PwC's Financial RegTech Insights: November 2026
The global scenario: Resilient yet risks remain elevated1
The global financial system has shown resilience despite several shocks, maintaining orderly markets after initial volatility from the West Asia conflict. Optimism is fuelled by lower-than-expected oil price rises, strong corporate earnings, AI-driven investment, and supportive investments. However, financial stability risks remain high due to uncertainties in supply chains that could tighten financial conditions and increase inflation, potentially leading to changes in monetary policy. Vulnerabilities such as high public debt, bond market fragilities, inflated asset valuations, and the expanding influence of leveraged non-bank financial intermediaries (NBFIs) could worsen future shocks. Inflation pressures have been accelerating with the market expecting future monetary policy to have an extended pause or rate hikes. While the peace deal is underway, global stability will predominantly rely on supply chain stabilisation. Overall, while markets are stable now, these vulnerabilities and any evolving shocks pose ongoing challenges to global financial stability.
RBI’s outlook on India’s financial system is cautiously optimistic2
Being an emerging market economy (EME) and more dependent on crude oil and energy imports, Indian economy has been impacted through financial channels. As a result of global conflicts and uncertainties, fiscal deficit has widened, Indian currency has depreciated and capital inflows have reduced significantly. Strong macroeconomic fundamentals and healthy financial systems have helped India withstand current crisis as it provides some buffer for external shocks. The risk outlook has improved further due to the conflict moving towards cessation, recent government and RBI policies supporting capital inflows and maintaining adequate capital buffers in financial institutions. Despite notable resilience, significant risks persist due to concentrated AIrelated investment exposures and fragilities in global bond markets. These pose latent spillover risks to emerging economies like India, calling for heightened vigilance in monitoring global market developments.
India is still the fastest growing major economy driven by higher domestic demands and private consumption3
GDP grew strongly at 7.7%, with expectations moderating to 6.6% for 2026−27. This robust growth is supported by private consumption, government spending, and exports.
GDP growth (%) assumptions under alternate scenarios
In the above chart, Adverse Scenario 1 predicts worsening geopolitical risks, higher energy prices, and exchange rate pressures causing inflation and growth slowdown in 2026–27, with gradual recovery in 2027–28 and adverse scenario 2 assumes extended geopolitical conflicts through 2027−28, causing ongoing domestic inflation and growth disruptions
Headline inflation rose moderately to 3.9% in May 2026, with Monetary Policy Committee (MPC) adjusting projections to 5.1% for this fiscal year, signaling manageable inflationary pressures.
India’s forex reserves remain healthy at $672.6 billion, offering over 10 months of import cover which a key buffer against external shocks. External debt is sustainable at 20.8% of GDP.
Indian banks show robust growth amid strong deposits, and accelerated credit expansion4
Aggregate deposits of scheduled commercial banks (SCBs) grew by 11.5% year-on-year in 2025−26. Public sector banks led deposit growth with 50.8% share of incremental deposits, followed by private sector banks at 38.6%. Regional rural banks’ deposit share declined slightly from 3.2% in 2022 to 2.9% by March 2026. Households remained the largest depositors at 59.3%, while shares of non-financial and financial corporations increased to 18.5% and 7.8%, respectively, by March 2026.
Deposits growth (% YoY)
Savings deposit share declined from 34.6% in March 2022 to 28.7% in March 2026 and term deposits, often with higher interest rates compared to CASA, increased from 55.2% to 61.6% over the same period. This shift is increasing banks’ cost of funds, pressuring net interest margins.5
Bank deposits (% YoY)
Credit growth of SCBs accelerated to 14.5% year-on-year in 2025-26, with public sector banks (PSBs) growing faster than private sector banks (PVBs), while foreign banks showed more varied performance. Small finance banks maintained robust credit growth, increasing their total credit share from 1.0 per cent in March 2021 to 1.6 per cent in March 2026.
Credit growth of SCBs (% YoY)
Personal loan growth was limited to 12.9% by March 2026 in the last FY, while it maintained a credible 30.7% share in total credit. Loans to the private corporate sector grew by 15.5%, and credit to agriculture and industry rose to 14.4% and 12.0%, respectively. Their respective shares in total bank credit was 12.9 per cent and 22.4 per cent, as of March 2026. Household borrowings grew to 14.3%, comprising 58.6% of total bank credit and gradual growth in FY 2025–26.
Housing loan portfolios seems to have an increase in the highervalue loans, with loans of ₹50 lakh and above, constituting 44.7% of outstanding housing loans, while housing NPAs reduced to 0.5% in March 2026, enhancing lender confidence and credit growth.6
Credit growth across sectors (%YoY)
Asset quality improves in Indian banking sectors, as lower slippages and persistent write-offs point to balance sheet clean-up and multi-decade low NPAs7
As of March 2026, asset quality improved for SCBs with the gross advances increasing to 212.9 lakh crores with 17.4% increase from last year. Also, the GNPA ratio hit a multi-decade low of 1.8%. Annual slippage ratio reduced to 1.2% in 2025-26 due to lower fresh impaired asset accretions. The provisioning coverage ratio was stable at 75.6%, while write-offs relative to GNPAs remained high, especially for private and foreign banks.
Write-offs to GNPA increased over the years across all bank groups with private banks (PVBs) showing the highest write-off ratios, rising to 49.7%, and public sector banks (PSBs) have relatively lower and stable write-offs around 24.3%. Overall, the write-offs of all SCBs’ write-offs increased to 33.2% in 2025–26. This increase in write-off ratios indicate banks are actively cleaning up their balance sheets by removing bad loans, thus improving asset quality. But it may reflect ongoing stress in loan portfolios, particularly in private and foreign banks.
Credit quality improved across all major economic sectors. Agriculture still had the highest GNPA ratio at 5.1% and the largest share of SCBs’ GNPAs at 37.2% in March 2026. Asset quality also showed broad improvement within industry subsectors and personal loans segments. Industry, services, and personal loans sectors show lower GNPA ratios of 1.6%, 1.5%, and 1.0% respectively as of March 2026. The declining GNPA ratios indicate an overall improvement in credit quality across sectors, with the agriculture sector still facing relatively higher asset quality challenges compared to others.
GNPA ratios for credit card receivables are the highest and have steadily increased from September 2024 to March 2026, reaching nearly 1.9%. Vehicle/auto loans and education loans show a notable rise in GNPA ratios over the period. Housing loans have maintained a stable, low GNPA ratio of around 0.8%. Other personal loans have shown fluctuation but remain around 1.0%. The rising GNPA ratios in credit card receivables, vehicle/auto, and education loans indicate increasing stress in these unsecured or semi-secured loan categories, suggesting higher credit risk. In contrast, housing loans remain relatively healthy with low and stable asset quality risks. This trend calls for focused monitoring and risk management in non-housing personal loan segments.
The share of large borrowers in total credit of SCBs rose slightly to 44.5% by March 2026, while their share in gross NPAs steadily declined. Asset quality improved across bank groups, with the GNPA ratio for large borrowers dropping from 2.4% in September 2024 to 1.2% in March 2026. Additionally, the volume of SMA-0 and SMA-2 loans decreased, but SMA-1 loans increased in March 2026. This indicates improving asset quality among large borrowers despite their growing credit share, though some early-stage loan stress (SMA-1) is rising.
Large borrowers are the borrowers with borrowing greater than ₹5 crore
Banks’ earnings momentum comes under pressure as PAT growth slows sharply, even as NIM, ROA and capital adequacy show continued improvement8
Net interest margin (NIM) and return on assets (ROA) have shown a steady increase from the 10-year median to March 2026, though NIM slightly declined recently. PAT growth peaked around March 2023 but declined sharply thereafter to 7.2% in March 2026. While banks have improved margins and asset returns gradually, profit growth has sharply slowed down, indicating possible pressure on earnings despite operating improvements.
Capital, liquidity, earnings, and profitability
Liquidity coverage ratio (LCR) peaked around March 2023 but declined by March 2026 to about 124.2, slightly above the minimum regulatory threshold. Capital to risk-weighted assets ratio (CRAR) also gradually rose to 17.7% in March 2026. Banks have maintained strong capital buffers and overall capital adequacy is improving. Liquidity ratios, while still above regulatory minimums, have softened a bit compared to their peak, indicating a gradual normalisation of liquidity conditions after heightened buffers during market stress.
Key risks and vulnerabilities to financial stability
- While resilience remains the headline, global and domestic risks can cause potential systemic impact.
- Declining foreign investment inflows and elevated long-term yields amid government securities supply pressures pose macro-financial challenges.
- Concentration of risks in major banks and vulnerabilities in NBFCs and mid-sized banks highlight potential spillovers and profitability pressures under stress.
Gold loans and MSME credit drive NBFC growth as improved asset quality supports sector resilience amid increased funding dependence on banks
NBFC credit growth moderated but remained resilient, with overall credit expanding 16.6% year-on-year by March 2026, supported by strong retail lending, particularly driven by gold loans, which accounted for 17.4% of the retail portfolio.
NBFC gold loan portfolios expanded sharply in 2025–26, with gold loans growing at a CAGR of 42.4% since March 2024—nearly twice the pace of overall non-housing retail loans—largely driven by existing borrowers leveraging higher gold prices to obtain larger loans or roll over debt.
Despite rapid growth, immediate credit risks remain contained due to stronger collateral buffers, as rising gold prices have lowered LTV ratios within the 75%–85% cap; however, continued vigilance is needed because a sharp correction in gold prices could weaken collateral values, increase borrower stress, and raise delinquency risks.
- Asset quality improved across NBFC and microfinance segments, as the net NPA ratio declined to 0.8% and microfinance delinquencies eased, with 31–180 days past due falling from 4.4% in September 2025 to 1.8% in March 2026.
- Profitability strengthened, but risk pockets remained, with NBFCs reporting 9.7% growth in net interest income and 11.8% rise in PAT, while rising FinTech delinquencies in small-ticket personal loans and increased dependence on bank borrowings require continued monitoring.
- Funding patterns saw increased dependence on bank borrowings for both NBFC-UL and NBFC-ML, with debentures and commercial papers as other major sources. NBFC-UL’s share of secured borrowings and NBFC-MLs unsecured borrowings both increased in March 2026.
- NBFCs continue to support MSME financing with robust credit growth and improving asset quality, while rising unsecured personal loans remains a concern due to borrower vulnerability in lower-income and higher-risk segments.
- Borrower credit profiles improved across NBFC portfolios, with a higher share of prime-and-above borrowers and risk-tier upgrades among nearprime and prime borrowers, reducing overall credit risk.
- Unsecured personal loans remain a risk area, as borrower vulnerability persists in lower-income and higher-risk segments despite regulatory measures moderating growth.
The RBI continues to evolve the regulatory framework to strengthen financial stability and governance
- Adoption of revised standardised approach for credit risk capital charges aligns with global Basel standards, requiring advanced risk identification and capital allocation.
- Continued emphasis on the Insolvency and Bankruptcy Code and Basel III reforms supports resolution of stressed assets and overall financial system resilience.
India’s financial system remains resilient, supported by strong capital buffers, orderly markets, and technological initiatives. However, emerging risks such as rising household debt, higher funding costs, capital flow pressures, and AI-enabled cyber threats require stronger governance, preparedness, and proactive risk management to sustain long-term financial stability, inclusion, and consumer confidence.
RBI’s approach to responsible AI
Artificial intelligence has become an integral part of day-to-day operations across financial institutions. It is being used to support a wide range of activities, from credit assessments and fraud detection to customer service and compliance monitoring. As adoption continues to grow, so does the focus on using AI responsibly. Issues such as data privacy, transparency, fairness, and accountability are increasingly coming to the forefront. Recognising both the opportunities and risks, the RBI is encouraging an approach that supports innovation while ensuring appropriate safeguards and oversight are in place.
As AI becomes more deeply integrated into India’s financial sector, RBI’s approach is to be more practical and forward-looking to support growth and business continuation. Along with strengthening governance and risk management practices, they are encouraging innovation through initiatives such as AI sandboxes, shared data infrastructure, and indigenous financialsector models. The convergence of AI with digital public infrastructure could further improve financial inclusion and accessibility. At the same time, measures such as stronger audits, graded liability, and incident reporting frameworks emphasise accountability and trust, helping ensure that AI adoption across the sector remains responsible and sustainable.
This shift is producing opportunities for regulatory technology providers, as institutes seek effective solutions to govern, monitor, and manage AI-driven processes in a rapidly evolving environment.9
Global development and approach for thematic regulatory sandbox
Around the world, regulators are making organised efforts to enhance AI innovation in a safe environments by implementing special testing units known as thematic sandboxes. For example, in 2024 the Hong Kong Monetary Authority (HKMA) introduced a GenAI Sandbox, which lets banks use and test AI scenarios within a risk‑managed environment, supported by technical guidance and supervisory feedback. Similarly, the UK’s Financial Conduct Authority (FCA) has taken a multilayered approach through various initiatives and an enhanced Supercharged Sandbox that offers better data access and computing power for experimentation. These approaches show a growing trend towards collaborative innovation between regulators and financial institutes. In this context, the RBI’s proposed approach aligns well with global practices, indicating its intent to build a similar ecosystem in India, one that encourages experimentation while maintaining oversight and stability.
What is a thematic GenAI sandbox?
A GenAI digital sandbox is a controlled test environment where financial institutions can try out GenAI models without any risks.
Key features include a secure testing environment that uses synthetic data to protect privacy, along with real-time monitoring capabilities that help identify potential biases and inaccuracies as AI models are being developed and evaluated.
The sandbox also provides tools to help assess whether the AI application adheres to AML, KYC and consumer protection norms.
The detailed notification can be accessed here.
AI adoption in Indian financial services: A glimpse of the economic survey
The adoption of AI across India’s banking and financial sector is growing fast, but it is still in a nascent stage. As highlighted in the Economic Survey 2025–26, only around 21% of banks and financial institutions have started using or building solutions around AI, with most of this adoption seen among larger players.
AI usage by institution type
Instead of implementing complicated systems right away, institutions focuses on simple, practical use cases like customer support, improving internal processes, and assisting decision-making. It reflects a careful and measured approach in which banks are testing what works, learning along the way, and slowly integrating AI into their operations while keeping an eye on costs, infrastructure, and regulatory expectations.10
However, Indian banking shows a clear AI gap, where large banks adopt technology rapidly, while cooperative banks lag significantly due to limited resources, high costs, and lack of technical expertise.
Use cases of AI in BFSI
RBI’s AI implementation roadmap
- AI innovation sandbox—a safer first step for AI experimentation
- Shared data infrastructure—building the foundation for AI
- Green AI: The next imperative for responsible innovation
- From complexity to clarity—reinventing compliance with RAG
AI innovation sandbox—a safer first step for AI experimentation
Innovation in financial services has traditionally been supported through thematic or regulatory sandboxes, which allow institutions to test new solutions with real users in a controlled environment. While these frameworks have worked well for validating products, the growing complexity of AI systems means there is a need for an extra step before live deployment.
The RBI’s idea of an AI innovation sandbox is meant to give financial institutions a safe space before going live. In simple terms, it allows banks, NBFCs, FinTechs, and even tech providers build and test AI solutions without the pressure of real-world consequences. Unlike the usual regulatory sandbox which involves limited live testing with actual customers, this one focus more on the backend. It provides shared resources like computing power, quality data, and foundational models to support experimentation. There are clear rules around access, timelines, and security, so everything stays controlled and responsible. Over time, the RBI plans to make this widely available. For FIs, this is a big step as it lowers the cost of trying new ideas, helps test models more thoroughly, and allows regulators to understand how these systems behave early on, making innovation both safer and more practical.11
Shared data infrastructure—building the foundation for AI
RBI has taken initiatives on creation of shared data ecosystems. AI models are only as strong as the data they are trained on, and many organisations, especially smaller ones struggle with accessing high-quality, standardised data. The RBI is looking at shared infrastructure, including programmes like AIKosh, to fix this problem. By making common datasets and infrastructure available, the regulator is helping create a fairer environment. This can significantly improve the quality of AI models throughout the system and lead to more consistent and dependable results in areas such as lending and risk evaluation.
What is AI landing zone?
To bridge this gap, RBI proposes shared AI infrastructure like ’AI landing zones, enabling smaller banks to access advanced tools on a pay-per-use basis. Along with capacity building, policy support, and ethical AI frameworks, these measures aim to promote inclusive, affordable, and responsible AI adoption across all banking sectors.
The detailed notification can be accessed here.
Another initiative is the India Datasets Platform which is being developed by MeitY as part of IndiaAI Mission. This platform is meant to be a type of DPI that offers access to curated, high-quality datasets for AI development. Such a platform can help standardise data access across institutions and reduce entry barriers for smaller players.12
What is AIKosh?
AIKosh enables controlled and secure access to India based datasets, thereby supporting data governance, privacy protection and responsible data use.
The detailed notification can be accessed here.
Green AI: The next imperative for responsible innovation
As AI adoption accelerates across banking sector, its impact on sustainability is gaining momentum and focus. These systems are very powerful, but they also use a lot of energy. When used at scale, they can push bank’s overall carbon footprint to a bigger extent. This is where the idea of green AI is making its way by encouraging institutions to build solutions that are not just efficient, but also responsible and energy conscious. At the same time, AI has become integral part of India’s financial sector operates, supported by strong digital infrastructure like UPI and Aadhaar. The growth story reflects this shift, with the AI market expanding from USD 2.97 billion in 2020 to USD 7.63 billion in 2024 and expected to reach USD 131 billion by 2032.13
In practice, the efforts are already visible. AI is helping banks handle 40 to 50% of regular customer queries, detect fraud in near real time, and make faster, more inclusive lending decisions. But as AI becomes more widely used, the discussion around its adoption is taking a different angle. It is no longer just about scaling AI; it is about scaling it responsibly. This marks an important shift. Going forward, success will not only be measured by how advanced AI systems are, but by how efficiently they are run, how transparently they are governed, and how sustainably they are integrated into the broader financial ecosystem.
From complexity to clarity—reinventing compliance with RAG
As regulatory requirements grow in volume and complexity, there is a clear need to rethink how compliance is approached in financial institutions. Today, teams still spend lot of time reading through circulars and master directions. This slows down the process of decision-making and increases the chances of mistakes and missing important details. With the growing pace of complex regulatory changes, traditional approaches find it difficult to align with the pace, especially in times when entities need to respond fast with accuracy in a dynamic environment.
In general, compliance teams deal with a constantly evolving regulatory landscape. They are keeping track of thousands of circulars, master directions, and notifications which must be correctly interpreted, and implemented. This process is slow and prone to human error, especially when multiple documents need to be cross verified. It gets even harder for smaller institutions that may lack the resources or specialised expertise to interpret regulatory updates effectively, leading to increased operational risk and exposure to compliance gaps.
This is where retrieval-augmented generation (RAG) can bring in change. By combining complex models with structured regulatory datasets, RAG helps institutes to generate precise, context-sensitive answers directly from source documents, reducing dependency on manual interpretation and minimising the risk of getting errors. It makes sure that responses are clear, traceable, and always up to date, solving the issues of confusion in how compliance is handled. Over time, this can enable taking faster decision-making, improved consistency, and more proactive regulatory management.
This marks a big change in how compliance is seen. Rather than being a reactive function, it can evolve into a more integrated, business-aligned capability. Looking ahead, AI-enabled RegTech solutions with RAG have the potential to make compliance more seamless, consistent, and forward-looking, helping institutions stay agile while maintaining trust in an increasingly complex regulatory environment.
What is RAG?
Retrieval-augmented generation (RAG) helps large complex models by letting them look up and use information from outside sources that are reliable and trusted before they create a response. This enhances accuracy and relevance without retraining the model, making outputs more reliable, context-specific, and cost-effective for domain-specific or organisational use cases.
Global examples of AI governance
Financial regulators from Singapore advance AI governance with industry-wide risk management toolkit
Financial regulator/s of Singapore are taking a practical approach to AI governance by helping financial institutions move from high-level principles to real world implementation. In March 2026, the regulator/s released an AI Risk Management Toolkit which is developed in collaboration with banks, insurers, and other industry participants. This toolkit provides guidance on establishing AI governance structures, and assessing AI-related risks, managing all the actions throughout its lifecycle, and building the internal capabilities needed for responsible adoption. It also addresses emerging technologies such as generative and agentic AI. The regulator/s plans to continue refining these practices through ongoing industry collaboration, demonstrating a clear focus on ensuring that innovation is accompanied by robust governance and accountability across the financial sector.14
United States’ banking regulators strengthen AI model governance requirements
US banking regulators are reinforcing oversight of AI and advanced analytics through updated model risk management guidance issued in April 2026. The revised framework emphasises that AI and machine learning models should be subject to the same rigorous governance standards as other critical banking models. Banks are required to keep clear lists of all their models, check higher-risk models on their own, keep a close eye on models throughout their life cycle, and use controls that match the level of risk in their business. The change shows growing regulatory attention on the increasing use of AI in banking and highlights the expectation that institutions maintain transparency, accountability, and effective risk management as AI deployments continue to expand.15
The Financial Stability Board (FSB) pushes for global standards on responsible AI adoption
The Financial Stability Board (FSB) is stepping up efforts to establish global expectations for the responsible use of AI across the financial sector. In 2026, the FSB launched consultation work focused on sound practices for AI adoption, covering areas such as governance, risk management, lifecycle controls, operational resilience, cybersecurity, and third-party dependencies. The initiative comes as regulators increasingly recognise that AI-related risks can extend beyond individual firms and may have broader implications for financial stability. By promoting common principles and supervisory expectations, the FSB aims to help financial institutions scale AI responsibly while maintaining trust, resilience, and strong governance frameworks.16
Navigating the risks of AI: From innovation to accountability
As financial institutions increasingly rely on external technology providers for AI capabilities, managing third-party risks becomes critical. While AI solutions are built and supported by external vendors, accountability still remains with the regulated entity. This creates challenges around oversight, data confidentiality, model transparency, and risk management, particularly when vendors use their own AI tools or subcontractors as part of the service delivery process.
AI can strengthen security controls, but it also introduces new vulnerabilities that traditional cybersecurity frameworks may not fully address. Threats such as data poisoning, adversarial attacks, and model manipulation can compromise AI systems and influence their decisions. As organisations embed AI deeper into critical operations, robust monitoring, access controls, and ongoing security assessments will become essential to maintaining trust and resilience.
The use of AI in credit assessment can improve efficiency and broaden access to finance, but it also raises concerns around transparency. If lending decisions are made using complex algorithms that cannot be easily explained, institutions may struggle to demonstrate fairness, address customer concerns, or satisfy regulatory scrutiny. Ensuring that AI-driven decisions remain understandable and subject to human oversight will be critical in maintaining confidence in digital lending models.
As AI plays a greater role in customer interactions and decision-making, institutions must ensure that customers are not left without clarity or recourse. Consumers should be able to understand when AI is influencing a decision and have access to mechanisms for clarification, review, or appeal. Without adequate safeguards, the use of AI could weaken trust and create concerns around fairness and accountability.
AI is evolving faster than many existing regulatory frameworks were designed to handle. While current regulations provide a strong foundation, emerging issues such as AI disclosures, vendor due diligence, cybersecurity exposure, and model governance require greater attention. This creates a need for clearer guidance and stronger governance practices to ensure that innovation progresses in a responsible and compliant manner.
From oversight to enablement: RBI’s evolving role in the AI era
As AI continues to gain traction across the financial sector, the industry is looking for greater regulatory clarity on issues such as data privacy, transparency of AI-driven decisions, bias management, the use of external AI models, and cross-border data governance. Going forward, the challenge for policymakers will be to create a framework that encourages responsible experimentation while maintaining trust and consumer protection. India has a unique opportunity to learn from global developments in AI governance while shaping an approach that reflects its own priorities around financial inclusion, innovation, and economic growth. A balanced, risk-based framework can help financial institutions adopt AI with confidence, ensuring that safeguards evolve alongside technology rather than becoming barriers to progress. By fostering collaboration between regulators, industry participants, and technology providers, the financial ecosystem can unlock the benefits of AI while maintaining stability, fairness, and resilience that are fundamental to long-term growth.
The RBI’s approach to AI is carefully planned, ensuring that new ideas are used in a safe and responsible way. Programmes like thematic and AI innovation sandboxes provide structured pathways for experimentation, the broader focus on governance, data infrastructure, and accountability mechanisms ensures that growth remains sustainable and well-regulated.
At the same time, global trends indicate that AI adoption is already moving beyond experimentation, with leading banks embedding intelligent, retrievaldriven systems into their core operations. This convergence of regulatory foresight and industry innovation highlights a clear direction—one where AI is not only scaled for efficiency, but also designed for trust, transparency, and long-term impact.
The future of AI in financial services will be defined by how effectively institutions integrate these elements—leveraging advanced technologies while maintaining strong governance—to build a system that is not only intelligent, but also responsible, inclusive, and sustainable.
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Contributors: Anurag Gupta, Mamta Kumawat, Shashank Gokhe, Gourab Mazumder, Smruti Bal and Samrat Biswas
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