Advanced Auditing, Assurance and Professional Ethics · Digital Auditing & Assurance

Emerging Technologies and Their Impact on Audit

Updated 5 October 2026 · Fact-checked

Emerging technologies such as AI, machine learning, blockchain, RPA, cloud and big data change how an entity processes transactions and how an auditor works. To answer a question, name the technology, state the new risk, link it to risks of material misstatement, then give the audit response and the benefit to the auditor.

Understand Emerging Technologies and Their Audit Impact

Start with one idea: technology changes where data sits, who touches it and how fast it moves. The audit objective does not change. You still need sufficient appropriate evidence to reduce audit risk to an acceptably low level. What changes are the risks you assess and the procedures you choose.

Here are the technologies you must know in plain words.

Artificial intelligence (AI): systems that perform tasks needing human-like judgment, such as classifying documents or predicting outcomes.
Machine learning (ML): a branch of AI where the model learns patterns from data and improves without being given fixed rules for each case.
Robotic process automation (RPA): software bots that follow fixed rules to copy, key in or move data between systems. A bot does not learn. It repeats a script.
Blockchain: a shared ledger where entries are grouped in blocks, linked cryptographically and very hard to alter once recorded. It is often used for contracts, supply chains and digital assets.
Cloud computing: using servers, storage or software hosted by a third party over the internet.
Big data: very large, fast and varied data sets that need special tools to analyse.

Each technology affects the entity and the auditor in two ways. First, it changes the entity's risks. Examples are bot errors, biased models, weak access control in the cloud, and disputes over blockchain records. Second, it gives the auditor new tools. Examples are testing full populations, spotting unusual patterns and automating routine checks.

The link to the Standards is simple. Understanding the entity's use of IT falls under risk assessment (SA 315). Information from systems must be reliable if used as evidence (SA 500). If the entity uses a service provider such as a cloud vendor, you consider the controls at that provider. Documentation and professional skepticism apply as always. Never say technology removes the auditor's judgment or responsibility.

The difference between ML and RPA is a favourite exam point. RPA follows rules you define, and it is good for repetitive, structured tasks. ML finds patterns from data, and it handles unstructured or uncertain tasks. An RPA bot posting journals is a control and risk item. An ML model flagging odd journals is an analytical tool.

Key rules to remember

Audit risk model: Audit risk = Risk of material misstatement × Detection risk; Risk of material misstatement = inherent risk combined with control risk. Audit risk is set at an acceptably low level. Technology can change the assessed inherent and control risk, and detection risk is then set in response to the assessed risk of material misstatement. Technology does not change detection risk by itself.
RPA vs ML: RPA = rule-based, repeats a script; ML = data-based, learns patterns; Use this one-line contrast in any comparison question.
Answer structure for technology questions: Technology → Risk → Effect on RMM → Audit response → Benefit; A memory chain to keep the answer complete and in order.

How to solve Emerging Technologies and Their Audit Impact questions

Use this method for any question on how a technology affects an audit, whether it is theory, a case scenario or an MCQ.

1Identify the technology in the question and define it in one line. Do not skip this; it earns the first marks.
2Locate where it sits in the entity: transaction processing, record keeping, reporting or control activity.
3State the specific risks it creates, such as errors in bot logic, unauthorised access, model bias, loss of data control or unclear ownership of records.
4Link each risk to the risk of material misstatement and to the relevant assertion or financial statement area.
5Give the audit response: understand the IT environment, test general IT controls and application controls, check reliability of system information, involve an IT specialist if needed, and consider service provider controls.
6Add how the technology helps the auditor, for example full-population testing or anomaly detection, with the caveat that the output still needs validation.
7Conclude with the effect on the audit approach, such as changes in nature, timing and extent of procedures, and keep documentation and skepticism in view.

Quickest way: Risk-Response-Benefit in three lines

When to use it: Use when time is short, in MCQs, or for a 4 to 5 mark short note.

Write the definition in one sentence.
Write two risks and tie each to misstatement or control failure.
Write two audit responses and one benefit, then stop.

Common mistakes in Emerging Technologies and Their Audit Impact

Saying RPA and machine learning are the same thing.
Both are called automation, so students treat them as one idea.
Fix: Say RPA follows fixed rules and does not learn. ML learns from data and its outputs can change as it learns.
Writing that technology reduces the auditor's responsibility or removes the need for judgment.
Students assume automated outputs are reliable by default.
Fix: State that responsibility for the opinion stays with the auditor. Outputs from AI tools or system reports must be validated before they are used as evidence.
Listing benefits of a technology without any risks.
Technology is seen as an improvement, so the audit angle is forgotten.
Fix: Always give risks first. The exam asks for audit impact, which means risks and procedures.
Claiming blockchain records are always accurate because they cannot be changed.
Students confuse immutability with truthfulness.
Fix: Explain that a blockchain preserves what was recorded. It does not prove the underlying transaction was real, valued correctly or authorised. Input and governance risks remain.
Ignoring the cloud provider when the entity's data is hosted outside.
Students focus only on the entity's own controls.
Fix: Mention reliance on controls at the service provider, the need for a service auditor's report where available, and access, backup and data location risks.
Giving a long general essay on digital audit with no link to risk assessment or evidence.
The topic feels descriptive, so students write everything they know.
Fix: Tie each point to risk assessment, controls or audit evidence, and use the structure Technology, Risk, Response, Benefit.

Worked examples

Example 1

A retail company has deployed software bots that post vendor invoices from emailed PDFs into the accounting system and release payments below a set limit. As the auditor, explain the risks this creates and how you would respond.

Show the solution

Technology: this is RPA, which follows fixed rules to key in and process structured data.
Risks: the bot logic may be wrong or outdated, so errors repeat on every invoice. Bot credentials may give excess access. Duplicate or fictitious invoices may be paid if validation rules are weak. Changes to the bot may go untested.
Link to misstatement: this affects occurrence, completeness and accuracy of purchases and payables, and creates a fraud risk through payments released automatically.
Response: understand the process and the bot's rules, and test change management and access controls over the bots. Re-perform the bot's logic on a sample, and check duplicate-invoice and payment-limit controls.
Reliability: if you use system reports of bot activity as evidence, test their completeness and accuracy.
Benefit: since the bot is consistent, if its controls are effective, testing a small number of transactions can give evidence about the rest, provided general IT controls over the bot, such as change management and access, are effective throughout the period. Ineffective controls call for more substantive procedures.

Answer: The bot is a rule-based automated control and also a source of risk. Assess risks of repeated errors, excess access and unauthorised payments, test the bot's logic, change management and access, and extend substantive testing if the controls fail.

Example 2

An entity stores its ledgers on a public cloud platform and uses a machine learning model to flag unusual journal entries for management review. The auditor wants to use the model's flagged list in the audit. Explain the considerations.

Show the solution

Cloud: data is held by a third party, so consider access control, backup, data location and the provider's controls. Ask for the provider's service auditor report if available.
Machine learning: the model learns from data and may be biased by poor training data or may miss new fraud patterns. Its results are not fixed.
Reliability of information: before using the flagged list as audit evidence, test the completeness and accuracy of the data fed into the model and understand how the model is validated and changed.
Use as a tool, not a conclusion: treat flags as indicators. Follow up flagged journals and also test journals not flagged, since the model may miss items.
Skepticism and documentation: record the model's purpose, the checks performed and conclusions. Consider an IT specialist for complex models.
Responsibility: the auditor remains responsible for the opinion and cannot rely on the model alone.

Answer: The auditor can use the flagged list only after validating the data and the model's reliability, considering cloud provider controls, and supplementing it with own journal testing. The model supports judgment but does not replace it.

Exam tips

Expect a case scenario naming one technology. Spend your first line defining it, then go straight to risks and responses.
For comparison questions, use a short two-column style in bullets: rule-based or learning, structured or unstructured data, fixed or changing output.
Always mention reliability of information and the auditor's retained responsibility. These points are easy to miss and easy to score.
For MCQs, reject options that say technology eliminates risk, replaces professional judgment or makes records automatically correct.

Practice questions from Digital Auditing & Assurance

Emerging Technologies and Their Audit Impact in other exams

The same ground in other exams, if you are preparing for more than one or want another angle on it.

Emerging Technologies and Their Audit Impact: frequently asked questions

What is the difference between machine learning and RPA in audit?

RPA follows fixed rules to perform repetitive tasks and does not learn. Machine learning finds patterns in data and improves with more data. In audit, RPA is often an entity process or control to be tested, while ML can be an analytical tool to flag unusual items.

How does RPA help auditors?

Auditors can use bots for repetitive work such as extracting data, matching records, preparing confirmation requests or reperforming calculations. This saves time and reduces keying errors. The auditor must still design, review and validate what the bot does.

What are the main audit risks of blockchain?

The records are hard to change, but they may still be wrong if the input was wrong or the transaction was not genuine. Other risks include governance of the network, key management, smart contract errors and unclear legal ownership. The auditor must understand these before relying on the records.

How does cloud computing affect an audit?

The entity's data and systems are held by a third party, so the auditor considers the provider's controls, access management, data security and backup. A service auditor's report may give evidence about these controls. The entity remains responsible for its financial reporting.

Does AI replace the auditor?

No. AI can speed up analysis and flag exceptions, but the auditor must validate the outputs and apply professional judgment and skepticism. Responsibility for the audit opinion remains with the auditor.