
India's central bank seeks approval for overseas rupee lending to neighbours, sources say
NEW DELHI, May 26 (Reuters) - India's central bank is taking another step to internationalise the rupee, seeking approval to allow domestic banks to lend the currency to overseas borrowers for the first time, two sources said.
The Reserve Bank of India (RBI) has asked the federal government to allow domestic banks and their foreign branches to lend Indian rupees to overseas borrowers to enhance the use and acceptability of the local currency in trade.
The proposal, which was sent to the finance ministry last month, suggests lending in rupees to non-residents can begin in neighbouring countries such as Bangladesh, Bhutan, Nepal and Sri Lanka, the sources said.
If successful, such rupee-denominated lending could be extended to cross-border transactions globally, one of the sources said.
According to Ministry of Commerce data, 90% of India's exports to South Asia were to these four nations in 2024/25, amounting to nearly $25 billion.
Currently, foreign branches of Indian banks are restricted to providing loans in foreign currencies and such loans are extended mainly to Indian firms.
The sources declined to be identified as the discussions are confidential. Emails sent to the Finance Ministry and the RBI requesting comment did not receive a response.
The central bank has been taking steps to increase the use of the local currency in global trade and investment.
As part of the strategy, RBI recently permitted the opening of rupee accounts for non-residents outside India.
Earlier this month, Reuters reported the RBI has sought government's approval to remove the cap on foreign banks with so-called vostro accounts buying short-term sovereign debt, to boost rupee-denominated investment and trade.
The RBI will open the foreign loans in rupees only for the purpose of trade, the sources said.
Currently, rupee liquidity is provided in other countries only through a limited number of government-backed credit lines or bilateral currency swap arrangements.
"The objective is to reduce dependence on such arrangements and instead allow commercial banks to provide rupee liquidity on market terms," the first source said, citing a communication from the central bank in April.
The second source said enabling easier access to rupee-denominated loans will help facilitate trade settlements in rupees and reduce exposure to foreign exchange volatility.
The government has received several requests from financial institutions to support strategic projects through rupee-denominated financing, the second source said.
India's experience with local currency pacts with the United Arab Emirates, Indonesia, and the Maldives, as well as Special Rupee Vostro Accounts used for trade with Sri Lanka and Bangladesh, has underscored the need to deepen the availability of rupee liquidity, the source said.
If implemented, the policy would mark a major step toward integrating the rupee into the global financial system, positioning it as a more widely accepted currency for international trade and investment, the second source added.
Hashtags

Try Our AI Features
Explore what Daily8 AI can do for you:
Comments
No comments yet...
Related Articles


BBC News
4 hours ago
- BBC News
Kohli's RCB end 18-year wait for maiden IPL title
Indian Premier League, Final, AhmedabadRoyal Challengers Bengaluru 190-9 (20 overs): Kohli 43 (35); Arshdeep 3-40, Jamieson 3-48Punjab Kings 184-7 (20 overs): Shashank 61* (30); Pandya 2-17Royal Challengers Bengaluru won by six runsScorecard Virat Kohli finally won the Indian Premier League as Royal Challengers Bengaluru sealed their maiden title with a six-run win over Punjab 36-year-old India legend, the only player to represent the same franchise in every edition of the tournament, top-scored for RCB, hitting 43 in his side's 190-9 from 20 who were also seeking a first IPL title, were unable to chase down their target, finishing on the batting alongside England's Phil Salt, Kohli's crucial knock from 35 deliveries, took RCB to 143 before he was the fourth wicket to fall, caught and bowled by Afghanistan's Azmatullah Omarzai. Salt struck 16 from nine balls before being caught by Shreyas Iyer off Kyle Jamieson, while England all-rounder Liam Livingstone hit 25 as the side's numbers three to six all posted scores in the accumulated steadily, Andy Flower's team failed to accelerate in the final over, with Arshdeep Singh taking three wickets in his last six balls to ensure RCB didn't pass found themselves 98-4 in the 13th over of their reply, losing captain and star batter Shreyas for just one, caught by Jitesh Sharma off Romario Wadhera and Shashank Singh began to rebuild, but their side's challenge effectively ended when Wadhera and the big-hitting Marcus Stoinis were both caught in the same Bhuvneshwar Kumar 29 from Josh Hazlewood's final over, Shashank hammered 22 from the last four balls of the innings, bringing his side tantalisingly close to their target with an unbeaten his efforts were ultimately in vain, and after 18 years and three previous final defeats, Kohli and RCB could finally celebrate a successful IPL campaign. More to follow.

Finextra
4 hours ago
- Finextra
Lending Redefined: Implementing ML To Enhance Products, Workflow, and Customer Experience: By Serhii Serednii
An AI-First-Approach is a core pillar of fast-growing fintech companies, aimed at enhancing both internal processes and product efficiency. Let ML Decide: The Credit Scoring Evolution Automation in lending and scoring has existed since the mid-20th century, but when we talk about digital lending today, we no longer refer to traditional rule-based scoring with questions like 'Do you have a higher education?' or 'Are you married?' – we are talking about a comprehensive decision tree and its reliable automation. The shift towards analyzing increasingly large volumes of data is driven by a fundamental difference in business models. Traditional banks typically provide customers with a few large loans in their lifetime (such as mortgages or car loans), while fintech companies focus on small amounts that clients can receive monthly. Accordingly, the approach to lending and risk management differs. In fintech, the depth of the decision tree can reach thousands of nodes. To automate this process, you can develop proprietary ML algorithms based on models like Gradient Boosting Machines. These ML methods are particularly effective for estimating each client's lifetime value and evaluating the potential for long-term cooperation. This allows you to balance desirable conditions for customers, attract new users, and maintain strong profitability. Behind Limit Strategies ML also helps fintech companies answer a critical question once a client is approved: How much money can be lent? It might be helpful to try ML-driven limit strategies inspired by Recommendation Systems, models typically used in products that adapt to user behaviour and preferences. Almost everyone has experienced such models in action: the content suggested to you on TikTok or YouTube is powered by them. The comprehensive strategies can allow you to assess the business impact on credit risk (the risk of a client not repaying a loan) and the risk of under-receiving profit (i.e., missed opportunity from issuing loans with lower limits or declining them entirely). See, Verify, Confirm Deep learning–based verification technologies have gained traction across the lending industry to strengthen fraud prevention and streamline identity verification. One of the helpful solutions here focuses on verifying the authenticity of a user's face during onboarding or transaction approval processes. It combines face detection with liveness verification to confirm that the input comes from a live person, not a static image or recording. It helps to cross-check each new face against an existing database. With that, a system identifies individuals attempting to submit multiple applications under different names, which is a common tactic in fraud scenarios. Controlling the Quality of 100% of Phone Calls Generative AI models like LLMs are now transforming internal operations, especially in customer support and call centers. Previously, supervisors in large teams could manually review only a fraction of calls (often less than 10%), limiting the ability to ensure consistent quality. Now, the AI tools can improve call centers' productivity: Transcribe 100% of incoming and outgoing calls in the local language and translate the transcripts into English. Assess call quality based on predefined criteria, such as detecting deviations and incorrect responses compared with the original call script. Generate individual reports to support supervisor feedback and employee training, and management reports to monitor the call center's effectiveness and overall performance. This tool is perfect for scaling a business's quality standards across multiple countries. It allows for transparent, data-driven assessments of employee performance and offers insights for the leadership team on user problems and service issues. Machine learning and artificial intelligence should be implemented into scoring, fraud detection, and customer service to enhance operational efficiency, personalize customer experiences, and ensure consistent quality. However, it is essential to remember that all such scenarios are handled in strict compliance with relevant data protection laws, including the GDPR. Constraints, legal, ethical, and technical, are not just boundaries, but a framework for responsible innovation.


Reuters
7 hours ago
- Reuters
India's HDB Financial gets market regulator nod for IPO
MUMBAI/BENGALURU, June 3 (Reuters) - India's markets regulator, the Securities and Exchange Board of India (SEBI), has approved the initial public offering (IPO) of HDB Financial Services, marking the HDFC group's first public float in seven years. According to a document published on the SEBI website on Tuesday, the regulator issued observations on the public listing, allowing the company to proceed with its IPO, a source with direct knowledge of the matter said. SEBI and HDFC Bank ( opens new tab did not immediately respond to a Reuters request for comment. In October, HDB Financial filed for an IPO of up to 125 billion rupees ($1.5 billion). HDFC Bank, which holds a 94.3% stake in the lender, will sell shares worth up to 100 billion rupees, while fresh shares worth up to 25 billion rupees will be issued. HDB Financial's listing follows new norms introduced by the country's central bank in 2022 that require large non-banking financial companies to be listed on stock exchanges by September 2025. For the quarter ended March 31, HDB Financial Services posted a profit of 5.3 billion rupees, while its net revenue came in at 26.2 billion rupees. The company's total loan book was 1.07 trillion rupees as of March-end. ($1 = 85.6540 Indian rupees)