New Delhi: The Comptroller and Auditor General (CAG) of India is developing its own Large Language Model (LLM) to enhance the speed and accuracy of government audits.
The AI-driven system will draw on decades of inspection reports to help auditors access institutional knowledge and ensure consistent analysis. According to an official release, the first version is expected to be operational by November.
The LLM will support auditors in analysing vast datasets, spotting risks, and drafting inspection reports with greater precision. It is part of the CAG’s wider digital transformation that includes remote and hybrid audits of central and state government agencies.
Risk-based planning, secure platform access, and geospatial tools such as PM GatiShakti will provide stronger evidence and consistency across sectors.
Pilot studies for remote audits have already concluded in multiple domains and are now being scaled nationwide. Remote receipts audits, such as a data-led GST review using centrally validated SQL queries, are advancing quickly due to high automation and standardised data.
Telangana’s Principal Accountant General (Audit) completed a remote compliance audit of the Stamps and Registration Department. On the expenditure side, hybrid audits combine manual records with digital analysis, demonstrated in Haryana’s Public Works audit.
West Bengal’s Virtual Audit System and AuditOnline platform now enable paperless certification of Panchayati Raj Institutions, while Madhya Pradesh is mapping a similar system for local fund audits.
To streamline communication with nearly 10 lakh auditee entities, the CAG will soon launch the Connect Portal, providing a unified digital interface for responding to audit queries and tracking inspection reports.
The portal is likely to debut at the Annual Conference of State Finance Secretaries on 19 September, where officials from the Union Finance Ministry, Reserve Bank of India, state governments and key accounting services will discuss strengthening public finance frameworks and unveiling a new Publication on State Finances 2022-23 featuring a decade of comparative fiscal data.
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