Internal auditing has become critical for ensuring financial integrity, operational effectiveness, and regulatory compliance. Businesses work with complex financial systems, and traditional audit methods are not up to the mark. This is why AI is considered an alternative to transform internal audit phases, from initial risk assessment to final report generation.
Do you need help with identifying patterns, anomalies, and potential risks? AI-powered tools deal with that and help companies shift from periodic reviews to continuous ones.
This might sound like a sweet deal, but it has its challenges. People are concerned about bias in AI models and the need for human intervention remains significant. This blog will explore how AI can benefit accounting and finance audits and discuss how to strike the right balance between two opposing worlds.
Why do companies use AI for internal audits?
Avoiding non-compliance
Regular auditing is a fundamental process that indicates financial integrity and operational effectiveness in the business world. Aside from verifying financial records, intern audits build a degree of trust among stakeholders. The reports created during the process help companies comply with the regulatory bodies. An auditor must report the irregularities and liabilities, as failure to do so will result in legal activities.
Nowadays, companies are using AI to manage all the phases of the internal audit process. This begins with audit risk assessment and ends with report generation. Yes, AI can understand and flag the deviation, but that’s not the only application. With AI tools, companies can stay updated on regulatory changes. Auditors can use the updated information to remain compliant and avoid potential risks. This helps to address the problems before they become a regulatory concern.
Audit Optimization
AI plays a significant role in optimizing internal audit processes in terms of quality and coverage. We know that this technology works best with large volumes of unstructured data. But that’s not the end; AI can understand the scope and context. It immediately widens the foundation, removing performance-related constraints. It streamlines the time-consuming processes, leaving auditors to focus on more critical tasks.
AI equips auditors with data on which areas need attention from top management and which do not. This is a far better approach than the hit-and-miss method, where they have to unthinkingly guess the areas of concern. Data-driven insights are also crucial in tailoring the department/ services according to market analytics.
Companies should be generous with human expertise because people are bound to make mistakes. They can overlook some data, missing columns, or put the same data twice, affecting the decision-making process. Lastly, AI can significantly reduce the financial expenses of internal audits.
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Key Areas Where AI Is Being Used In Audit Lifecycle
Risk Assessment
In the early days, auditing tasks were time-consuming, and auditors couldn’t reach all the stakeholders. The data collection method was preliminary and did not incorporate all the financial metrics. But now, this process has transformed from in-person interviews to self-assessment portals.
However, AI helps auditors make the risk assessment process more subjective and catered towards the business. They can use prompt engineering to enlist the risks the company might encounter in a specific department. Or they can use AI to scrutinize large datasets to get insights about patterns and trends. This data can help them identify potential risks and what are the areas of concern. This technology can also tell which issues should be immediately dealt with or those that can be dealt with later.
Audit Planning and Monitoring
Internal audit planning is a meticulous process with multiple rounds, especially in complex organizations. AI can simplify the process and help auditors create a first draft incorporating all the data. The technology can collect data, scrape through risk assessment figures, and produce a feasible plan.
The internal audit team can feed availability data into the system. Using the data, the system will generate an appropriate schedule, thus taking the chore from the corporate team. They can also use the system to monitor financial transactions and check for potential risks continuously. With AI, the focus shifts from periodic scrutinies to continuous audits, leading to better decision-making.
AI and Fieldwork optimization
AI systems can monitor financial activities in real time and quickly flag discrepancies, if any. They can automate the process, minimize human errors, and deliver the project within a specific time. It improves precision and accuracy by decreasing the chances of human oversight.
Fraud Detection
AI audit software uses Machine Learning algorithms to analyze potential contract fraud or irregularities. These algorithms are adept at adapting and understanding slight deviations from typical behavior. In some cases, human auditors can overlook these issues. By adopting this proactive approach, companies can address and mitigate potential frauds before running into significant financial and legal problems.
Reporting & Analysis
In the pre-AI days, audit report generation was a complex process requiring manual effort. Using AI, companies can collect, study, and preview audit findings in a digestible format, ensuring report quality and accuracy. These tools simplify and speed up the process and help auditors make accurate decisions.
Use cases of AI in accounting and finance
KPMG
KPMG integrates AI to enhance transparency and real-time monitoring during the internal audit process. It enables auditors to continuously assess financial transactions, identifying risks as they occur rather than relying on periodic manual reviews. The platform’s predictive analytics help auditors detect anomalies early, allowing quicker response times and reducing audit timelines. AI can also enhance the audit trail, offering comprehensive documentation and insights that improve decision-making, accuracy, and compliance.
PwC
PwC’s AI-driven platform automates reviewing financial transactions within a company's general ledger. It uses machine learning algorithms to scan millions of transactions and identify outliers or unusual patterns that may indicate fraud or errors. The tool improves accuracy by continuously learning from past data, enabling it to spot irregularities faster than traditional methods.
Deloitte
Deloitte uses powerful AI audit software to streamline auditing by scanning vast amounts of contracts and documents for anomalies. It can detect non-compliant language, missed deadlines, and deviations from contract terms, which may go unnoticed in manual audits. By automating contract review, Argus helps reduce human errors and boosts the efficiency of audit teams.
Are There Any Risks Associated With AI in Internal Audits
AI models can be exposed to human biases and produce erroneous results. We have seen how AI in the recruiting industry has shown gender and racism-related biases against women. This has been caused by embedding wrong logic, massive oversight, and a lack of testing. If companies are using AI for audits, they have to eliminate these issues.
Otherwise, the team may have to deal with financial and reputational damage. The technology is not there yet, so human discretion is still needed. When showcasing goodwill, no one can do it better than humans.
AI during internal audits can play a vital role in streamlining processes, boosting accuracy, and mitigating risks. For these reasons, we have seen a trend where companies heavily invest in AI audit tools. This software can be used in different phases: risk assessment, fieldwork optimization, fraud detection, and report generation. We know that AI can use vast data to identify deviations, making the process entirely reliable and efficient. It is a vital instrument to ensure that the company remains transparent and compliant during the audit and maintains assets and reputation.
Chirpn has integrated AI into its audits, a paradigm shift from manual processes to automation. This technology enhances the scope and accuracy of auditing while removing the bottlenecks. Natural Language Processing can perform an exhaustive analysis of relevant data, and identify the subtle areas of concern. By using this technology, Chirpn has stayed vigilant and improved stakeholder confidence.
Chirpn, an AI-first company, helps businesses plan and implement AI solutions in their operations. We help departments to optimize operations, enhance decision-making, and streamline processes. With its AI-driven solutions, Chirpn assists businesses in identifying opportunities, automating repetitive tasks, and making data-driven decisions. If you are considering similar technologies, Chirpn can take care of the process, from planning to execution. We maintain a balance between technology and human expertise, meanwhile adding value to the process.
