AI in auditing: Benefits and applications to know

/ Posted By - Bradleys Accountants / Categories - Business start-ups

With Artificial Intelligence (AI), organisations can process large volumes of data more efficiently than ever before. This has profound implications for all kinds of business activities, and auditing is no exception.

This function is ideally suited to AI intervention in many ways, owing to the non-negotiable need for accuracy and oversight over the smallest details. Let’s take a closer look at the benefits and applications of AI in accounting and auditing.

Why use AI in auditing

1. Make repetitive tasks a breeze

Much of auditing work involves mundane tasks like data entry and verification, documentation, routine calculations, compliance checks, and so on.

Trained AI algorithms can handle these activities in minutes or less, and since they usually follow the same patterns repeatedly, very little human oversight is needed. Plus, there’s no risk of human error that can emerge from the fatigue of performing repetitive tasks repeatedly.

2. Spot fraud easily

Techniques like Machine Learning (ML) are adept at flagging suspicious activities in real-time and are thus often used in the financial services sector to monitor transactions.

This capability can also be applied when auditing a company’s financial records or analysing meeting notes for inconsistencies.

For instance, accountants use ML algorithms to detect unusual patterns in financial statements that could indicate fraudulent activities, such as vendor payments or expense report anomalies.

3. Get a clear picture of risks

AI can study historical data on risky events and fraud incidents and deliver insights on how businesses can safeguard against such incidents in the future. Moreover, it can use the data to detect patterns indicating future system vulnerabilities and fix them in advance.

For example, auditors use AI to analyse past audit outcomes, identifying trends that could signal potential risks in current financial operations and allowing them to take preemptive measures.

4. Save money big time

With AI handling audit processes, companies can hire smaller audit teams and redirect their resources to more meaningful activities. Plus, AI helps identify risks early and thus protects the business from financial losses.

For instance, an accountant might implement AI to automate routine audit tasks, significantly reducing the time and cost associated with manual audits while also enhancing accuracy and efficiency.

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    Applications of AI in auditing

    Let’s talk about some of the concrete use cases of AI in the audit process.

    1. Proactive auditing with predictive analytics

    AI uses predictive analytics to study historical data and foresee potential discrepancies/anomalies before they become bigger problems. This allows auditors to proactively solve issues, safeguarding the business against future losses and improving its trustworthiness.

    For instance, AI can study years’ worth of data related to revenue recognition issues at a financial services company and identify signs that a transaction is deviating from regular patterns. This will allow the company to flag the client immediately rather than discovering the deviation later.

    2. Smarter auditing with cognitive technology

    There are standard auditing tools, and then there are cognitive tools that can uncover powerful insights from even more subtle indicators.

    AI-powered cognitive technologies can understand and interpret the relationships between various indicators, such as the sentiment and tone in communications and financial narratives, to flag things that might require attention.

    For instance, AI could flag tone shifts in emails exchanged between a company’s finance department and external clients that could indicate concerns about the client’s ability to repay loans. Human auditors can then study those accounts more carefully and make extra provisions for bad debt, if necessary.

    3. Streamlined compliance with automated checks

    Financial regulations constantly evolve, and it can be hard for a company to keep pace with them. AI, in this context, can automatically stay updated on the latest changes and run checks on the company’s financial documents to ensure full compliance.

    For instance, AI can examine client documents against the latest financial regulations and flag any areas that need attention, such as new disclosure requirements or changes in the eligibility criteria for tax exemptions.

    This protects the company from running into legal hassles and having to fork out penalties later.

    4. Securing data integrity with Blockchain integration

    Integrating AI with Blockchain technology creates a permanent, unalterable audit trail – a game changer when handling financial information.

    With Blockchain in place, any attempt to edit data is flagged, and given the decentralised nature of Blockchain, no one party can manipulate it for personal gain. This major benefit improves the company’s integrity and stakeholder trust.

    Companies of all sizes can benefit from it – and in particular, multinational companies maintaining ledgers across borders can use it to keep track of any exchange rate manipulations or unauthorised transactions,

    Industry-specific examples of AI for auditing

    AI models can be trained to meet industry-specific auditing needs. This ensures that companies get the most benefits from their AI investment and makes fitting into the existing audit framework easier. Some real-world applications include:

    • Inventory audits and supply chain financial health at a manufacturing company
    • Compliance verification and loan disbursal audits at financial services firms
    • Patient history tracking and auditing records at a healthcare company
    • Sales activity and inventory monitoring at a retail company

    In conclusion

    While the human perspective of experienced auditors has no substitute, AI can do much of the heavy lifting regarding repetitive processes and data crunching.

    Therefore, going forward, we are likely to see the auditing function take on a collaborative shape, where AI-powered efficiency and human endeavour come together to deliver optimal results.

    There’s also the need to ensure that AI algorithms are fed the right data to avoid biases—again, human oversight plays a critical role.

    As business becomes more complex, the need for accurate audits is greater than ever to ensure companies are doing right by their stakeholders and customers. AI’s future in auditing is bright, and it’s only the beginning of its applications.

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