NFRA to complete 10 audit firm inspections in FY26, exploring Al to boost oversight

NFRA chairperson Nitin Gupta has said NFRA plans to complete inspections of 10 audit firms in FY26 described as the highest annual number in NFRA’s history. He also said NFRA is taking “baby steps” in testing AI with the goal of making workflows faster and more efficient and to help analyse financial statements for critical accounting policies, questionable transactions, and auditor professionalism.

What NFRA is and why inspections matter

One of NFRA’s key tools is the audit firm inspection. An inspection is not a punishment. It is a structured review of how an audit firm works. NFRA checks whether the firm follows auditing standards, maintains independence, and applies proper quality controls.

Inspection reports are made public. These reports show gaps in audit practices. They also guide the audit profession on what NFRA expects.

When NFRA increases the number of inspections, it increases scrutiny across the audit market. This affects not only the inspected firms but also other firms that study the reports and improve their systems.

What NFRA tends to inspect inside an audit

NFRA’s inspection focus as areas with high risk of material misstatement such as related party transactions, impairment of non-financial assets, and internal controls over revenue recognition. The inspection approach also checks whether firms maintain required audit documentation, and whether their processes support independence and comply with the Companies Act and audit-quality rules.

This is consistent with how audit regulators operate; they examine both “tone at the top” and “audit file reality,” since failures often arise from weak quality controls plus weak engagement-level execution.

AI: what NFRA is exploring and what is already on record

1) AI for financial reporting compliance monitoring (public challenge with IndiaAI)

NFRA has formally launched (with IndiaAI) an IndiaAI Financial Reporting Compliance Challenge to solicit AI tools that help monitor financial reporting quality and speed up internal tasks.

The official PIB release states the challenge asks teams to build an engine capable of:

• extracting data from multi-format documents, and
• validating the extracted information against frameworks.

The output NFRA seeks includes:

• explainable compliance validation reports,
• automated analytics for risk indicators, and
• an AI-enabled insight bot to support NFRA’s mission.

The same release sets out incentives and timelines, a Rs. 1.5 crore prize pool, interim awards to shortlisted teams, and a potential two-year contract worth up to Rs. 1 crore for national-scale deployment, with applications closing on 22 Feb 2026.

This is the most concrete public description of what NFRA wants AI to do not “AI in general,” but a pipeline that reads varied disclosures and produces explainable compliance and risk outputs.

2) AI testing for audit oversight and inspections

Separately, in the interview by Mint, Gupta said NFRA is testing AI to speed up workflow and improve oversight capability. The stated direction is to enable faster review of financial statements for critical policies and questionable transactions and to support the inspection and oversight function.

Taken together, NFRA’s AI track has two layers:

• tools to review financial reporting and disclosures at scale, and
• tools to support audit oversight, where NFRA examines how auditors responded to risks.

3) Earlier work: hackathon on LLMs and GenAI with IIT Kanpur

NFRA and IIT Kanpur also held a hackathon on LLMs and Generative AI focused on making complex financial statements more readable and extracting insights. This indicates NFRA has been building capability and a talent pipeline, before the FY26 AI challenge.

What AI can change inside NFRA’s oversight model

If NFRA deploys AI tools in line with its own challenge brief, there are direct oversight benefits:

• Scale and speed: AI can extract, tag, and compare disclosures across many companies, which supports risk-based selection of audit engagements and themes.
• Consistency: AI can run repeatable checks against defined compliance rules and disclosure frameworks, which reduces variance between teams.
• Signal detection: Automated risk indicators can flag patterns in revenue recognition disclosures, related party notes, impairment assumptions, and key estimates areas that NFRA already focuses on in inspections.
• Explainability requirement: NFRA’s brief calls for explainable compliance validation reports. This is important because an audit regulator must defend findings through evidence and reasoning, not model output alone.

The hard risks NFRA must manage if it uses AI

NFRA’s direction is clear, but AI deployment in regulation requires controls:

• False positives and due process: A model that flags risks must be treated as a screening tool. Enforcement or adverse findings must rest on verified evidence and professional analysis.
• Model bias and drift: Regulatory checks must remain stable across time and across industries. NFRA will need governance on training data, updates, testing, and version control.
• Confidentiality and security: NFRA handles sensitive audit working paper themes and company disclosures. AI systems must meet strict data handling and access controls.
• Explainability vs performance trade-off: NFRA has already signaled explainability as a core requirement. This reduces the space for black-box use in regulatory decision paths.

A global parallel is useful here. The UK’s Financial Reporting Council has warned that large firms have not formally measured how AI tools affect audit quality, even as AI use rises. That risk is relevant for India too, because NFRA inspections look at whether audit evidence and judgments remain robust under tool-assisted workflows.

What audit firms should expect in FY26

If NFRA completes 10 inspections and builds AI tooling, audit firms should expect:

More risk-based selection driven by disclosure analytics, market events, and pattern detection. Sharper documentation expectations in high-risk areas (revenue, related party, impairment, internal controls). More scrutiny on independence systems and firm-wide quality control, since NFRA’s inspection model combines both. Faster inspection cycles if AI reduces the time NFRA staff spend on extraction and first-level checks.

What to watch next

1. NFRA’s list of the 10 inspected firms and the cadence of report releases.
2. Whether NFRA’s AI challenge leads to a deployed tool and how NFRA describes the tool’s role in decision-making.
3. Updates to inspection guidelines or new technical guidance that reflects AI-era oversight.

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