AI's Game-Changing Role in Finance and Audit Processes

With the rapid progression of AI technologies, finance professionals are faced with both opportunities and challenges. On one hand, AI offers the promise of increased efficiency, accuracy, and insights through automation and data analysis. On the other hand, there are concerns about data privacy, biases in algorithms, and the need for ethical considerations in AI implementation.

In an interview, Rahul Chowdhary, Partner at Deloitte India, offers a compelling exploration into the disruptive force of Artificial Intelligence (AI) within the realms of finance and audit. Delving deep into its transformative potential, Chowdhary navigates through the landscape of AI adoption, addressing critical questions, challenges, and ethical considerations. As he unveils AI's pivotal role in detecting financial irregularities, providing real-time insights, and streamlining audit processes, the conversation resonates with the urgency of responsible innovation in the ever-evolving digital age. Excerpts:

Have finance professionals started using AI?

As finance professionals, we have witnessed the transformative impact of AI in the field of finance especially in areas like Audit & Accounting. Auditors and their clients are using next-generation technologies more than ever. For many auditors, using automation and analytics is a first step in their digital journey towards an AI-enabled audits. AI is increasingly deployed into audit processes, where it efficiently analyses vast volumes of financial data to identify deviations and potential risks of fraud. For instance, AI algorithms can detect irregularities in financial statements, flag suspicious transactions, and automate compliance checks, thereby enhancing the accuracy and effectiveness of audits.

Can you elaborate on specific types of irregularities AI can identify in financial data?

Let’s define financial data. It refers to any piece of information that helps to provide clear insight on financial performance of an organisation or business to its various stakeholders – investors, regulators, customers, employees, vendors, etc.

AI-driven analytical techniques enable finance professionals and auditors to uncover hidden patterns, trends, and irregularities in financial data that may go unnoticed through traditional audit methods. AI empowers businesses to enhance financial transparency, mitigate risks, and maintain regulatory compliance in an increasingly complex and dynamic financial landscape. Following are some types of irregularities AI can identify in financial data of an organisation.

Accounting Errors: AI tools can be used to analyse the financial data, for detecting irregularities or errors. Auditors can use these AI tools and technology to save their time and utilise their efforts on critical areas that requires human intervention. Areas prone to errors and involving significant judgment are great use cases – Provisions and accruals, Calculation of rebates and discounts, Sales incentives, etc. 

Auditors can perform entire recalculations and reperformances with just a few clicks. Let’s take Fixed assets. Considering the voluminous records in organisations' fixed asset registers, auditors can deploy AI for accurate classification and componentisation of fixed assets, recalculating appropriate depreciation, and deriving net asset value, to reconcile the numbers with the organisation’s fixed asset register.

Forecasting Errors: Organisations and auditors can use it to forecast the future numbers. These applications can process and analyse organisations’ current and earlier performance to generate patterns and metrics that helps in forecasting organisations’ future results, and ultimately in finding the irregularity from the expected results.

For e.g., Advanced AI applications now enable auditors to generate future cash flow projections and compare them with those provided by the organisation’s management, facilitating the identification of any deviations. This enhanced capability empowers auditors to conduct more comprehensive analyses, thereby enhancing the accuracy and thoroughness of financial audits.

Fraudulent Transactions: AI tools can scrutinise accounting data to identify patterns and anomalies that suggest potential fraudulent activities, such as transaction fraud, bogus bills, or cash embezzlement. Let’s take an example of  payment aggregators. They process millions of transactions daily, which makes them a prime target for cybercriminals. To combat fraud effectively, they should employ AI with advanced machine learning algorithm that analyses vast amounts of transaction data in real-time. AI system continuously monitors transactions as they occur. It analyses various factors, including transaction amount, location, payment device used, and the user's historical transaction patterns. Sophisticated machine learning techniques enable AI systems to build models of normal user behavior. It then compares each new transaction against these models to identify material inconsistencies or deviations that may indicate fraudulent activity. And if a user suddenly makes a high-value purchase from a different location without prior history of such transactions, it could trigger an alert and would require IVR authorisation to proceed with the transaction thus avoiding any fraudulent transaction from such an account.

Artificial Intelligence (AI) emerges as a transformative force, revolutionising traditional approaches to data analysis and risk assessment. From identifying inconsistencies in financial statements to pinpointing suspicious transactions, AI enhances the precision and efficiency of audits, thereby bolstering the integrity of financial reporting processes.

What does all of this mean for the role of the auditor? How does AI provide real-time insights that free up auditors for more strategic work?

By automating routine tasks and streamlining data analysis processes, AI frees up auditors from manual time-consuming activities, allowing them to focus on high-value activities such as interpreting findings, developing strategic recommendations, and providing valuable insights to stakeholders.

Real-Time Data Analysis: AI algorithms can quickly analyse complete sets of data, empowering auditors to instantly identify deviations and patterns that indicates potential risks because this capability enables auditors to proactively address deviations in real-time. For example, AI can continuously monitor journal entry postings (concurrent audit case). If journal entry imbalances are detected, the system can trigger an immediate alert, prompting auditors to investigate transactions immediately and take necessary action.

Also, if there are any high value payments or receipts involving related parties or irregular parties, the system can flag such transactions on an immediate basis. This methodology improves the audit performance by enabling audit staff to process issues quickly and effectively mitigate risk.

Natural Language Processing (NLP) for Document Review: AI-powered NLP algorithms streamline data extraction for auditors by consolidating information from diverse sources such as invoices, bank statements, and legal documents into a single spreadsheet. This consolidated data facilitates efficient analysis and comparison, saving auditors valuable time that would otherwise be spent compiling data from multiple sources.

Predictive Analytics: AI uses predictive analytics to analyse historical data and existing market conditions to forecast upcoming trends, outcomes, and risks. It can accurately forecast future cash flows, revenue forecasts, or inventory levels, and help auditors evaluate forecasts against past and ongoing data.

By comparing projected data with actual results, auditors can easily identify any discrepancies and investigate the underlying reasons for deviations from the AI forecasted numbers. This proactive approach empowers auditors to uncover potential fraud or irregularities, enabling timely intervention and risk mitigation measures.

The Institute of Chartered Accountants of India (ICAI) established a Center of Excellence (COE) on AI. How can such centers promote AI adoption within the accounting and auditing profession?

The adoption of AI extends beyond private sector entities to encompass regulators, trade bodies and public institutions as well. Many public institutions and bodies are also taking steps to implement AI and raise awareness about its potential among the public. Initiatives such as the establishment of the COE on AI by the ICAI serve as catalysts for promoting AI adoption within the accounting and auditing profession. In future, this center may serve as a hub for innovation, research, and knowledge dissemination, fostering collaboration among industry professionals, academics, and technology experts to harness the full potential of AI in enhancing audit quality and efficiency. 

Data privacy and regulations pose significant challenges. Can you elaborate on how these challenges can be addressed to ensure successful AI implementation?

The successful embrace of AI in auditing is not without its challenges. These include potential biases in AI algorithms, privacy concerns regarding the collection and use of personal data, and the possibility of AI systems perpetuating or amplifying existing inequalities or discrimination. Ignoring data privacy issues can have serious repercussions. While using AI, the applicable country wise codes (like General data protection regulation (Europe); California consumer privacy act (US); Personal information protection and electronic documents act (Canada)) for data privacy should be adhered.

The foundation of our profession is trust. Without it, we have no means of acting as an objective, independent intermediary between information producers and information consumers. Auditors need to consider the risks associated with data and privacy, and design security protections that match the sensitivity of the data.

Auditors must establish robust validation processes to verify the reliability and accuracy of AI-generated insights, mitigating the risk of erroneous conclusions or biases inherent in algorithmic decision-making. Data privacy regulations, compliance requirements, and ethical considerations must be carefully navigated to ensure the responsible and ethical use of AI technologies.

Some measures that organisations can take:

Inhouse AI integrated software: Organisations can develop inhouse AI integrated softwares, through which organisations can significantly reduce the risk of data leakage by imposing various restrictions and rights on the data accessibility. It also undergoes regular security checks and penetration testing to identify and address vulnerabilities before they can be exploited by malicious bugs.

Crafting an ethical AI framework: Given the early stage of AI tools, they are currently in a self-training phase. Therefore, organisations should focus on developing AI for ethical purposes. This involves identifying guiding principles and values for AI to ensure it produces ethical outputs. Such a framework can serve as a compass to ensure that AI upholds core values such as fairness, transparency, accountability, and exposure.

Whitelisting/Blacklisting: Organisation can utilise whitelisting or blacklisting practices on its VPNs to control which IP addresses or domains are allowed or blocked from accessing the website. By implementing these measures, organisations can effectively restrict the share of the data to unauthorised users and minimise the privacy challenges.

To fully realise the potential benefits of AI while mitigating these risks, it is crucial to take certain essential steps. First, thoroughly test AI systems to identify and address errors or biases. Second, ensure that the data used by AI is accurate and reliable. Third, prioritise transparency in the development and deployment of AI systems. Fourth, monitor AI systems continuously to verify their performance. Lastly, provide ongoing training and education to all stakeholders to promote responsible and effective use of AI. By adhering to these steps, we can ensure that AI works for us rather than against us.

While AI offers advantages, there are inaccuracies and risks as well. What mechanisms can be put in place to detect and mitigate such risks?

Auditors can face several risks when using AI. These risks include over-reliance on AI-generated insights, potential biases and quality issues from incomplete or poor-quality data andcybersecurity threats such as consequences in terms of hacking of the confidential data from the AI websites. Thus, it is necessary to ensure compliance and implement safeguarding measures.

Following are some of the possible measures that can be implemented to mitigate the above-mentioned risks.

Human judgement: While AI is a great tool to be incorporated in the professional world to help auditors and organisations streamline their existing processes, AI work on standard algorithm that can’t be customised on case-to-case basis. Therefore, to ensure the accuracy of the results, a human review can be placed in practice to review from and validate the accuracy of output results.

Updating back-end algorithms: The better the algorithms, the better the results. Regular updates to the back-end algorithms can yield more accurate and improved outputs, adapting to  changing scenarios and data formats, ultimately mitigating the risk of incorrect or inaccurate results..

Security controls over AI: Despite its numerous advantages, privacy remains a significant concern with AI. Auditors may hesitate to use AI due to fears of data leakage and privacy breaches, which could lead to legal repercussions and reputational damage for their organisations and the profession. To address these concerns, organisations developing AI solutions must prioritise stringent security measures. This includes safeguarding individuals' private information through strong encryption, anonymising data, and controlling access to sensitive information.

In conclusion, AI presents immense possibilities for enhancing our lives and businesses. Its transformative potential to revolutionise industries, boost productivity, and solve complex challenges is undeniable. However, ethical concerns regarding privacy and bias demand careful consideration and proactive measures. Moving forward, we must embrace AI responsibly, ensuring its development and application adhere to ethical principles and societal values. Navigating the evolving AI landscape requires a balanced approach that combines innovation with responsibility, empowering AI to act responsibly for the betterment of humanity.