The New Wave of AI-Driven Audits
Artificial Intelligence (AI) is the pivot of modern technology that is transforming industries. And it’s no surprise that the auditing sector is also being revolutionised by AI capabilities. Investing in AI is no longer about tech advancement; it's twofold:
- Improved quality through advanced capabilities helps anomaly and fraud detection, and risk assessment.
- Enhanced efficiency through the rapid processing of large datasets provides insights faster than traditional methods.
The Broad Spectrum of AI's impact
Artificial Intelligence has ushered in a transformative phase in auditing. With the capability to process copious amounts of both structured and unstructured data, including text and images, AI augments the precision and efficiency of the auditing process. Specifically, AI's advanced pattern analysis is invaluable for anomaly detection, as it can deftly identify discrepancies within extensive datasets.
The integration of AI in auditing will transform the role of auditors, enabling them to adopt a more strategic approach and predict potential issues before they arise. Additionally, it will improve responses in areas that require professional judgment. However, this labour shift might take a few years to manifest, and the full realisation of these efficiency gains might be a long-term result. This is because such a shift will require the development of both the AI capability and human resources competence in areas of judgement. Data analytics in auditing involves discovering patterns, identifying anomalies, and extracting insights from data through analysis, modelling, and visualisation. This helps enhance understanding of an entity's operations and risks. (AICPA 2017).
Modern AI technologies contributing to enhanced audit efficiency
Natural Language Processing (NLP)
Natural language processing helps machines to interpret and understand human language so that they can automatically perform repetitive tasks. Auditors can employ NLP to analyse qualitative data, such as the content in annual reports, emails, contracts or agreements, to ensure compliance or identify patterns indicative of fraudulent behaviour.
Example: Extracting key themes from the management’s discussion or analysis sections of annual reports to identify discrepancies or anomalies.
Robotic Process Automation (RPA)
RPA, one of the specific applications for AI algorithms, allows the automation of repetitive tasks using software robots. (Fedyk, A., Hodson, J., Khimich, N. et al. (2022). It can be used to automate routine data collection and initial stages of data analysis.
Example: Automating the extraction of transaction data from an enterprise resource system for sampling and initial scrutiny.
AI Algorithms can also be used for data analytics, predictive modelling, and anomaly detection. Specific algorithms can be run on financial data to predict potential defaults or to identify unusual transaction patterns indicative of fraud.
Cognitive Automation
Cognitive automation in auditing combines RPA with machine learning to automate more complex tasks that require decision-making, such as classifying transactions, assessing risks, or making predictions about financial health.
Cognitive auditing utilises AI to detect errors in financial reports (Schulenberg 2007). This method, rooted in machine learning, aids auditors in spotting mistakes and anomalies. Gentner et al. (2018) noted that AI accelerates the audit process, helping in error detection and data pattern recognition for predictive decisions. AI-enabled auditing tools often execute intricate audits more efficiently and precisely than humans.
Example: Classifying different types of expenses automatically in a large dataset and assessing if they adhere to company policies and procedures.
Visualisation Tools
These tools allow data to be visualised in graphs, charts, and other visual formats for better understanding. They can help auditors to comprehend vast amounts of data quickly and identify patterns, outliers, or anomalies.
Example: Creating a chart of entity financial transactions all over the year in line with its approved operational plans.
Blockchain
Blockchain, being a decentralised ledger where data is stored in chained blocks, provides a transparent and tamper-proof environment for auditing and transaction records.
Example: While auditing a company that uses blockchain for its supply chain, the auditor can verify the authenticity and integrity of transactions without relying solely on third-party confirmations.
Optical Character Recognition (OCR)
The advent of optical character recognition (OCR) technology, an offshoot of AI, has proven instrumental in simplifying and enhancing contract and lease reviews. It allows the conversion of images or text into machine-encoded text. It can be used to digitise paper records or to extract data from scanned documents.
Example: Scanning and extracting transactional data from paper invoices for incorporation into an electronic analysis system.
Gazing Into the Future
The integration of AI in auditing is more than just a technological shift; it's a transformative approach towards achieving unparalleled quality and efficiency. Firms should stay abreast of these trends, equipping themselves for a future where AI is a crucial tool for an auditor's efficiency in conducting audits with enhanced quality. However, AI remains a tool, and not yet can replace human judgement and expertise, making the success of the audit linked to the synergy between AI and human auditors.