in your audit process where AI can be used to address problems like reducing manual effort, improving risk assessment or enhancing fraud detection. Objectives should align with firm-approved AI use policies, data governance standards, and defined audit methodologies.

Step 2: Assess Data Availability and Quality

Evaluate the availability and quality of your financial data. Ensure that the data is well-structured,  accurate and accessible. Insights from AI algorithms are only as good as the input data. Garbage in, garbage out.

Step 3: Select the Right AI Tools

We must choose AI tools and technologies that align with our audit objectives. Common AI tools available include machine learning algorithms for anomaly detection, natural language processing (NLP) for textual data analysis and predictive analytics for trend forecasting. Auditors will need to work with data scientists and AI experts to ensure that they have the correct tools for audit requirements. 

Selecting technology is only one part of the process. Organizations also need a structured approach to implementing AI tools, including evaluating security, defining ownership, preparing users, testing outputs, and establishing controls before the technology is introduced into critical workflows.

Step 4: Data Preprocessing and Cleansing

Data will need to be preprocessed to ensure meaningful results. Data cleansing is the process of detecting and correcting corrupt or inaccurate records from a data set. This involves removing duplicates, correcting errors and standardizing data formats.

Step 5: Integration With Audit Process

Auditors will need to identify points in the audit process where AI can add value such as data analysis, risk assessment and fraud detection. AI will help automate, accelerate and enhance the audit process by allowing auditors to obtain evidence over larger and more complex sets of data, as well as removing time-consuming tasks that auditors must complete. This will allow auditors to apply more valuable skills to other areas.

Technology may change how audit procedures are performed, but organizations still benefit from completing the fundamentals of audit readiness before fieldwork begins. This includes preparing schedules and supporting documentation, reconciling accounts, documenting internal controls, and coordinating requests with the audit team. Organizations preparing for an upcoming engagement can review how to prepare for a financial statement audit for additional audit-readiness guidance.

These efficiency gains reflect the broader AI accounting benefits and risks organizations must consider. AI can reduce manual work, analyze larger data populations, and identify unusual activity more quickly, but its outputs still require professional review, secure data handling, and clearly defined limits.

Step 6: Monitor and Refine

AI performance requires continuous monitoring and validation. Audit teams should implement controls over AI outputs, including exception thresholds, secondary review, and documentation of how outputs were corroborated with audit evidence. AI outputs may include “hallucinations,” confident but unsupported results, which reinforces the need for professional skepticism, independent verification, and clear documentation of how conclusions were reached.

The same balance between innovation and oversight applies more broadly to AI in accounting. Organizations must evaluate not only where AI can create efficiency or financial value, but also how data quality, governance, accuracy, and accountability affect the reliability of AI-supported decisions.

Step 7: Enhance Auditor Skills

Usage of AI by audit teams is meant to supplement and not replace auditors. Training of audit teams is crucial to understanding the capabilities and limitations of AI audit tools. Auditors will need training in how to interpret insights, validate results and make informed decisions based on AI recommendations.

Step 8: Ethical Considerations and Transparency

Audit teams need to maintain transparency in AI usage. They need to ensure that AI results are explainable and free from biases. Auditors also need to adhere to guidelines and regulatory requirements to keep trust and credibility. Audit teams must also consider AI governance, transparency, and accountability. AI-driven analyses should be explainable and traceable, allowing auditors to understand how outputs were generated. Firms should align AI use with professional ethics standards, independence requirements, and emerging regulatory guidance to maintain audit quality and stakeholder trust.

AI Governance, Validation, and Audit Evidence

As AI becomes more embedded in audit workflows, firms must ensure that outputs meet audit evidence standards. This includes validating results against source data, documenting how AI was used, and maintaining reproducibility of outputs where possible.

Audit teams should treat AI-generated insights as indicators or leads that require further investigation, not as standalone evidence.

Using AI in Financial Audits Responsibly

The use of AI in financial audits has moved beyond experimentation and into controlled, real-world application. Audit teams are no longer asking if they should use AI, but how to use it responsibly while maintaining audit quality, independence, and compliance with professional standards.

AI can enhance efficiency, expand data analysis, and surface insights faster than traditional methods. However, it must be implemented within a framework that emphasizes governance, data security, validation, and documentation. Outputs should always be treated as decision-support tools and must be reviewed, corroborated, and supported by sufficient audit evidence.

As firms continue integrating AI into audit workflows, the focus should remain on balancing innovation with control—ensuring that technology enhances, rather than compromises, audit integrity.

If your organization is exploring how to incorporate AI into its audit process or strengthen existing controls, LBMC can help. Our Data and AI services help organizations assess data readiness, identify practical AI use cases, establish governance, and implement solutions responsibly.

LBMC also provides financial statement audit services, allowing organizations to approach AI adoption with a clear understanding of audit quality, compliance, data security, and professional standards.