CAG Uses AI Audits to Detect Fraud in State Schemes

AI Audits Latest News

• The Comptroller and Auditor General (CAG) of India, K Sanjay Murthy, revealed that AI and ML-based forensic audits have exposed numerous fraudulent cases in state beneficiary schemes. 
• Speaking at the second State Finance Secretaries Conference, he said these audits can prevent misuse of funds and save significant financial resources for state governments.

CAG Pushes AI-Driven Forensic Audits and Digital Governance

• The CAG of India emphasised the growing role of artificial intelligence (AI) and machine learning (ML) in detecting fraud during audits of state beneficiary schemes. 
• Digitisation of audit processes is now the CAG’s top priority to enable remote audits, reduce hassles for executives, ensure timely reporting, and expand coverage — including 100% checks of GST and Income Tax databases.

Use of AI/ML in Fraud Detection

• AI/ML-based forensic audits have already exposed numerous fraudulent cases across states. 
• These tools could save states significant financial resources currently being siphoned off through fraudulent practices. 
• A key application of remote audits will also include identifying tampering of electronic documents.

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Digitisation of Financial Management Systems

• There have been progresses by states in adopting digital tools for public financial management. 
• Key systems include: 
  • the Integrated Financial Management Information System (IFMIS), 
    • IFMIS is a digital, web-based platform used by government entities to automate and integrate various financial functions like budgeting, accounting, payments, and reporting. 
  • Works and Accounts Management Information System (WAMIS), 
    • WAMIS is a comprehensive digital system for managing construction projects, covering their entire life cycle from inception to completion. 
    • Developed by C-DAC, it tracks and monitors financial transactions, links them to physical progress, etc.
  • Government e-Procurement Platform (GePNIC), 
    • GePNIC is a software system developed by India’s National Informatics Centre (NIC) for conducting all stages of the government procurement process for goods, services, and works.
  • e-voucher systems, and 
  • The Digital India Land Records Modernisation Programme (DILRMP). 
    • DILRMP is a Central Sector Scheme launched by the GoI to computerize, digitize, and modernize all land records and registration processes across the country
• However, the maturity level of these applications varies significantly across states, though digitisation has already improved the finalisation of Monthly Civil Accounts.

Remote Audits Across Departments

• The CAG has successfully conducted remote audits in areas such as GST, stamp and registration, e-procurement, works, and Direct Benefit Transfer (DBT) schemes. 
• The next goal is to extend remote auditing to all government departments with digitised records.

Auditing Local Governments

• There is need to audit Urban Local Bodies (ULBs) and Panchayati Raj Institutions (PRIs), as nearly 15 cities contribute more than 50% of India’s GDP. 
• Cities like Mumbai, Delhi, Bengaluru, Chennai, and Hyderabad alone account for 30% of GDP and could add 1.5% to annual growth if efficiencies improve.

Conclusion

• Initiatives such as SNA SPARSH will strengthen cash management, while schemes like Special Assistance to States for Capital Investment will help states build IT-driven financial infrastructure.
  • SNA SPARSH is a cash management system and a Single Nodal Account (SNA) initiative for India’s Centrally Sponsored Schemes (CSS) that facilitates a “just-in-time” release of funds from central and state consolidated funds. 
  • It integrates the Public Financial Management System (PFMS), the State Integrated Financial Management System, and the e-Kuber platform of the RBI to achieve better transparency, efficiency, and accountability in fund flow.
• It is therefore imperative for states to continuously adopt and integrate advanced digital tools, including artificial intelligence (AI) and machine learning (ML), into their public financial management frameworks. 
• Such integration will not only enhance transparency and accountability but also significantly improve efficiency in governance, thereby ensuring more effective utilisation of public resources and long-term fiscal sustainability.

Source: IE | ToI | OB