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Winning in the AI era - the new playbook for Indian banks

Inaugural address by Mr Sanjay Malhotra, Governor of the Reserve Bank of India, at the Annual Financial Institution Benchmarking and Calibration (FIBAC) 2026 Conference, organised jointly by the Federation of Indian Chambers of Commerce and Industry (FICCI) and the Indian Banks' Association (IBA), Mumbai, 11 August 2026.

India's banking sector may leverage its legacy technology challenges as an advantage in the transition to artificial intelligence, according to Boston Consulting Group (BCG). Neetu Chitkara, APAC Head of Fintech and MD & Partner at BCG, explained that banks with fewer legacy systems could leapfrog directly into AI-first operations.

The report, Balance: Thriving in the Age of AI, launched during the Global Fintech Fest (GFF) 2026, explores how financial institutions can balance growth and innovation with safety and ethos as they adopt AI. India has a history of skipping technology cycles, having moved directly from mobile and digital public infrastructure to AI.

This leapfrogging could be particularly beneficial for cooperative and rural banks, which have simpler legacy systems. AI could help Indian banks achieve gains such as nearly 100 basis points of return on assets, almost twice the assets per employee, and a 25-30% reduction in cost-to-income ratio. However, capturing these gains will require a shift from simply adding AI tools to existing systems to fundamentally redesigning processes around AI.

This approach could be a significant advantage for India, allowing institutions to build the next generation of banking directly around AI without being constrained by decades of complex legacy infrastructure.

Written by urgent.news from The Economic Times's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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Why the New LLM Reasoning Leak Paper Matters for Your Team’s AI Workflow

A Quick Look at the Finding A group of researchers just released a paper titled Stealing Reasoning Traces from Proprietary LLM APIs (see the original site here ). In short, they show that when you call a commercial large‑language model (LLM) like Claude, GPT‑4, or Gemini, the service often returns encrypted “chain‑of‑thought” blocks .

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