CMA Hrishikesh R Nampoothiri

The world is evolving at an unprecedented pace. What we witness and experience today were once mere dreams or figments of imagination a decade ago. This rapid transformation underlines the need for continuous adaptation. Among the most revolutionary developments shaping our future is Artificial Intelligence (AI)—a technology that organizations must acknowledge and integrate to thrive in the new digital landscape.

AI, or Artificial Intelligence, is a branch of computer science focused on creating machines and software capable of performing tasks typically requiring human intelligence. While achieving complete human-level cognition remains aspirational, AI systems have successfully emulated several aspects of human decision-making. One domain where this transformation is especially impactful is auditing.

A well-known quote—“Internal auditors must be creative and innovative”—captures the spirit of adaptability essential for auditors. Traditionally driven by manual reviews and sample-based testing, internal audit functions are now undergoing a significant transformation through the integration of AI. These technologies are reshaping internal audits into more efficient, accurate, and data-driven processes.

The evolution of AI is also deeply influencing the banking sector. India’s financial ecosystem is becoming increasingly complex, with massive volumes of data generated across branches, digital platforms, and back-office systems. In this dynamic environment, internal audit has emerged as a vital mechanism for ensuring governance, operational efficiency, and regulatory compliance.

Auditors are tasked with evaluating thousands of transactions, assessing complex risks, and ensuring compliance with changing regulatory frameworks issued by entities such as the Reserve Bank of India (RBI), Securities and Exchange Board of India (SEBI), Pension Fund Regulatory and Development Authority (PFRDA), and the National Payments Corporation of India (NPCI). In this context, AI serves as a powerful enabler, supporting auditors in managing large datasets, enhancing analytical capabilities, and generating real-time insights.

1. Audit Planning and Risk Assessment

Assessing risk across diverse branches and business functions is one of the most challenging tasks in auditing. With a wide variety of financial products and evolving trends, risk levels fluctuate continuously. AI-powered platforms, driven by machine learning algorithms, can analyze historical audit data, transactions, customer profiles, and regulatory alerts to identify high-risk areas.

These insights help audit teams prioritize coverage and allocate resources more effectively. Tools such as ACL Analytics (now part of Galvanize) and MindBridge AI Auditor can analyze entire datasets and flag suspicious or high-risk transactions.

2. Data Collection and Fieldwork

Data collection is often a time-consuming and frustrating phase for auditors, especially when information is delayed or incomplete. AI simplifies this process through Robotic Process Automation (RPA) tools like UiPath and Automation Anywhere, which extract data from Core Banking Systems (CBS), generate necessary reports, and consolidate data from multiple platforms with minimal human intervention.

In addition, Natural Language Processing (NLP) tools help auditors process unstructured data—such as emails, policies, and circulars—by extracting relevant information with greater speed and accuracy.

3. Sampling and Testing

The accuracy of an audit report is directly tied to the quality of its sampling. Poor sample selection can compromise the validity of the entire audit. AI overcomes this limitation by enabling comprehensive testing across 100% of transactions instead of a limited sample.

Process mining tools like Celonis reconstruct actual business workflows and compare them with expected procedures, highlighting deviations for closer review. This enhances the auditor’s ability to assess internal controls effectively.

4. Reporting and Communication

No matter how insightful the findings, they must be presented clearly and persuasively to be impactful. AI tools with Natural Language Generation (NLG) capabilities can convert complex data sets into coherent narrative summaries, producing draft reports with minimal manual effort. This aids in timely reporting and better decision-making at the management level.

5. Follow-Up and Continuous Auditing

The audit process does not end with reporting. Follow-up is essential to ensure that identified issues are addressed and not repeated. AI facilitates continuous auditing by monitoring controls and transactions in real time. When certain thresholds are breached or rules violated, alerts are automatically generated for auditor review.

• The integration of AI into internal auditing offers numerous benefits:




• Enhanced accuracy and efficiency




• Scalability in handling complex and large datasets




• Early risk detection




• Improved compliance with regulatory frameworks

• Data privacy and security: Mishandling of sensitive customer information can have serious legal and reputational consequences.




• Regulatory uncertainty: India’s AI-related regulatory frameworks are still evolving, leading to ambiguity in implementation.




• Lack of human judgment: While AI excels in data processing, it lacks the nuanced understanding and intuition that human auditors bring. Not all decisions can be reduced to algorithms.

As banking operations become increasingly digital and data-centric, adopting AI is no longer optional—it is imperative. AI empowers auditors to maintain high audit quality, identify emerging risks proactively, and uphold regulatory compliance. While challenges remain in terms of infrastructure, skills, and governance, Indian banks that strategically embrace AI-driven auditing will be better equipped to ensure transparency, operational resilience, and stakeholder trust.

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