The Impact of Integrating Artificial Intelligence in Auditing

As a consequence of artificial intelligence, auditors can perform their duties more efficiently and cost-effectively, and their work will be of higher quality.

FREMONT, CA: A major impact of AI is that it is a source of risk in and of itself, as audit clients utilize AI in their operations. This can result in financial and operational risks due to data breaches, inappropriate data usage, and reputational risk due to AI biases. This vector of influence has been the subject of some research, mostly from an internal auditing standpoint. However, such topics still need to be explored from an external auditing standpoint.

Human intelligence and skill may then be applied to higher-value processes. It also enables more detailed data analysis by automatically evaluating all accessible data rather than sampling. Auditors will obtain a better knowledge of an organization's risk and will be able to focus on high-risk documents and anomalies.

Automated data processing: Auditors acquire evidence concerning corporate processes from various sources, including process documentation, invoices, system logs, and reports, whether the audit is internal or external. When done manually, gathering data from unstructured sources is time-consuming. Intelligent automation solutions can read and comprehend the context of documents using natural language processing and intelligent document processing technology. This implies that auditors may automate acquiring evidence from sources like emails, papers, and reports, freeing them up to focus on more complicated activities like data analysis and interpretation. Automation also reduces the need for human data entry, which saves time and money.

Finding discrepancies: Besides analyzing and visualizing statistical data, intelligent bots can use machine learning algorithms to analyze obtained data and discover abnormalities such as probable fraud or suspicious IT logs based on predefined criteria. Auditors can focus on high-risk locations throughout the population by noting these irregularities. Furthermore, AI-enabled bots may learn and adapt to datasets, increasing anomaly detection accuracy. Auditing is a subject of expertise frequently highlighted as ripe for automation at all operations utilizing artificial intelligence technologies. Most firms in the auditing industry use artificial intelligence (AI) to analyze massive datasets. AI in the context of big data is constantly advancing; small data solutions remain uncommon. However, most businesses in the world employ fewer than 50 employees and, as a result, can seldom automate with big data.

Keeping track of interviews: Auditors perform many interviews and meetings when preparing and conducting an audit. One kind is management interviews, which are used for risk assessments. Another type is auditing team meetings, which are used to plan the audit; the third type is interviews, which are used to confirm practices or transactions, clarify details, and present results. Today, these interviews are rarely documented, notes are kept in local repositories, and expertise needs to be shared among audit teams. AI might be used to record and evaluate client-related spoken conversations. The AI might build a central knowledge store regarding the client's risk profile, company activities, and linked parties.