Supervising what you cannot inspect

by Maninder Singh Juneja and Renuka Sane.

In traditional financial regulation, supervisors are able to inspect the thing being regulated. For example, a scorecard by a bank was usually a short list of factors that one could comprehend. These factors could be traced to key documents within the bank, banks had a stated rationale for why they were being used, and there was some common sense in making weighted averages. The scorecard itself was static - the same weighted factors were applied over a period of time. Inspecting this process of constructing the scorecard and using it for management decisions was how trust was built. The AI world is different. Models are rented rather than built, change continuously, and behave probabilistically. They cannot be fully inspected even by the institution deploying them let alone by the regulator.

How should we then think of regulation?

One approach is to intensify the traditional approach where regulators demand more explainability, more documentation, and more validation. This will drive up the costs of compliance. But more importantly, this is ill suited to the new world where the technology changes rapidly, where the bank does not control the AI it uses, where there is no clear artefact that the bank can give the supervisor such as a model or a document (Board of Governors et al 2026). When regulators push traditional approaches, banks will respond by choosing AI models which are easy to document rather than the ones best for them, or defer AI deployments altogether. All these are unhappy consequences. What we need are policy makers who understand the live systems of the new world.

In this article, we analyse these emerging problems from first principles. We start from scratch, understand the landscape of market failure in the world of AI in banking, and think about how regulators can grapple with this world.

Our key idea is that AI systems resist replicability. Regulatory strategies that demand replicability will flounder or choke technology deployment. We suggest the regulatory standard applied should be AI deployments that are "supervisable" - the outcomes should be observable, they should be attributable to causes, and reversible by humans.

How AI is actually deployed

Before we get to the puzzles faced by regulators, we need to describe what AI in banking is. This categorisation is not unique to banking. Banking is simply where their consequences are regulated. Five modes span the range.

AI as Tool: AI augments a step, a search, a calculation, a first draft that the human controls. The human is cognitively engaged.

AI as Collaborator: The human and AI co-produce iteratively, and the human participates at every stage.

AI as Recommender: AI generates scores or options, and the human makes the decision. This is the classic human-in-the-loop, but credible only if the human can meaningfully interrogate the recommendation and not degenerate into cognitive surrender.

AI as Preparer: AI does the work and the human signs off. The approval here is closer to a check by a supervisor rather than a re-derivation.

AI as Autonomous executor (agentic AI): AI executes autonomously inside guardrails and the human monitors on exception. Emerging forms of agentic AI for banking run from single-task agents (a payment released, a service query resolved) to multi-step workflows and customer-facing agents that transact.

A central issue here is the true (de facto) role of the human. A reviewer who approves a thousand recommendations a day is not overseeing a model, the model is overseeing her. A feature of any deployment is the measured divergence between the model recommendation (and estimated uncertainty) vs. the human decision. Managers of banks will need to worry about relapses of human behaviour inside the organisation, a bit like how hospital managers worry about bad behaviour by doctors within their organisation.

What AI does to market failure in banking

We now shift gears to look at the standard knowledge on market failure in banking. Regulation may be justified when (and only when) there exist market failures which cannot self-correct fast enough, and there is adequate state capability in banking regulation to be able to correctly identify them and intervene. AI's distinctive feature is that it can cure several classic failures. Better default prediction reduces credit rationing, better fraud detection cuts deadweight loss, richer risk assessment lets banks serve customers they previously could not price, AI advisors help customers avoid some malpractices by the bank. But there are also some new problems that are anticipated.

Information asymmetry: This happens in banking at two levels: borrower-to-lender (adverse selection, hence credit rationing) and firm-to-consumer (product complexity, hence mis-selling). AI narrows the first through alternative data and may widen the second. On one hand, the customer armed with AI can see through many things proposed by the bank which are not in her best interest. But the consumer cannot observe why they were shown a product, offered a price, or steered toward a specific insurance plan. The sales process itself becomes more opaque. Personalised pricing approaches first-degree price discrimination, extracting consumer surplus. A single flawed model can mis-sell to millions simultaneously, converting isolated conduct failures of the pre-AI world into a big correlated event. And redress weakens when a denial comes from a model the firm itself cannot explain.

Systemic externalities from shared infrastructure: Each bank chooses its models, data sources and vendors to optimise its own performance. When multiple entities choose the same ones, the sector's exposures become correlated, which is a cost no individual bank prices in. We list the vulnerabilities below:

Correlated model risk: banks on similar models and the same foundation providers respond identically to an event. The regulator, at the system level, has to manage what happens when institutions move together, because no single entity has experience of such behaviour or of the impact synchronisation adds.

Third party concentration: one vendor's failure propagates everywhere at once. India has already run this experiment, when a ransomware attack on one shared technology provider knocked roughly three hundred cooperative and regional rural banks off the payments network.

Correlated cyber breach: shared stacks mean one exploited vulnerability is every institution's vulnerability. AI lowers the attacker's costs (automated vulnerability discovery, deepfake social engineering) and adds new attack surfaces (data poisoning, model inversion, prompt injection against agents that can move money).

Runs on banks at level 3. The bank runs of old were a queue on the pavement. Then we got to Silicon Valley Bank where over a weekend, customers took away money from the bank. Now we can be at level 3: autonomous agents managing customer cash can turn a shared signal into a self-reinforcing run at machine speed.

The various market failures listed above behave differently across the five modes of AI use. For example, when a human constructs the offer, opaque pricing can get contained. However, when an agent personalises autonomously at scale, this may become severe. Systemic correlation is moderate when AI advises and severe when fleets of similar agents act simultaneously. Any regulation that grades by model type alone, or by use case alone, misses half the object. The next question is the mode of regulation itself.

Regulatory strategy

Regulation can work in two ways. Process-based regulation is ex ante: it prescribes how the firm must operate, defines required controls, mandates oversight, validation standards, limits on autonomy. Outcome-based regulation is ex post: it prescribes ends, fair treatment, solvency, and judges results, leaving the choice of methods to the firm.

Outcome-based regulation is the efficient default. It is technology-neutral, so it does not ossify as methods change; it lets firms find the least cost route to compliance; and it does not require the regulator to understand the firm's production function better than the firm does. But it has important preconditions: the outcome must be observable and measurable; it must be attributable to the firm and, ideally, to the cause; and the harm must be reversible or compensable. Process regulation is the right departure from the default when those preconditions fail and where outcomes are unobservable, harm is catastrophic or irreversible, or damage manifests only systemically or with a lag.

Traditional doctrine treats these preconditions as given: examine the activity, choose the mode. This does not work for AI. Whether an AI deployment's outcomes are observable, attributable, and reversible is an engineering choice, which needs to be settled at design time. We suggest that regulation should mandate observability. This makes it possible to have an "outcome-based supervision" model. There are three ways to ensure observability.

1. Telemetry implies that the institution keeps a track of every decision such that the system records which version of itself it was using, what information it was given, and any time a person stepped in to overrule it. This makes outcomes attributable. That way if a certain group starts getting more (or less) approvals than before, the organisation can evaluate what caused the shift - was it the model, or the group itself. Such a capability is being mandated elsewhere in the world for similar use-cases (European Parliament and Council of the European Union, 2024). The regulator should also consider if it wants to set a minimum common telemetry standard.

2. Boundaries and rollback include putting caps on what the system is allowed to do, rolling out new updates to just a small number of cases first (instead of everyone at once), and having a tested plan for switching back to the older version if something goes wrong. This ensures that if a bad update slips through, it only affects a small slice of decisions.

3. Probes make bias observable. One way is "paired testing": one submits two applications that are exactly the same except for details that hint at things like someone's race or gender, and see if they get treated differently. One can also compare approval rates against the company's own normal levels. Together, these checks can catch an unfair credit model in just a few weeks, instead of waiting years to see who actually pays back their loans. One can also check rejected applicants against credit-bureau data to see which ones got approved by someone else, and how they fared.

These are similar to the idea of decision receipts that record which rules were applied to which facts and in what sequence for every decision made by a government or public system (Srivastava, 2026).

Over time, the supervisor should also build a repertoire of its own test cases drawn from incidents, complaints and examinations across the system and run it against every material AI deployment, much as stress-test scenarios are run against every balance sheet today. This will ensure that what surfaces in one institution becomes a probe for all others. It thus allows the regulator to set its own observability layer.

Process regulation is then reserved for the harms that are systemic, correlated, or irreversible at machine speed. For such events there need to be protections such as circuit breakers that halt things before they cause damage, limits on how much the systems can do on their own, model diversity so they don't all fail the same way, and rehearsed back-up plans.

What follows for the supervisor, the board, and the customer

For the supervisor: The unit of examination shifts from the model to the deployment, and the examiner's question shifts from "show me the validation report" to "show me the behaviour": what boundaries were set, what exceptions were thrown, what overrides were exercised, how far the system drifted from its baseline. Supervisors will also need to find the intellectual clarity to avoid a wide variety of extraneous claims about regulation of AI, e.g. the push for economic nationalism which has nothing to do with market failure.

For the board: A board cannot certify systems it cannot inspect or understand. Its role is to govern the framework which includes the limits on what the system is allowed to do, and making the rules for when a decision must be escalated to a human. The board then needs to continuously check the exceptions and overall performance.

Exception-handling should also be written into policy. If a problem stays unresolved beyond a defined size or time limit, it automatically gets escalated to the board. Internal auditors should double-check that the numbers are real. The board sticks to this audited framework and does not inspect the system directly.

If a board is asked to approve something they can't understand, they will default to saying no. But if you let them govern the limits and the exceptions instead, they can say yes.

The customer: The customer becomes part of the supervisory architecture. The widening firm-to-consumer asymmetry has a structural corrective the pre-AI world lacked: the customer now has AI too. Mainstream assistants abroad have begun connecting directly to users' accounts. If product terms (rates, fees, eligibility criteria) are mandated to be structured and machine-readable, the customer's own AI does the comparing, the explaining, and the policing of mis-selling, continuously and at zero supervisory cost. The redress channel weakened by opacity is restored the same way: an adverse decision should carry its reason to the customer, and what would have had to be different for the answer to change. This restores the ability to contest. The same asymmetry that AI widened, AI-equipped customers can close, but only if regulation hands them the data.

An example

Consider a debt-collection example. When borrowers fall behind on payments, the bank has to decide how to chase each overdue account. Contact methods differ in cost. Automated SMS and IVR (the automated phone system - "press 1 to pay") are cheap; having an employee actually phone the customer is expensive. So the bank builds a model that allocates accounts: cheap automated nudges for most, and the costly human call reserved for the accounts where the model predicts that talking to a person will actually "cure" the account (get it back to paying). Human calling works better but it costs more. The model is rationing an expensive resource to where it thinks it'll pay off.

With AI, the collection systems would retrain continuously or run reinforcement-style optimisation against a live reward (cure rate per rupee spent). The model would be moving on its own, faster than the review cycle, toward a target that is a proxy for what the bank may actually want. Under the conventional "inspect-the-artifact" approach, the model is checked before deployment, signed off, and reviewed on a schedule (say quarterly or annually). However, if AI is an active optimiser, a quarterly review discovers deterioration only after it has touched thousands of accounts. It may drift toward a mis-specified target in a way no one can read off the model itself. Further, if the collections model is a shared vendor product, or several banks fine-tune the same foundation model on similar data, they all go bad the same way at the same time when borrower behaviour shifts. In contrast, in a "supervisability-built in by design" approach, a small slice of accounts is deliberately kept on the previous allocation method, running live alongside the new model. This allows comparison between the old and the new in real time.

Every decision should keep a record of a few things: which version of the AI was used, what action it chose, which group of customers it was dealing with, and how things turned out in the end. This is how it helps: Say the AI's overall success rate is going up, but one particular group is quietly getting fewer phone calls from real people and doing way worse because of it. With all that recorded, that gap shows up right as it's happening. You can compare it against what's going well elsewhere, trace it back to a specific version of the AI, and undo it that same day, because the older setup is still up and running as a backup.

The company should never have to dig into the AI's inner "thought process" to realize its behaviour has taken a bad turn. None of this replaces the normal testing that the firm would do anyway. The difference is that here, the ability to observe what's happening, pin down what caused it, and shut it down are all built right into the system from the start, instead of being things you have to go do to the AI afterward.

Conclusion

AI deployments sit uneasily in conventional mores of banking regulation. The intelligence is rented, the behaviour is probabilistic, and harms can move at machine speed. We suggest that the response should not be more inspection of what cannot be inspected, nor blind faith in outcomes that arrive too late. Efficient regulation will emerge from observability, attributability, and reversibility. Regulators should require banks to build these.

References

Board of Governors of the Federal Reserve System, Federal Deposit Insurance Corporation, and Office of the Comptroller of the Currency. "Supervisory Guidance on Model Risk Management." SR Letter 26-2. April 17, 2026. https://www.federalreserve.gov/supervisionreg/srletters/SR2602.pdf.

European Parliament and Council of the European Union. Regulation (EU) 2024/1689 (Artificial Intelligence Act), arts. 12, 26(6), and Annex III(5)(b). https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-12.

Srivastava, Manish. "Digital Governance Needs Decision Receipts." Episode 73 of Big Ideas. XKDR Forum, June 1, 2026. Podcast, video, 13:06. https://youtu.be/WFX4ITb9yok

Digital transformation and the paradox of financial inclusion in India

by Suyash Rai.

India has made great strides in digital technology, becoming a leading exporter of digitally delivered services to the global economy. These capabilities with computer technology fuelled hopes that digital transformation could yield gains for the Indian state that are comparable to those seen in the private sector. The `Digital Public Infrastructure (DPI)' approach, with India's Aadhaar digital ID system as a prime example, is presented as a path to higher GDP growth for developing countries. There is an emerging debate on the role of the state in shaping the development and deployment of DPIs.

Two key pillars of the Indian story with DPIs are identity services ("Aadhaar") and their impact on financial inclusion. In a new working paper, Economic development and digital transformation: Learning from the experience of Aadhaar and financial inclusion in India, I critically examine the Indian progress on financial inclusion between 2011 and 2021, revealing a paradox: while account ownership surged, account usage remained low.

The facts

The paper analyses India's performance compared to other lower middle-income and middle-income countries. The evidence shows:

• Impressive account opening: India witnessed remarkable progress in account penetration, surpassing the average improvement in middle-income countries.
• High inactivity: A significant percentage of accounts in India were inactive, far exceeding the average for middle-income countries.
• Low account usage: India lagged behind in account usage for both consumption smoothing (regular deposits and withdrawals) and digital payments, indicating a gap between account ownership and actual financial inclusion.

The role of government mandates and Aadhaar

We argue that the rapid scale of account opening was caused by a series of government and Reserve Bank of India (RBI)mandates, particularly the Pradhan Mantri Jan Dhan Yojana (PMJDY). While Aadhaar played a role, it was primarily used as a physical ID for KYC, rather than as a digital ID through e-KYC. The gains in account opening may have a lot to do with state coercion and less to do with DPI.

The primary objective driving these initiatives was to facilitate direct benefit transfers (DBT) for welfare schemes. The government's focus on DBT aimed to reduce leakages and improve attribution for its welfare programs in the eyes of voters.

Why did this approach yield disappointing results?

The paper explores several reasons for the limited account usage despite the increase in account ownership:

• The lack of a viable business model: No-frills accounts, with zero minimum balance and free transactions, are commercially unattractive for banks.
• Mismatch between the solution and the problem: The focus on account opening for DBT didn't necessarily translate into accounts that address the richness and complexity of finance for the poor, of meeting the diverse needs of users for consumption smoothing and payments.

Lessons

The top-down approach, with a readiness to utilise the coercive power of the state, has limitations. While the government achieved its objective of scaling up DBT, this came at the cost of genuine financial inclusion and limited the potential uses of Aadhaar as a DPI.

We highlight the need for a more balanced approach, considering market forces and user needs, so as to obtain better outcomes with DPIs. We stress the importance of political creativity, institutional reforms, and a broader understanding of public value, beyond narrow fiscal objectives, when designing and implementing DPIs.

We offers insights into the complexities of digital transformation and financial inclusion, challenging the simplistic narrative of Aadhaar's success. These experiences invite us to rethink the role of the state in shaping DPIs and consider alternative approaches that can truly leverage technology for inclusive and sustainable development.

Suyash Rai is a Fellow at Carnegie India and a Visiting Research Fellow at the xKDR Forum

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Analysing India's KYC Framework: Can We Do Things Better?

by Rishab Bailey, Trishee Goyal, Renuka Sane, and Ridhi Varma.

Know-Your-Customer (KYC) norms require customers of the formal financial system to establish their identity through certain specific identification documents, prior to engaging in any transactions. According to World Bank's estimates, approximately 32 million adults in India do not have access to an officially recognized proof of identity. This puts the formal financial system out of reach for a significant number of people. Along with creating difficulties for customers, KYC requirements increase compliance costs for businesses including by lengthening customer onboarding time and heightening regulatory risks (Lyman and Noor, 2014). While India has seen progress towards limiting financial exclusions, not least due to the use of Aadhaar-based verification and e-KYC systems, the problems outlined above still remain.

In a recent paper Analysing India's KYC Framework: Can We Do Things Better? we ask what drives the current KYC policy framework? How is the Indian KYC framework different from that in other FATF compliant countries? Is there a possibility of doing KYC more efficiently in India?

Tracing global roots of domestic KYC requirements

The design of KYC norms derive from the requirements imposed by Financial Action Task Force (FATF). The FATF is an inter-governmental body that aims to prevent the use of the financial system for money laundering (ML), terrorist financing (TF) and weapons proliferation (WPF). To this end, it issues recommendations that lay down standards for member countries to adopt. While the recommendations of the FATF are directory in nature, a failure by a member state to adhere to them can have adverse implications on the state's economic interests. Accordingly, the FATFs recommendations shape domestic legislation. One of the cornerstones of the FATFs recommendations pertains to the need for reporting entities (financial institutions and other designated non-financial businesses) to carry out appropriate customer identification and verification. These requirements, broadly speaking, are aimed at ensuring greater transparency and accountability in use of the formal financial system.

Before India became a member of the FATF in 2010, it had implemented customer identification requirements on various financial entities under the Prevention of Money Laundering Act, 2002 ("PMLA") and rules issued thereunder. Sectoral regulators such as the Reserve Bank of India (RBI), Securities and Exchange Board of India (SEBI), Insurance Regulatory Development Authority of India (IRDAI) and the Pension Fund Regulatory and Development Authority (PFRDA) also lay down specific obligations in this regard. Cumulatively, these customer identification procedures are referred to as "Know Your Customer" or KYC norms. The PMLA framework has however been consistently amended, ostensibly in light of India's FATF obligations.

Key Challenges of the Indian KYC framework

While the FATF allows for a certain degree of flexibily in the design of KYC norms, these are not fully utilised in India. We find that India implements extremely stringent KYC requirements in comparison to countries such as Australia, Germany, UK, the EU and the US. Our analysis suggests that there are three main problems with the manner in which India implements KYC requirements:

1. Over-emphasis on address proof: The address proof requirements in Indian law are excessively detailed and rigid in nature. Indian regulators require customers to provide proof of multiple addresses at the KYC stage - current, permanent, and residence. The need to provide documentary proof of multiple addresses, and the lack of flexibility in this regard can prove problematic for marginalised or vulnerable sections of the population (Lyman and Noor, 2014). This is specially so as significant numbers of Indians, such as migrant labour, as well as nomadic communities, vulnerable communities, the homeless, etc., may not have any fixed or permanent address. Even in cases where the individual has a permanent address, they may not have officially valid documents (OVDs) that demonstrate their "current address". The need for providing a "current address" can be problematic despite the existence of various pan-India identification schemes (such as Aadhaar, passports, voter IDs, etc.). It is difficult and time-consuming to update addresses in these documents. Further, in order to update the address in these documents, the individual needs to provide some proof of a local/current address, which may not always be easily accessible. For instance, many migrants may not be part of the NREGA system (if they work in the private or informal sector), implying that they cannot use a NREGA job card for the purpose. The strict requirements under Indian law can therefore exacerbate the problem of financial exclusions.

2. Incomplete technological solutions: In order to reduce costs of conducting KYC, regulators have attempted to introduce various technological methods to enhance efficiency. These include the use of electronic KYC (e-KYC) methods as well as video KYC for on-boarding customers. Another technological intervention involves the creation of the Central KYC Registry (CKYCR), a centralised repository of customer KYC information. However, we find that the regulatory framework and implementation of these methods leaves much to be desired.

   For instance, in order to complete the video KYC process, entities under the purview of the RBI and SEBI can only accept ID documents that have been authenticated using the e-Sign facility that is provided by the DigiLocker system. Thus, a video KYC process is unavailable for those customers without a DigiLocker account. The fact that a DigiLocker account can only be created by Aadhaar holders creates another possible hurdle for customers. As per Dalberg's estimates, this requirement excludes around 8% of the population (of which 28 million are adults) from using the simplified KYC procedure. The video KYC process also requires a bank official to conduct the video-call in real time. This is resource intensive, which limits scaling. The video KYC process is also problematic from the perspective of the digital divide (Gera, 2020). The CKYCR process is also said to suffer from various implementation problems.

   Further, while banks are permitted to conduct e-KYC using Aadhaar, NBFCs are not. Even if a customer wants to use Aadhaar based authentication for KYC, non-banks and non-banking financial institutions, are forced to use the offline method of authentication. Anecdotal evidence as well as our interactions with industry practioners suggests that this leads to significantly increased costs for NBFCs. Customers too, are said to struggle with in-person verification processes. This difference therefore creates an uneven playing field, even for entities providing similar financial services.

3. Poor design of enforcement actions: We find that the unlike in various foreign jurisdictions, the Indian regulatory framework does not provide for sufficient enforcement options of a scaled or proportionate nature. The penalties applicable for even minor violations can be of a significant order - including in the form of criminal sanctions, cancellation of licenses and high fines. By way of comparison, regulators in the US, Australia and the UK have a much broader range of enforcement actions they can adopt, ranging from the issuance of advice or cautionary notices, to securing compliance undertakings.

   When combined with the absence of an appellate mechanism, the strict penalties under the Indian framework can incentivize financial institutions to adopt a conservative, risk-averse attitude. This can lead to businesses exiting markets and customer segments associated with higher risk of compliance failure - particularly where it comes to customer segments with low profit margins (Lowery and Ramachandran, 2015). This, paradoxically, heightens risks of money laundering and other financial crimes that KYC requirements were designed to solve in the first place (Lyman and de Koker, 2018). The significant penalties that can be imposed under the Indian regulatory framework are not only a regulatory risk for businesses, but also make it more difficult for regulators to apply penalties on a consistent basis, particularly for minor breaches. This promotes arbitrariness in enforcement actions.

Recommendations

We suggest liberalising and standardising address proof requirements within the existing FATF framework. The first step towards reform requires regulators to clearly delineate the purposes of securing an address proof. For example, if the purpose of securing documentation is merely to enable correspondence with the customer (as is indicated by a numnber of KYC forms that use the term "correspondence" address interchangeably with "current" address), a number of simpler methods can be used. For example, the Supreme Court has recognised that communications through e-mail and instant messaging can constitute valid service.

Further, entities under the domain of SEBI, IRDAI, PFRDA, in addition to requiring customers to prove existence of a bank account, also need documentary proof of the customer's current address. However, bank accounts themselves can be opened by providing a self-declaration of current address (where an OVD has been submitted for a permanent address). Thus, there appears to be little justification for the additional requirement of proving current address implemented by SEBI, IRDAI and PFRDA. That said, even if the requirement for proving current address is mantained, the manner of doing so could be liberalised. For instance, certain countries such as the United States permit a customer to merely specify a post box as a mailing address, or even use the address of a referee. These methods could be considered in India, as a means to limit the exclusionary effects of KYC norms.

Our analysis suggests that attempts to simplify the KYC procedure by adopting video and eKYC norms have also not achieved their true potential. This is for a variety of reasons concerning both the regulatory structure and implementation issues. For instance, the measures of simplification are largely limited to banks (and not NBFCs). NBFCs constitute a vital channel for financial inclusion. However, the restricted applicability of simplified KYC procedures have hindered their ability to reach customers by increasing operational costs and limiting scaling. In this context, one may consider permitting NBFCs to also utilise simplified KYC processes (though this may come at a cost to privacy interests). Regulators also need to be mindful of the digital divide issue in India. Accordingly, making simplified processes completely reliant on technical infrastructure such as internet facilities may be unwise. Therefore, in addition to simplifying the KYC requirements themselves, regulators must attempt enable multiple channels of verification. One may also consider the need to de-duplicate KYC processes. Notably, the report of the High-Level Committee on Deepening of Digital Payments advocates reducing KYC compliance requirements at multiple levels. For instance, it suggests implementing lower KYC requirements where a customer who already has a verified KYC compliant account, uses this to open a second account or an account with a mutual fund or payments wallet.

Finally, there is a need to streamline enforcement actions and allow for regulators to adopt a wider range of "softer" remedial actions. An appeals process must be provided from RBI decisions. The Financial Sector Legislative Reforms Committee (FSLRC) has made similar recommendations in its report. The FSLRC report, which is now nearly a decade old, advocates for a unified Financial Sector Appellate Tribunal to be created to hear appeals from all the financial sector regulators. Further it suggests that penalties and enforcement actions be reviewed to ensure proportionality. While entities in the banking sector have lobbied for the adoption of the FSLRCs recommendations, and discussions in this regard have taken place at the highest levels of government, no legal developments have yet taken place (Adhikari, 2018).

In conclusion, we recommend that the KYC framework in India be revised to better balance the goals of preventing financial exclusion, creating a level-playing field for businesses, and the need to prevent money laundering and other financial crimes.

References

Lyman and Noor, 2014: Timothy Lyman and Wameek Noor, AML/CFT and financial inclusion: New opportunities emerge from recent FATF action, CGAP Focus Note, No. 98, September 2014.

Isern and de Koker, 2009: Jennifer Isern and Louis de Koker AML/CFT: Strengthening financial inclusion and integrity, CGAP Focus Note No.56, August 2009.

Lowery and Ramachandran, 2015: Clay Lowery and Vijaya Ramachandran Unintended consequences of anti-money laundering policies for poor countries, Center for Global Development, Working Group Report, 2015.

Adhikari, 2018: Anand Adhikari Will the government push for an appellate tribunal for RBI?, Business Today, December 2018.

Lyman and de Koker, 2018: Timothy Lyman and Louis de Koker KYC utilities and beyond: Solutions for an AML/CFT paradox? , CGAP Blog Series, Beyong KYC Utilities, March 2018.

Gera, 2020: Ishaan Gera KYC over video? Yes, RBI makes it possible with tweaks in rules, Financial Express, February 2020.

The authors are researchers at NIPFP.

Grievance Redress by Courts in Consumer Finance Disputes

by Karan Gulati and Renuka Sane.

India has made progress on financial inclusion through the use of digital payments and fintech. As more and more consumers interact with the consumer finance industry, there will invariably be greater frictions and an increasing number of grievances. In an environment with a good consumer complaints system, these should get resolved by the financial service provider (FSP), and if not the FSP, then the regulator. However, this is not so in India. Courts are often the preferred recourse for retail consumers. For example, in the ongoing dispute regarding Yes Bank's written off AT-1 bonds, consumer courts seem like the last remaining alternative for retail investors. Unless grievances are satisfactorily resolved, we may hurt the progress made on financial inclusion. While India needs to set up good regulator-based grievance redress mechanisms such as a Financial Redress Agency, it also needs to improve the functioning of courts to provide effective relief in consumer finance (and other)disputes. In a recent paper, Grievance Redress by Courts in Consumer Finance Disputes, we review 60 judgments on consumer finance to study the position that courts have taken on these disputes. We also describe the challenges in court functioning that have a bearing on the efficiency of courts in dealing with issues of grievance redress.

The structure of courts

In 2020, India enacted a new Consumer Protection Act (CPA). The Act aims to protect consumers' interests and provide timely and effective settlement of disputes. It entrusts courts to redress consumer grievances. A complainant can approach specialised courts i.e. consumer commissions established by the CPA. However, these are additional remedies. Cases may also be decided by the High Court of various States and the Supreme Court of India.

The powers to grant relief depend on which court the complainant approaches. Consumer commissions are bound by the CPA. They may order a party to: (i) remove defects, (ii) return the price of the goods or the charges for the services along with interest, (iii) pay compensation or punitive damages, and (iv) withdraw the goods or services from the market. High Courts are bound to decide cases either within the confines of a statute under which they are approached or the constitution. Going one step further, the Supreme Court has held itself not restricted in any way to grant adequate relief.

Banking and insurance disputes

Litigation is disproportionately costly and troublesome for small consumers. Very rarely can an ordinary consumer go through the prolonged ordeal of fighting with a bank. For this reason, courts have granted relief to individual consumers, given that they come with clean hands.

This has not been the case when interpreting insurance contracts. If consumers knew about the terms, courts have enforced the terms of the contract, regardless of whether the terms themselves were unfair, one-sided, or opaque. On the other hand, if the terms were kept hidden from the consumer, courts have granted relief to consumers. This is true both while entering the contract and settling claims.

Several consumers have been introduced to complex products and contracts, but these consumers have insufficient know-how. They are vulnerable to mis-selling. The strategy in Indian finance has historically focused on the caveat emptor doctrine -- let the buyer beware. Though the new CPA gives consumer commissions the power to declare certain unfair terms as void, it does not address the ability to understand the terms. Thus, consumers have been left to their own devices, and unaware consumers are unlikely to get their desired remedy if they approach a court.

Challenges to court functioning

We find the following challenges in court functioning as they deal with consumer finance disputes.

1. Low Compensation: Courts tend to award low compensation that does not adequately compensate the complainant. For example, in Dr Virendra Pal Kapoor v. Union of India and Ors, a senior citizen had invested INR 50,000 in a unit-linked product in 2007. Upon payout in 2012, he had lost the entire sum except INR 248 on account of hidden charges. Though the insurer was directed to repay the original Rs. 50,000, no interest was awarded. The reason for low compensation seems to be that there are no guidelines for courts to follow. There is no expert analysis of the loss. In the absence of financially prudent legislation, courts often tend to award compensation that only makes sense when the legislation is enacted.

2. Delay: Low compensation becomes more severe when it takes too long to settle disputes. The CPA provides that cases should be decided in no more than five months. However, as per the case management system of the National Commission, it takes 1.99 and 2.38 years to settle banking and insurance disputes, respectively, i.e. more than five times the statutory guideline. In fact, in February 2020, the National Commission adjourned a matter till January 2021 - almost a year after the hearing.

3. No Class Action: If consumers cannot understand complex financial agreements, they may benefit from pooling their knowledge and approaching courts as a class. Plaintiffs can share evidence, expert witnesses, and litigation costs. However, unlike other countries, such suits are few and far between in India. This may be because of unclear substantive law and strict rules on financing litigation. This makes it difficult for class members to come together. Courts have left it to their discretion to evaluate whether the class is adequately represented and whether financing agreements are fair. Moreover, the legislature had prohibited contingency fees. This creates a system that either prohibits or disincentives class actions.

4. Specialisation: Consumer courts in India resolve all consumer disputes. Though the members are highly qualified individuals, they lack specialization in finance. This is unlike other common law countries where sectoral experts adjudicate finance disputes. They have adopted extensive adjudicatory legislation regarding financial products and services. On the other hand, laws in India regarding finance have been restricted, leaving courts to start from a clean slate. If timeliness and predictability can make India's finance regime more appealing, specialization by adjudicators could prove valuable.

Way forward

One obvious way to improve the system is by general improvements in the judiciary's capacity and knowledge on matters related to finance. This will, however, take a long time. Policymakers should also consider adopting certain targeted interventions.

There are two types of interventions that are required. The first is on the legislative front. Like the targeted legislation in other countries, the legislature could enact separate rules for financial transactions mandating clear and understandable disclosures. Policymakers may also consider prescribing adequacy requirements in class action suits and transitioning towards contingency fees for lawyers and third-party investors. Any such changes in legislation would also benefit from an advisory council on consumer finance. The council may be responsible for making representations about policies; reviewing, monitoring, and reporting their effectiveness; and highlighting its views on new rules and regulations.

The second is on the judicial front. One problem we identify is low compensation. This may be addressed by updating and consolidating the rules governing compensation considering modern market understanding. Other jurisdictions often order disgorgement (surrender of profits earned through illegal means) or grant a remedy of restitution. This seeks to measure actual damages. On the question of delays, courts may also separate their judicial and administrative functions. This will likely reduce the time it takes to conclude hearings since members of the commission would have more time to focus on their judicial tasks. The National Commission can also exercise its power to call for statistics from State Commissions and conduct systematic reviews.

These solutions can have significant consequences, especially in India, where financial literacy is low and regulatory enforcement appears weak. Though they were developed after studying consumer finance disputes, they may have consequences outside this domain and yield better functioning courts. Market-oriented compensation, without delay, when parties can come together as a class would be beneficial in any dispute. In a growing financial landscape such as India, redress bodies such as the judiciary become increasingly important. A specialized consumer protection law is a step in the right direction, but it can benefit from targeted interventions.

References

Department of Economic Affairs, Report of the Financial Sector Legislative Reforms Commission: Volume 1, March 2013.

Dhirendra Swarup, Establishing the Financial Redress Agency, January 27 2017, The Leap Blog.

Dr Virendra Pal Kapoor v. Union of India and Ors, May 29 2014, Allahabad High Court.

Karan Gulati and Renuka Sane, Why do we not see class-action suits in India? The case of consumer finance, May 03 2020, The Leap Blog.

Karan Gulati and Shubho Roy, India's low interest rate regime in litigation, March 11 2020, The Leap Blog.

Murali Krishnan, Supreme Court urges consumer forum to look into grievance of year-long adjournments, August 16 2020, Hindustan Times.

National Informatics Centre, Computerization and Computer Networking of Consumer Forum in the Country.

Neil Borate, Those mis-sold Yes Bank AT1 bonds face long haul, May 11 2020, LiveMint.

Pratik Datta, Mehtab Hans, Mayank Mishra, and others, How to Modernise the Working of Courts and Tribunals in India, March 25 2019, NIPFP Working Paper No 258.

Reserve Bank of India, National Strategy for Financial Inclusion, January 10 2020.

Supreme Court Bar Association v. Union of India, April 17 1998, Supreme Court of India.

Tinesh Bhasin, RBI sees 387% rise in complaints against NBFCs, 58% rise against banks, February 08 2021, LiveMint.

The authors are researchers at NIPFP.

Get by with a little help from my friends (and shopkeepers): Household borrowing in response to Covid 19

by Renuka Sane and Ajay Shah

The lockdown in the early days of the Covid 19 pandemic in India impacted on on economic activity. Between April and August 2020, 18.9 million salaried people lost their jobs, difficulties were faced by migrant labourers, and small and medium businesses. Deshpande (2020) shows that overall employment dropped sharply post-lockdown, with larger drops for women than men. Household incomes were adversely affected. As an example, survey work by Lee, Sahai, Baylis and Greenstone (2020) shows that two months into the lockdown poor and non-migrant workers in Delhi saw a drop of 57% in their incomes, with 9 out of 10 workers reporting that their weekly income had fallen to zero. Bertrand, Krishnan and Schofield (2020) measure the fraction of households who say they are able to survive on their own for a week, and in April that value was 34% in the overall population and 50% or more for below-median household income.

How would households cope with such a shock? Economic theory suggests that households desire consumption smoothing. One mechanism for consumption smoothing is borrowing. For example, there was an increase in household borrowing after demonetisation (Karmarkar and Narayan, 2020; Wadhwa, 2019; Chakraborty and Sane, 2019). This connects to the working of the financial system. While India has made a lot of progress in ownership of a bank account, and increased electronic payments, access to formal credit remains low.

What do we expect about household borrowings?

Borrowing during the Covid crisis is shaped by three factors:

1. Income transfers: The government of India announced a stimulus package worth Rs.1.7 trillion after the lockdown. This included food security measures as well as direct cash transfers to poor households. This may have helped households deal with the immediate crisis.

2. Low demand: As people were at home owing to the lockdown, demand may have been affected. It is also possible that households saw this job loss as permanent, and hence cut back on expenditures in a way they would not have had they seen this as a temporary disruption. This also fits with the view that precautionary savings increase after a deep crisis (Rajadhyaksha, 2020). However, this may be true for households in the higher income distributions, but is unlikely to be the case for those below median income.

3. Supply constraints: India's financial system has faced difficulties since 2012. This has manifested itself as business failure at ILFS and other financial firms, large and small. Credit growth was decelerating prior to the lockdown. The difficulties for the financial sector increased when the Reserve Bank of India announced a moratorium on all loan repayments for three months from March to May 2020, and then extended it for another three months. These moratoriums made it more difficult for financial firms to assess the credit quality of borrowers. Overall bank credit growth was 5.8% in September 2020 compared to 8.1% in September 2019. From 2018 onwards, when certain borrowers faced supply constraints, they would have had to deleverage (repaying old loans while not getting new ones) or default.

The grand question of the field consists of understanding the economic condition of households in India in 2020, in examining how consumption was held up through new kinds of labour supply and through borrowing, and in obtaining insights into these three distinct economic forces that are in play. In this article, we discover some new facts that contribute towards this overall research agenda.

Methodology

We source data from the Consumer Pyramids Household Survey (CPHS) for the months of May, June, July and August from the years 2016 - 2020. The borrowing data comes from the Aspirational India table within CPHS. Using this we ask three questions:

1. Did households have debt outstanding at the time of the survey? This helps us understand the total number of borrowers in the economy.
2. What are the sources from whom households have outstanding borrowings? This tells us whether households borrow from the formal or the informal sector.
3. What is the purpose for which households have outstanding borrowings? This tells us if households are borrowing for consumption expenditure, for consumer durables, or for running their businesses.

CPHS does not provide information on the value of debt outstanding. We are, therefore, not able to analyse the impact on borrowing on an intensive margin. Our analysis is restricted to understanding the proportion of households borrowing from various sources, for various reasons, i.e. on the extensive margin. Household weights for each wave are provided by CPHS -- these are used to get population estimates.

Results: The number of borrowers

Table 1 presents the number and percentage of households having debt outstanding in the months of May - August in each of the five years. The number of borrower households had been consistently increasing till 2019. In May - August 2016, 12% of the population had debt outstanding. This increased to 50% by 2019. The number, however, fell in 2020 to 45% of the population. The fall has been greater in urban regions than rural.

Table 1: Number and share of borrowers in the population

WAVE	NATIONAL	RURAL	URBAN
	in million	in million	in million
May - Aug 2016	34.8 (12.3%)	22.8 (12.0%)	11.9 (13.0%)
May - Aug 2017	81.9 (28.3%)	56.7 (29.1%)	25.1 (26.6%)
May - Aug 2018	136.0 (45.6%)	94.0 (46.8%)	42.0 (43.0%)
May - Aug 2019	154.7 (50.5%)	105.1 (51.1%)	49.6 (49.4%)
May - Aug 2020	141.6 (45.1%)	99.6 (47.2%)	42.0 (40.7%)

Disentangling explanations: Sources of borrowing

Given that a large proportion of households did not have enough to live on for more than a couple of weeks, we would have expected a huge increase in the number of borrower households. In order to investigate the sources of this drop, we begin by analysing the role of the financial system in the household borrowing story by studying the sources of borrowing.

Table 2 presents the percentage of borrower households borrowing from each source. We find that the biggest drop in borrowing is from banks: in 2019, 26% of borrower households had borrowed from banks - this has dropped to 20% in 2020. The proportion of households borrowing from money lenders has also dropped - from 7% in 2019 to 4% in 2020. The drop in households borrowing from banks and money lenders was higher in urban regions than rural regions. There has been a lot of discussion in India about the increased risk aversion of banks. A fall in the number of borrower households may be a result of this phenomenon.

Table 2: Sources of borrowing

SOURCE	May - Aug 2019 Rural	May - Aug 2019 Urban	May - Aug 2020 Rural	May - Aug 2020 Urban
Banks	26.6%	25.6%	21.9%	15.3%
Money Lenders	7.1%	7.1%	4.6%	3.4%
Employer	0.5%	1.4%	0.5%	1.0%
Relatives/Friends	14.5%	13.3%	21.1%	27.2%
Shops	52.0%	50.7%	57.6%	49.8%

There has been a concurrent rise in the number of households who have borrowed from friends and family from 14% in 2019 to 21% in rural regions, and from 13% to 27% in urban regions. The sharp increase in the borrowing from friends and family suggests that some smoothing of consumption expenditure is likely to have occurred using informal social networks that play an important role in the economic lives of those in developing countries (Munshi, 2014).

Household borrowing from shops increased in rural India - from 52% to 58%, and fell slightly in urban India. It is interesting to recall that Chakraborty and Sane (2019) had found that between the years 2016 and 2018 (i.e. after demonetisation), the biggest rise in borrowing was from shops, especially by those in the lower income deciles. This seems to be true in the current situation as well, especially in rural regions.

In difficult times, it was not banks, money lenders, and employers that mattered. It was friends and family, and the neighbourhood shops. It appears that non-financial firms and cash flow management by the retail supply chain have been more important than financial firms. The connections from the formal financial system to these shops could then be unusually influential.

Disentangling explanations: Purpose of borrowing

Examining the purpose for which households borrow can tell us something about the demand for credit. Table 3 presents the top five reasons for borrowing in 2020, and compares it with 2019.

Table 3: Purpose of borrowing

SOURCE	May - Aug 2019 Rural	May - Aug 2019 Urban	May - Aug 2020 Rural	May - Aug 2020 Urban
Consumption	62.3%	59.8%	70.1%	65.7%
Business	12.9%	9.1%	19.2%	8.2%
Debt Repayment	7.3%	8.8%	9.1%	11.9%
Housing	7.3%	9.7%	2.4%	4.7%
Durables	5.1%	8.6%	1.6%	4.3%
Investments	6.4%	1.9%	0.4%	0.5%

In May-August 2019, 62% of rural and 60% of urban borrower households had borrowed for reasons of consumption expenditure. In May-August 2020, this had risen to 70% and 66% of rural and urban borrower households respectively. This is consistent with the importance of consumption smoothing, and of many households not having enough resources to survive for more than a few weeks. The increase for reasons of consumption expenditure is higher in urban regions. Urban India was more likely to be affected because of both the Covid infections and the intensity of the lockdown than rural India. Income transfers from the government are also likely to have targeted rural households than urban households.

There has been an increase in borrowing for business and debt-repayment reasons in this period as well. The numbers for rolling over debt went from about 9% to 12% of borrower households in urban regions, and from 7% to 9% of borrower households in rural regions. This suggests that households who would otherwise have serviced debt through business or personal income took recourse to borrowing when those cashflows subsided. A personal insolvency law that is able to provide some relief to debtors and allow for restructuring of the larger loans can help alleviate some of this stress.

The fall in the number of borrower households seems to be driven by the fall in the borrowings for housing, durables purchase and investments. It is also likely that large purchases such as housing and durables are made through bank loans. The fall in the borrowings from banks may be a result of a fall in these large durable purchases.

Conclusion

We study the response of households on borrowings during the 2020 lockdown. We do not have data on the value of debt outstanding. We expected that there would be an increase in the number of households that borrow owing to the disruptions to economic activity. However, it is remarkable, that despite the large shock, overall, there has been a reduction in the number of households that borrow. This fall is driven by fewer households borrowing from banks, and fewer households borrowing for housing, and consumer durables purchases. Households continue to borrow for consumption expenditure, business and debt repayment. The most utilised sources of borrowing are friends and family and shops.

This work suggests many interesting possibilities for downstream research. For example, one can study the differences in borrowing patterns between households with different income and wealth profiles, as well as the correlation between sources and purpose of borrowing. It will also be possible to evaluate whether income transfers from the government led to a fall in the number of borrower households. Similarly, one can ask whether different health outcomes play a role in their borrowing outcomes.

The number of households choosing to borrow has been different from what happened after demonetisation. There may be several reasons for this - the magnitude of the disruption, the length of time for which it lasted, the possibility of more permanent impacts on labour markets among others. This leaves us with interesting research possibilities to understand household behaviour and their interaction with financial markets.

References

Ashwini Deshpande (2020), The Covid-19 Pandemic and Lockdown: First Effects on Gender Gaps in Employment and Domestic Work in India, Working Paper 30, Ashoka University.

Azim Premji University (2019), "State of Working India 2019", Technical Report, Centre for Sustainable Employment.

Kaivan Munshi (2014), "Community Networks and the Process of Development", Journal of Economic Perspectives, 28(4), pp: 49-76.

Kenneth Lee, Harshil Sahai, Patrick Baylis, and Michael Greenstone (2020), "Job Loss and Behavioral Change: The Unprecedented Effects of the India Lockdown in Delhi", Working Paper, EPIC India.

Marianne Bertrand, Kaushik Krishnan, and Heather Schofield (2020), "How are Indian households coping under the COVID-19 lockdown? 8 key findings", Rustandy Centre for Social Sector Innovation, Chicago Booth.

Niranjan Rajadhyaksha (2020), "The covid shock could alter people's financial priorities", Livemint, 5 May 2020.

Sagar Wadhwa (2019), "Impact of demonetization on household consumption in India, Working paper.

Subhamoy Chakraborty and Renuka Sane (2019), "Household debt over time", The Leap Blog, 24 May 2019.

Sudipto Karmarkar and Abhinav Narayanan (2020), "Do households care about cash? Exploring the heterogeneous effects of India's demonetization", Journal of Asian Economics, 69.

 

Sane is a researcher at the National Institute of Public Finance and Policy, Shah is an independent scholar. We thank four anonymous referees, Kaushik Krishnan, Radhika Pandey and Anjali Sharma for useful comments.

RBI vs. Covid-19: Understanding the announcements of March 27

by Rajeswari Sengupta and Josh Felman.

When the first cases of Covid-19 started getting reported in India, the economy was already in a precarious situation and the space for a macroeconomic policy response was limited. Even so, the Reserve Bank of India has come up with a number of initiatives to combat the crisis. In this article, we consider the broad principles that should guide the macro policy response, summarise the RBI announcements of March 27, and assess the announcements against the principles.

Background

The "corona crisis" consists of three interlinked problems: a health shock, an economic shock following from the lockdown, and a global economic downturn. Each one of these shocks on its own is significant. Put together, they have created considerable pressure upon policy makers to act quickly and decisively.

Coming up with an effective policy response is not an easy task. For one thing, the corona crisis poses some exceptional difficulties. It is clear that the human and economic toll will be serious, but it is unclear how long the crisis will last or how deep the damage will be. And without a clear understanding of the size and duration of the problem, it is difficult to know how to calibrate the policy response. For example, monetary easing could take a year to have a significant effect. By then the problem might be over, and inflation might have re-emerged, at which point painful measures would be required to bring it down. This is not just a theoretical possibility, it is precisely what happened in the aftermath of the Global Financial Crisis in 2009-13.

Principles of policy response

Policy making is difficult in the best of times. It is harder in exceptional times, when there is pressure for quick actions, grounded in reduced analysis. It is in exceptional times that the toolkit of good governance becomes even more important:

• The lowest cost actions are those which are grounded in root cause analysis.
  
• Each action needs to be carefully weighed in terms of the costs and benefits imposed upon society.
  
• As much as possible, policy responses should be fitted into existing rules and frameworks.
  
• All state actions should be preceded by public debate and consultation.
  
  

This toolkit is a valuable discipline, an institutionalised application of mind. Why is root cause analysis important? Consider the problem of weak banks lending to firms in recent years. From 2018 onwards, RBI has been trying to address this problem by injecting more and more liquidity into the banking system, in the hope that banks would deploy these resources and lend more (link, link, link). But liquidity issues were not at the root of the problem, the twin balance sheet (TBS) stresses at firms and banks were the real issue. Bank lending has also been discouraged by the government’s measures to investigate and prosecute bank officials for their lending decisions. As a result of these factors, banks have remained reluctant to lend to the private corporate sector, curtailing credit to industry to a year-on-year growth rate of just 0.67 percent in February 2020.

As an example of poor cost-benefit analysis, consider the regulatory decisions after the Global Financial Crisis. At the time, it was felt that exceptional times called for exceptional deviation from prudent financial regulation. A series of restructuring schemes followed, allowing banks to postpone NPA recognition and hide bad news. With the benefit of hindsight, we know that this restructuring worked poorly, and helped prepare the ground for the twin balance sheet crisis of 2011-2020.

As for respecting frameworks, there is a temptation during crises to abandon rules and resort to discretion. But recent experience warns us that "temporary measures" are often difficult to reverse (consider the 2010 fiscal stimulus), while inadvertent consequences (such as NPAs) are difficult to resolve. More fundamentally, temporary measures disrupt the stable configuration of expectations of economic agents, which hamper the recovery. It takes many decades of consistent behaviour in a rules-based framework to shape the rhythm of the working of state institutions, to build up policy credibility. This credibility can be rapidly dissipated.

Hence, policy makers need to proceed cautiously.

The March 27 announcements

It is in this context that we need to examine the March 27 announcements. Four bold actions were taken, following an "out of cycle" i.e., unscheduled Monetary Policy Committee (MPC) meeting:

• The repo/reverse repo rates were cut by sizeable amounts, to 4.40/4.00 percent from 5.15/4.90 percent. The 91-day treasury bill rate, which measures the de facto stance of monetary policy, dropped to 4.31 percent from 5.09 percent on 26 March.
  
  
• Ordinarily, banks can borrow on a short-term basis from the RBI using the repo window. To supplement this facility, a new `targeted long-term repo operations' (T-LTRO) mechanism, with a limit of Rs.1 trillion, was announced. Banks may find this attractive because they do not have to mark to market the investments made with these borrowed funds for the next three years. However, there is a condition: the money that is borrowed here must be deployed in investment-grade corporate bonds, commercial paper, and non-convertible debentures, over and above the outstanding level of their investments in these bonds as on March 27, 2020.
  
  
• The cash reserve ratio (CRR) was reduced by 1 percentage point, bringing it down to 3% of deposits ("net demand and time liabilities"). This is the first time the CRR has been changed in the last 8 years. RBI's initiatives appear to be motivated by the desire to increase liquidity, as their statement highlights that these measures will free up Rs 3.74 trillion in banks' funds.
  
  
• Banking regulation requires banks to recognise and provide for a loan when there is a delay in payment. According to the Prudential Framework for Resolution of Stressed Assets, banks are required to classify loan accounts in special mention categories in the event of a default. The account is to be classified as SMA-0, SMA-1 and SMA-2, depending on whether the payment is overdue for 1-30 days, 31-60 days or 61-90 days, respectively. RBI has now modified this regulation, so that banks can offer a moratorium of 90days for term loans and working capital facilities for payments falling due between March 1, 2020 and May 31, 2020. However interest on the term loans will continue to accrue during this period. If a firm applies for and receives a moratorium, the loan account in consideration will continue to be recognised as a standard asset and the SMA classifications will no longer apply. Interest on term loans will continue to accrue during this period. 

Analysing the monetary policy announcements

Monetary policy is most effective when economic agents understand and can anticipate the behaviour of the MPC. This process of learning and understanding is still underway, given that India is in the early years of building up the credibility of the inflation targeting framework and the MPC process. So, one would have expected that the MPC statement would go into great details and spell out its macroeconomic forecast, explaining why it believed the 75 basis points rate cut was consistent with its commitment to the 4 percent inflation target.

However, tt did not explain the rate decision in the context of a revised inflation forecast, or any other element of a macroeconomic forecast. It did not offer a justification for the magnitude of rate cut chosen.

Since the rate cut announcement was not couched in the standard IT framework, the public does not have the assurance that the rate cuts will be reversed when inflation begins to rise again. To remedy this problem, monetary policy actions could henceforth be couched in terms of this framework, as a way of assuring the public that the RBI is keeping its eye on this critical objective, and that the mistakes of the past will not be repeated.

Analysing the banking regulation announcements

We know that the corona crisis is a temporary shock. Standard economic theory tells us that the optimal response to a temporary shock is for (viable) firms and households to obtain financing, so that they can tide over the difficult period. Over the next few months, three categories of firms will emerge: a) firms that are able to pay their dues throughout the crisis period, b) firms that are fundamentally viable and can survive provided they are given adequate credit support, and c) firms whose business is faulty and who should become bankrupt as a result of this shock.

It will be important for the banks to distinguish among these firms. Banks should ideally do nothing with firms in category (a), extend credit support to firms in category (b), and take the firms in category (c) to the insolvency and bankruptcy courts as and when that process resumes.

Under the 27 March package, the RBI has given regulatory approval to banks and other lending institutions to decide which of their customers needs a 90-day deferral. This decision, to allow banks but not require them, to grant moratoria is a good one, as it allows banks to distinguish among the three types of firms.

However, the plan is not without drawbacks.

• No mechanism has been created to classify the loans that will be rescheduled, so transparency has been lost. Investors – already nervous because of accounting surprises at Yes Bank and other financial institutions – will consequently provide capital only at a cost marked up to reflect this information risk premium. And this increase in banks’ costs will be passed on to the borrowing corporate sector.
  
• Moratoria will create problems for pass-through certificates, i.e. loans that have been bundled as bonds and sold to mutual funds, because there are no provisions in these certificates for loan rescheduling.
  
• Finally, and most importantly, there is no clarity on what happens once the moratorium period is over. How will banks clean up the mess that will be created later, as many of the firms which benefited from the moratorium end up defaulting? There will be a new wave of NPAs, which we know from experience will be difficult to resolve.
  

There is also a risk: now that a "temporary" moratorium has been introduced, there will be pressure for it to be extended again and again. If the RBI is unable to resist, we will quickly find ourselves back in the 'extend and pretend' era of post-2008. Banks, investors, the RBI, will all be navigating in a fog, since no one will know – and hence, be able to deal with -- the true size of the bad loan problem.

In other words, under the current design, there are risks that the costs of the moratoria could end up exceeding the benefits. Is there an alternative? In fact, two supplementary actions could reduce potential costs, while preserving the benefits.

First, RBI could announce that firms seeking a moratorium would be marked in a separate category. This would give transparency regarding the true financial situation of the banks. There will also have been a bit of a stigma for borrowers, helping to preserve debtor discipline. If a firm has no choice, it will still postpone repayment. But if a firm can afford to pay, it will do so, in order to escape the stigma.

Second, forward planning could help deal with the consequences of the inevitable surge in defaults. Even before the corona crisis, bankruptcy cases were taking far longer than what the law stipulates. Large cases were taking several years to resolve. If this situation is not addressed, there is a risk that large sections of the economy will be tied up in bankruptcy courts, making it impossible for the economy to return to normal, even after the virus abates. To make sure this does not happen, the Insolvency and Bankruptcy Code (IBC) needs to be reformed urgently in order to ensure faster and effective resolution. Such reforms would also have an immediate benefit: banks would be more confident in lending now if they knew the IBC would not be overwhelmed by cases after the crisis is over.

Reviving credit growth

The need of the hour is to revive credit to the private corporate sector. But the marginal benefit of the RBI adding more liquidity to a system that is already in a surplus mode is not clear. This strategy has already been tried, without success. It is unclear why it would work now, especially now that uncertainty about firms' prospects has only increased.

For a proper root cause analysis, let’s go back to economic fundamentals. Consider a loan decision. When a bank decides to approve a loan, it is performing two functions simultaneously: it is assuming risk, and it is allocating capital. In the current circumstances, it is still possible for banks to allocate capital. They can assess which firms are more likely to be hit badly by the crisis and which firms are going to be less affected. That is, banks can figure out the relative risk. The problem for the banks is that right now they cannot assess the absolute level of risk, because they do not have any idea about how long the crisis is going to last, or how deep the crisis is going to be. And this shock has come at a time when banks have already become risk-averse given the last few years of balance sheet problems. Hence, it is difficult for them to lend, especially to new customers.

In these circumstances, giving them liquidity, exhorting them, coming up with any number of subsidy schemes, will not work. But there is a possible solution. The government-- not the RBI -- could relieve the banks of the burden that they cannot manage: the burden of risk.

This can be done through a mechanism as follows. The government can capitalise a fund which will then give loan guarantees. The scheme would have some selection criteria, say MSMEs that have been current on their bank loans. It would also specify the maximum rupee amounts per firm, pegged say to the annual revenues of the company. Once the eligibility criteria are specified by the government, the actual selection of the firms would be done by the banks. They would identify the best firms, originate the loans, and then apply to the fund for guarantee coverage. The banks should be charged a fee for this, to discourage them from using the fund unnecessarily.

In this way, we could use the law of comparative advantage to obtain better economic outcomes: the government would do what it does best in crises, namely bearing risk, while the banks would continue to do what they do best, namely allocating capital.

Conclusion

The RBI’s March 27 announcements were bold and decisive. In particular, the reduction in the repo rate by 75 basis points will provide significant debt service relief to firms and households. This is a welcome measure, at a time when their cash flows are going to be seriously strained. The announcement that banks will be allowed to grant temporary debt moratoria to firms and households could also prove a major help, for exactly the same reasons.

That said, the announcements could have been better grounded in basic principles. The root causes of the banks’ reluctance to lend have not been addressed. At the same time, the way the policy actions were designed and announced run the risks of damaging confidence in the existing frameworks. The public may not be so sure that the authorities remain committed to preserving low inflation or financial stability. Nor is it clear that there is an "exit strategy", to ensure that the defaults will be resolved expeditiously, allowing the economy to return quickly to normal, once the health crisis is over.

There is still time to clear up these ambiguities, and remove any doubts. Initial actions can be followed by supplementary steps, and initial problems can always be remedied. This will take careful root cause analysis, cost-benefit calculations, and a determination to reinforce existing policy frameworks.



Josh Felman is a researcher specialising on India. Rajeswari Sengupta is a researcher at IGIDR.

Unsophisticated households and banks versus securities

by Ajay Shah.

The borrowing of banks through deposits

When a household deals with a bank, there is a clear promise by the bank, that the deposit will be redeemable at par with some interest that is known up front. But how is a household to verify that the promise will be met at future dates? Monitoring a bank every day is hard for unsophisticated investors. Unsophisticated households face asymmetric information, a market failure.

In order to address this market failure, we do two things in financial regulation. First, we have micro-prudential regulation. The regulator coerces banks to bring down their failure probability to an acceptable level. A good thumb rule for Indian conditions is to aim for a failure probability of 2% on a one-decade horizon. This requires two elements of work: forcing banks to mark their assets to market so that bad loans are valued at fair market value, and a leverage rule which caps the leverage of banks. Second, we require a Resolution Corporation to deal with bank failure: a specialised bankruptcy process, which pays out deposit insurance to households (Rai, 2017).

This is the well understood regulatory apparatus that is brought into play when banks borrow through bank deposits, which go alongside high intensity promises.

Resource mobilisation by firms through the securities markets

How should we think about households and investment in securities (equity or debt)? Conversely, what should a financial regulatory apparatus do when a firm (a bank, an NBFC or a non-financial firm) wants to issue shares or bonds on the primary market?

A key difference on the stock market or the bond market is the lack of a promise. No promise is made, either about liquidity or about the price at which a future transaction will take place. This immediately improves the situation from a regulatory standpoint. Investors walk into buying bonds or shares with their eyes open, no promises are made to them.

Hence, we do not need to worry about micro-prudential regulation of the issuer when an investor buys shares or bonds on an exchange.

What about the primary market? In a primary issue, there is the risk of an advertising campaign that makes lurid promises to unsophisticated investors. This is addressed nicely by having a rule which requires that a minimum x% of the primary issue (of either bonds or shares) be purchased by sophisticated investors, and these investors be locked in for a certain short period. A good definition of a `sophisticated investor' for this purpose is a person who invests a minimum of Rs.10 million in the issue. Once the issue passes the market test of appealing to such investors, it is safe for households to participate directly in the primary market for securities.

Under such conditions, the gatekeepers for resource mobilisation through the primary issuance of shares or bonds are sophisticated investors and not the state. If a firm had poor prospects, or mispriced its securities, it would not get the support of these investors, and the issue would fail. How much leverage, and what debt characteristics, are appropriate for a highway or a steel company or an NBFC? There is no need for the government to get involved in terms of micro-prudential regulation or interference in the price. The only role of the state is in the adequacy and truthfulness of disclosures that are made at the time of the issue.

As there is no high intensity promise by an NBFC, failed NBFCs should go to the ordinary IBC process. The need for the Resolution Corporation, in handling firm default, is only when a systemically important NBFC fails.

When a bank borrows using the bond market, this changes the overall leverage of the bank, and the bank would of course have to comply with micro-prudential rules that cap its leverage. But there is nothing special about the primary issue of a bank, when compared with the reasoning above.

Conclusion

NBFCs in India are facing many difficulties. However, micro-prudential regulation of NBFCs is not the answer. There is no need for the state to get involved, or engage in micro-prudential regulation, of bond issues by banks, NBFCs (Roy, 2015; Shah 2018) and non-financial firms.

The sophisticated investors on the primary market are the gatekeeper; unsophisticated households free ride on their price discovery.

The Companies Act should not interfere in the bond issuance of companies, and RBI should not micro-prudentially regulate NBFCs.

The reticence of the bond market in lending to some NBFCs, from August 2018 onwards, is market discipline at work.

References

The regulatory difficulties of NBFCs in India, Shubho Roy, The Leap Blog, 24 December 2015.

Movement on the law for the Resolution Corporation, Suyash Rai, The Leap Blog, 19 June 2017.

Financial regulation for the Fintech world, Ajay Shah, The Leap Blog, 21 March 2018.

Delays in deposit insurance

by Karan Gulati, Shubho Roy, Renuka Sane.

In late 2017, the government introduced the Financial Resolution and Deposit Insurance (FRDI) Bill which proposed a new resolution framework for banks and financial firms. It planned an overhaul of the present system operated jointly by the Reserve Bank of India (RBI) and the Deposit Insurance and Credit Guarantee Corporation (DICGC), and introduce a modern Resolution Corporation with more extensive powers to regulate and resolve banks.

The bill faced resistance in Parliament. The opposition stated that the bill risked the solvency of public sector banks and accused the government of putting public money at risk. Some argued that the current system of deposit insurance would be taken away by the proposed FRDI law. The Bill was said to have been “designed to punish small depositors for the sins of defaulters, corrupt bank managers and political masterminds”. The bill was withdrawn in 2018. As a result, the DICGC has remained the insurer for bank deposits.

In this article, we attempt to measure how the DICGC has fared in processing bank failures and settling claims of depositors.

Present system

Today, the DICGC acts as a pay-box. Under its eponymous law of 1961, the DICGC insures the deposits of banks created by parliamentary (central) legislation, private banks, and eligible co-operative banks. Eligible co-operative banks are a subset of co-operative banks where the state legislature has empowered the RBI to exercise some regulatory oversight. Under the 1961 Act, if a bank insured by DICGC is wound up or has its license cancelled by the RBI, every depositor is entitled to insurance of up to Rupees 100,000.

The law envisages a quick payout to reduce inconvenience to depositors due to a bank failure. Within three months of being appointed, every liquidator of a bank must provide to DICGC, a list of depositors with the amount due to each one of them (S.17(1)). The DICGC, then makes the insurance payout. The law (S. 17 (2)) requires DICGC to pay claims within two months of receiving the list from the liquidator.

Data

We source data from annual reports of the DICGC and RBI notifications concerning the cancellation of license/ de-registration of 87 banks from 2013 to 2018. Since payouts to the depositors can take place in multiple tranches, we observe 127 payouts for these banks. We note the year of payout under the DICGC Act, the number of depositors, and the amount of claims settled. We were able to trace RBI notifications for 78 banks, which accounted for 95% of all depositors, and 99% of the amount of payouts. We measure the time taken between the RBI notification (which we assume to be analogous with the exit of the bank), the disbursement of the payments; and the opportunity cost of the amount of claims at 8% and 20% per annum.

The hand-collected data set is accessible here.

Overall performance

Bank failures are especially disruptive for depositors. Banks work on the promise of providing deposits callable at par. A bank makes a promise to allow the depositor to withdraw their money within one banking day. This gives confidence to the depositor to use banks for their daily needs rather than hoarding cash. Though there have been no commercial bank failures in India in the recent past, this hides a deep and persistent problem of co-operative bank failures in India.

Cases of Bank Failures handled by the DICGC from 2013 to 2018

Year	Number of Payouts	Depositors	Claims (Rupees million)
2013-14	51	96590	1030.93
2014-15	30	185901	3212.89
2015-16	17	90792	471.44
2016-17	10	35215	586.37
2017-18	19	56173	435.19
Total	127	464671	5736.82

As the table above shows, on average, the DICGC has made 25 payouts each year. More than 400,000 depositors had to use the deposit insurance scheme. These are not small numbers. Their experience of the deposit insurance payout will be the basis of the trust they will repose in the banking system, the regulator, and the deposit insurance scheme.

Time taken to disburse payments

Under the DICGC Act, the liquidator is supposed to provide a list of claimants to DICGC within three months of her appointment. However, a major cause of delay for the payouts is that the claims list is not received from the liquidator within the stipulated time limit. This may be because cases filed against the liquidator are in court, appeals by the bank are pending before the Ministry of Finance (Appellate Authority), or clarifications are required about the claims list.

The process of disbursement of payments needs to be expedient so that depositors do not suffer undue losses and maximum value is derived from the failed institution. To measure the effectiveness of DICGC we calculate the opportunity cost of the delay in payments.

Money today is worth more than money tomorrow. Since the depositors are unable to access their money, which was promised to be to be callable at par, they face a loss. Depositors have three choices (i) find an alternate source/borrow, (ii) forgo consumption or, (iii) forgo investment. The time taken by DICGC for the disbursement of the due amount imposes a cost on the depositors. The standard measure for delayed payment is the opportunity cost of the money. It is calculated by discounting the amount with a discount rate over the time by which it was delayed. For example, if the depositor is owed Rs. 100; and the insurance payout delay is 1 year; and the discount rate is 10%, then a payout of Rs. 100 at the end of the year is effectively a payout of only Rs. 91. If the payout is delayed by 2 years; effective payout is Rs. 82.64.

We use two rates of discounting to measure the opportunity cost: 8% and 20%. 8% is slightly above the risk-free rate of return and can be considered as the minimum opportunity cost of money. However, if you are poor or an individual, it is almost impossible to borrow at this rate. So we choose another realistic rate of 20%. This is the rate at which the government requires buyers to compensate MSMEs for delayed payments (S. 16, MSMED Act).

As the graph above shows, it took an average of 2.10 years for the disbursement of the amount by DICGC. This is five times the statutory limit of five months. Similarly, the median time taken is more than four times the statutory limit. The pay-out process took over 5 years in almost 1/3rd of the cases of failures. A reading of the Annual Reports of DICGC shows that in two cases of bank failure, it took over 14 years for the insurance claims to be disbursed.

As of 20th March 2019, there were another 25 bank failures pending before DICGC, where depositors have been waiting for an average of 6.57 years. Two of these banks were de-registered 20 years ago and DICGC is yet to receive the claims list from the liquidator.

Year-wise metrics of delay in disbursement

Year	Average Time Taken (years)	Claims (million)	Opportunity Cost at 8% (million)	Opportunity Cost at 20% (million)
2013–14	1.16	1030.93	930.18	814.21
2014–15	2.31	3212.89	2642.44	2030.18
2015–16	1.35	471.44	410.30	342.16
2016–17	2.16	586.37	496.86	399.19
2017–18	3.86	435.20	322.56	216.32
Total	2.10	5736.83	4802.34	3802.05

As Table 2 shows, opportunity costs have risen over the years. At a conservative discount rate of 8%, depositors in 2017-18 effectively got only Rs. 322.56 million, for claims of Rs. 435.2 million, a 26% loss. This is due to the increasing delays in processing payouts. In 2013-14, the loss rate was only 10% of the value of insurance. However, 8% is an optimistic rate. At a realistic rate of 20%, depositors lost more than 50% of the value of payouts in 2017-18. At the realistic rate, depositors have lost over one third the value of their insurance in the past five years due to delays.

Proposed System

One of the reasons for the delay is due to the fact that that DICGC itself does not have the power to obtain the list of depositors in a bank. It has to depend on the liquidator to provide the list. This explains a significant portion of the delays. As shown here, in a number of cases the DICGC is waiting for the liquidator to provide the list of insurance claimants.

The FRDI Bill solves this problem through two measures:

1. Allowing the Resolution Corporation to take over the management of a bank at risk before it stops banking activities and is bankrupt.
2. Combining the function of the liquidator/receiver and insurer in the same agency.

The Bill proposed a mechanism for an early warning system for banks at risk of failure. Banks would be classified into five categories ranging from low to critical. If a bank was classified in higher risk categories it would have to formulate its resolution plan which would include information about depositors [S.44]. The bill also allowed the Resolution Corporation to take over the management of banks at critical risk (S. 46) which is before bankruptcy (unlike the present system). This would give the Resolution Corporation the power to get into the books of the bank before it failed and consequently give it more time to make a list of insurance claimants without delay to depositors.

This is unlike the present system where the DICGC enters the process only after the bank has been declared insolvent by the RBI and is unable to pay its dues (see here, here and here). By coming in before a bank has failed, the Resolution Corporation would have the ability to analyse its operational statements, books of records, and list of depositors entitled to payouts in the event of such a failure.

Another cause of the delay is that the liquidator and DICGC are independent of each other. The liquidator is appointed after a Bank has been ordered to be wound up, and is not part of the DICGC. The DICGC hence has no authority in preparing the list of eligible claimants and has to depend on the liquidator to provide it. In contrast, the proposed Resolution Corporation would have acted as the liquidator and insurer for a co-operative bank in the event of a failure (S. 62). As such, the corporation would not have to depend on an external party to prepare a list of claimants.

Conclusion

The process under the current regime is slow. It takes close to two years after the cancellation of license for the disbursement of claims. Since most co-operative banks service poorer clients, their failure hurts people who are not in a position to forgo their deposits for long periods of time. Today, without a framework for bankruptcy and orderly resolution for financial firms, India faces the risk that if a large private sector bank goes bankrupt, the depositors could be stuck for years before getting their money back.

The focus of the deposit insurance corporation needs to shift from payouts after default, into the problem of identifying weak banks and stopping them. Many countries have developed specialised resolution regimes for various categories of financial firms. The Financial Stability Board recommends operational independence as a key attribute of resolution regimes. In several jurisdictions, including the USA, Canada, Malaysia, Mexico, Japan, Korea, etc., these are in the form of separate institutions with resolution powers.

References

A welcome retreat: withdrawing the FRDI Bill, The Hindu, 10th August 2018.

FRDI Bill: Panacea for banking sector set for quiet burial after PNB scam?, Business Standard, Archis Mohan, 22nd February 2018.

Movement on the law for the Resolution Corporation, by Suyash Rai in The Leap Blog.

Raghuram Rajan and Ajay Shah. New directions in Indian financial sector policy. In Priya Basu, editor, India’s financial sector: Recent reforms, future challenges, chapter 4, pages 54–87. Macmillan, 2005.

The demise of Rupee Cooperative Bank: A malady, by Radhika Pandey and Sumathi Chandrashekaran in The Leap Blog.

The Financial Resolution and Deposit Insurance Bill 2016, Department of Economic Affairs.

Won’t let Centre pass FRDI Bill in Parliament: Abhishek Banerjee, The Indian Express, Express News Service, 26th December 2017.

 

Shubho Roy and Renuka Sane are researchers at the National Institute of Public Finance and Policy and Karan Gulati is a law student at Symbiosis Law School, Noida. An earlier version of this article incorrectly mentioned 127 banks instead of 87. The article was edited on November 01, 2020.