A wave of recent fraud scandals has sparked an urgent call for a new era of internal audits within India Inc. Businesses are transitioning from traditional methods to dynamic, real-time oversight, leveraging technology to monitor risks as they arise. Experts suggest that this forward-thinking strategy is vital for mitigating potential losses.
The Comptroller and Auditor General (CAG) of India has recently revealed the use of artificial intelligence (AI) and machine learning (ML) to detect widespread fraud in various state beneficiary schemes. CAG K Sanjay Murthy announced this breakthrough during the second State Finance Secretaries Conference in 2025. The integration of AI/ML tools is transforming audit processes, enhancing efficiency and transparency across government departments. The CAG's office has adopted AI and ML to conduct forensic audits, which help identify fraudulent transactions quickly and accurately. AI/ML-based audits can examine large data sets, flag anomalies, and reduce manual errors, ensuring financial savings by preventing misuse of funds in state schemes.
Several examples illustrate how AI is being used in internal audits, including fraud detection in banking, continuous monitoring in IT companies, shrinkage audits in retail, and ESG compliance checks in energy firms. Different industries in India are using AI for internal audits, including banking & finance, retail, telecom, healthcare, and IT/ITES.
Remote audits allow auditors to examine records without physical presence, reducing logistical challenges. This method enables timely audits and wider coverage, such as 100% audits of GST and income tax databases. Remote auditing depends on departments digitizing their data and records. State governments are increasingly adopting digital tools for public financial management, with systems like the Integrated Financial Management Information System (IFMS), Government e-Procurement Platforms, and Digital India Land Record Modernisation Programme strengthening transparency. These platforms help track government spending, reduce corruption, and improve accountability. Programs such as SNA SPARSH aim to improve cash management in government and Special Assistance to States for Capital Investment supports IT infrastructure development. These initiatives facilitate better implementation of digital finance systems and enhance state capacity to manage public funds effectively.
However, AI adoption requires significant investment in tools, data infrastructure, and talent. For small and mid-sized Indian companies, this can feel out of reach, even if it pays off in the long run. AI systems handling sensitive financial or operational data are targets for cyberattacks, especially in sectors like banking and telecom. A breach in an AI-driven fraud detection system could expose customer transaction patterns to malicious actors.
The IIA India-Protiviti survey further revealed that the majority of Indian enterprises are equally convinced that AI and ML will shape the future of internal audit, with 69% of the respondents saying so. However, these respondents also admit that there is a lack of available use cases that show how AI/ML can be used in internal audits. While data analytics and AI-driven tools have significantly improved audit quality, many organizations have yet to fully integrate advanced analytics into their audit processes. Only 18% of the respondents claim to be using data analytics extensively, while a vast majority of 82% are using it moderately or rate-to-none. As cyber threats, financial fraud, and compliance risks intensify, internal audit must evolve beyond traditional oversight and embrace predictive analytics, automation, and stronger board engagement.
To counter the rise in employee fraud, Indian businesses must bolster internal controls and governance. Instances like impersonation fraud have intensified, exploiting remote hiring trends. High-profile cases in banking reveal financial misreporting's severe impacts. Everyday frauds, like collusion in logistics and healthcare, erode trust over time. Companies can adopt AI and blockchain for detection and verification. Strong internal audits, an ethical culture, leadership integrity, and cross-industry collaboration are essential to protect institutional trust and integrity.
