AI Governance in India's Financial Sector: RBI's FREE-AI Framework, CERT-In Advisories and Regulatory Coordination
Background: Parliamentary Question on AI in Financial Services
During the Lok Sabha session on 27th July 2026, a significant question was raised regarding the deployment of Artificial Intelligence in India's financial ecosystem. The question, registered as Lok Sabha Unstarred Question No. 1153, was posed by Ms. Praniti Sushilkumar Shinde and addressed to the Ministry of Finance. The response was delivered by Shri Pankaj Chaudhary, Minister of State in the Ministry of Finance.
The query covered four distinct dimensions:
- Whether the Government had examined the growing deployment of AI by financial institutions and fintech entities in areas such as credit scoring, fraud detection, algorithmic trading, and customer profiling
- Whether any regulatory framework or guidelines had been issued or were under consideration to govern AI use in financial services — particularly regarding transparency, accountability, and algorithmic explainability
- Steps taken to mitigate risks arising from AI deployment, including algorithmic bias, data privacy vulnerabilities, and systemic threats
- Whether coordinated oversight was being pursued between the Government, the Reserve Bank of India, and other financial regulators to protect consumer interests
The official reply provided a consolidated response to all four sub-questions, offering a comprehensive picture of where India stands on AI governance in its financial sector.
Widespread Adoption of AI/ML Across the Financial Sector
The Ministry confirmed that Artificial Intelligence and Machine Learning (AI/ML) technologies have seen rapid and widespread adoption across India's financial services landscape. This adoption has been driven by significant advances in software capabilities and the growing demand for efficiency, speed, and precision in financial operations.
Key areas where AI/ML is currently being deployed include:
- Customer service chatbots — enabling round-the-clock query resolution and improved user engagement
- Fraud detection — using real-time pattern recognition to identify and flag suspicious transactions
- Credit underwriting — automating and enhancing the accuracy of lending decisions
- Real-time transaction pattern analysis — monitoring large volumes of transactions simultaneously for anomalies
- Risk management — deploying predictive models to assess portfolio and operational risks
- Client identification and monitoring — supporting Know Your Customer (KYC) processes and ongoing due diligence obligations
The breadth of these applications underscores how deeply AI has penetrated financial sector operations — and equally, why a robust governance architecture has become essential.