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In Delhi there is a bank branch where a lot of money was stolen from people over the country. This bank branch is where all the money disappeared. The people who did this did not wear masks. Break in at midnight. They just used a passbook a rubber stamp and a form that nobody checked carefully. This is the truth that the people who investigate cybercrime keep finding. The way that cybercriminals get away with the money is not by using a computer it is by using a bank account. The police in Delhi who investigate cybercrime have found that a lot of accounts were opened at bank branches. These accounts were opened using identity documents that were borrowed bought or stolen. Then these accounts were rented out to groups of criminals. One bank branch keeps coming up in complaints. This is not bad luck it is a sign of a bigger problem with how banks check who is opening an account.

These fake accounts, which are called " accounts" are controlled by criminal groups, not the people whose names are on the accounts. These accounts are a part of the cybercrime problem in India. The mistakes that bank branches make which allow these accounts to be opened raise a lot of questions. These questions are about how banks check who is opening an account how they prevent money laundering and how they work with groups to stop cybercrime. The bank accounts are the way that cybercriminals in India get away with the money they steal from people. The cybercrime investigators keep finding bank accounts like the ones at the bank branch, in Delhi, where the money was stolen.

The Anatomy of a Mule Account Network

The pattern is now familiar to investigators. A fraud complaint on the National Cyber Crime Reporting Portal traces a victim's stolen money to a beneficiary account. When police pull the account-opening file, the person named on the KYC documents often denies ever visiting the branch or signing the forms; signature verification frequently shows a mismatch. In one recent Delhi case, a cooperative bank's deputy manager was arrested after a single account he had helped open surfaced in 159 separate cyber fraud complaints from across the country, with transactions worth nearly Rs 68 crore routed through it before detection. Similar investigations have uncovered supply gangs that procure dozens of accounts at a time using POS machines, stacks of ATM cards, and cheque books belonging to different people and rent them out to fraudsters as ready-made conduits for stolen money.

What makes a single branch or a small cluster of accounts significant is what it reveals about entry-point failure. Investigators do not describe these as sophisticated hacking operations; they describe them as verification failures as are accounts opened without the mandatory in-person checks, video KYC, or document authentication that RBI rules require. When 96, or 700, or 8.5 lakh mule accounts are traced back through a handful of branches and intermediaries, the story is not really about the fraudsters at the far end of the chain. It is about the choke point where honest oversight should have stopped the account from ever existing.

Where the KYC Framework Is Breaking Down

The RBI's Know Your Customer Master Direction requires banks to establish customer identity, verify a genuine business relationship, and apply risk-based due diligence before allowing an account to operate. In practice, investigators have repeatedly found accounts opened through complicit or negligent bank staff, business correspondents, and third-party agents who bypass these checks entirely. Analysts note that mule accounts systematically exploit gaps in customer onboarding, KYC verification, transaction monitoring, and dormant-account surveillance, with criminals using forged or stolen identity documents and layering funds across multiple accounts to escape detection. Economically vulnerable individuals who are daily-wage workers, students, the unemployed are frequently paid a small commission to hand over their documents or existing accounts, often without understanding that they could face criminal liability for transactions they never authorised.

This is compounded by a financial-inclusion paradox that regulators themselves acknowledge: India has expanded banking access faster than it has expanded financial and digital literacy, leaving a population that is easy to recruit knowingly or unknowingly  into mule networks. The result is a KYC regime that looks robust on paper but is only as strong as its weakest branch-level implementation, and weak implementation has proved trivially easy for organised networks to locate and exploit at scale.

The Regulatory and Institutional Response

RBI: From Static Compliance to Active Detection

The Reserve Bank of India has moved beyond periodic KYC audits toward technology-driven detection. It has directed banks to tighten onboarding controls, strengthen transaction monitoring, and report suspicious activity more proactively, and it has proposed additional safeguards, including limits on aggregate credits into accounts where a satisfactory business relationship has not yet been established. Its most significant intervention is MuleHunter.ai, an AI and machine-learning system built to flag suspected mule accounts from transaction-behaviour patterns rather than static KYC data alone; the platform is already operational across roughly two dozen banks and is being expanded. The RBI Innovation Hub has also begun working directly with the Indian Cyber Crime Coordination Centre (I4C) to share fraud-risk intelligence and coordinate detection in near real time.

FIU-IND and the PMLA Framework

The Prevention of Money Laundering Act, 2002 (PMLA) is the backbone of India's AML architecture. It mandates KYC verification, Customer Due Diligence, record maintenance, and timely reporting of suspicious transactions to the Financial Intelligence Unit–India (FIU-IND). Banks are required to file Suspicious Transaction Reports (STRs) and Cash Transaction Reports with FIU-IND, which in turn analyses financial intelligence and shares it with law enforcement and regulators. On paper, this creates a feedback loop between banks, the RBI, and enforcement agencies; in practice, the sheer volume of mule-linked transactions are hundreds of thousands of accounts flagged nationally  has strained the capacity of this reporting chain to generate timely, actionable freezes before funds are withdrawn or converted to cryptocurrency.

The IT Act, CERT-In, and Cyber Enforcement

The Information Technology Act, 2000, together with provisions of the Bharatiya Nyaya Sanhita, provides the criminal-law basis for prosecuting mule account operators, aggregators, and the fraudsters who direct them. CERT-In's role sits slightly upstream of the banking layer: it issues advisories on phishing, fake payment gateways, and compromised digital infrastructure that fraud syndicates use to recruit mule account holders and move money. The Ministry of Home Affairs' I4C coordinates the National Cyber Crime Reporting Portal and the 1930 helpline, which allow victims to report fraud and trigger a limited window for freezing beneficiary accounts. I4C has also issued direct public alerts against illegal payment gateways built on mule accounts, warning citizens not to rent or sell their bank credentials to intermediaries.

The Coordination Gap

None of these institutions is short of legal authority. The gap is operational: banks, the RBI, FIU-IND, state police cyber cells, the CBI, and I4C each hold a piece of the picture, but no single agency has a real-time, end-to-end view of an account from opening to fraud to freeze. A mule account can be flagged by one bank's internal monitoring, reported through a completely different victim's complaint in another state, and investigated by a third jurisdiction's cyber police with each step introducing delay. The Indian Banks' Association has publicly pushed for the RBI to be given clearer power to directly freeze accounts flagged as mule accounts, rather than requiring each bank to act unilaterally or wait for a police request, precisely because this fragmentation lets fraudsters withdraw or launder funds within hours of a transaction.

Policy Recommendations

1. Mandatory video-KYC and biometric re-verification for all new accounts opened through business correspondents and third-party agents, with personal liability for verifying bank officials found complicit.

2. A statutory, RBI-backed mechanism allowing banks to freeze accounts flagged by MuleHunter.ai-type systems or FIU-IND intelligence within hours, rather than only after a formal police complaint.

3. A unified, interoperable case database linking the National Cyber Crime Reporting Portal, FIU-IND's STR system, and state cyber cells, so that an account flagged once is visible to every agency instantly.

4. Stronger due-diligence audits of banking correspondents and cooperative banks, which recur disproportionately in mule account cases relative to their share of total accounts.

5. Public financial-literacy campaigns targeted at the economically vulnerable groups most often recruited as unwitting mule account holders, paired with clear legal guidance distinguishing victims from willing participants.

Conclusion

The branch-level mule account cases surfacing across Delhi and other cities are not isolated policing stories; they are a live audit of India's AML and KYC architecture. The RBI, FIU-IND, CERT-In, and law enforcement agencies each have credible tools and legal mandates like MuleHunter.ai, PMLA reporting, IT Act prosecutions, and I4C's coordination portal chief among them but fraud syndicates continue to outpace the system by exploiting the seams between institutions rather than any single point of failure. Closing that gap requires less new law and more operational integration: faster account freezes, verified accountability at the point of account opening, and a shared, real-time picture of mule networks across every agency involved. Until banks, regulators, and investigators can act as one system rather than several disconnected ones, every dismantled racket will simply be replaced by the next.

References

1. https://aninews.in/news/national/general-news/delhi-police-arrests-bank-deputy-manager-in-83776792-crore-mule-account-case-linked-to-159-cyber-fraud-complaints20260610130737/
2. https://the420.in/delhi-bank-manager-mule-account-cyber-fraud-case/
3. https://www.business-standard.com/finance/news/what-are-mule-accounts-cybercrime-banking-layer-india-fraud-rbi-126062400855_1.html
4. https://www.business-standard.com/india-news/centre-freezes-450-000-mule-bank-accounts-used-in-cyber-fraud-schemes-124111200320_1.html
5. https://www.medianama.com/2025/04/223-iba-rbi-cyber-fraud-measures-freeze-bank-accounts-cybercrime/
6. https://www.deccanherald.com/amp/story/india%2Fcentre-warns-of-illegal-payment-gateways-and-mule-accounts-3252723
7. https://www.deccanherald.com/india/over-85-lakh-mule-accounts-in-700-bank-branches-used-by-cyber-criminals-cbi-3604229
8. https://website.rbi.org.in/en/web/rbi/-/notifications/master-direction-know-your-customer-kyc-direction-2016-updated-as-on-may-04-2023-lt-span-gt-11566
9. https://www.indiacode.nic.in/bitstream/123456789/15402/1/moneylaunderingact2002.pdf
10. https://www.indiacode.nic.in/bitstream/123456789/13116/1/it_act_2000_updated.pdf
11. https://www.mha.gov.in/en/division_of_mha/cyber-and-information-security-cis-division/Details-about-Indian-Cybercrime-Coordination-Centre-I4C-Scheme

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Introduction

Quantum mechanics is not a new field. It finds its roots in the works of physicists such as Niels Bohr in the 1920s, and has informed the development of technologies like nuclear power in the past. But with developments in science and engineering, we are at the cusp of harnessing quantum mechanics for a new wave of real-world uses in sensing and metrology, computing, networking, security, and more. While at different stages of development, quantum technologies have the potential to revolutionise global security, economic systems, and digital infrastructure. The science is dazzling, but it is equally urgent to start preparing for its broader impact on society, especially regarding privacy and digital security. This article explores quantum computing, its threat to information integrity, and global interdependencies as they exist today, and discusses policy areas that should be addressed.

What Is Quantum Computing?

Classical computers use binary bits (0 or 1) to represent and process information. This binary system forms the base of modern computing. But quantum computers use qubits (quantum bits) as a basic unit, which can exist in multiple states ( 0, 1, both, or with other qubits) simultaneously due to quantum principles like superposition and entanglement. This creates an infinite range of possibilities in information processing and allows quantum machines to perform complex computations at speeds impossible for traditional computers. While still in their early stages, large-scale quantum computers could eventually:

1. Break modern encryption systems
2. Model complex molecules for drug discovery
3. Optimise global logistics and financial systems
4. Accelerate AI and machine learning

While this could eventually present significant opportunities in fields such as health innovation, material sciences, climate modelling, and cybersecurity, challenges will continue to arise even before the technology is ready for commercial application. Policymakers must start anticipating their impact.

Threats

Policy solutions surrounding quantum technologies will depend on the pace of development of the elements of the quantum ecosystem. However, the most urgent concerns regarding quantum computing applications are the risk to encryption and the impact on market competition.

1. Cybersecurity Threat: Digital infrastructure today (e.g., cloud services, networks, servers, etc.) across sectors such as government, banking and finance, healthcare, energy, etc., depends on encryption for secure data handling and communications. Threat actors can utilise quantum computers to break this encryption. Widely used asymmetric encryption keys, such as RSA or ECC, are particularly susceptible to being broken. Threat actors could "harvest now, decrypt later”- steal encrypted data now and decrypt it later when quantum capabilities mature. Although AES-256, a symmetric encryption standard, is currently considered resistant to quantum decryption, it only protects data after a secure connection is established through a process that today relies on RSA or ECC. This is why governments and companies are racing to adopt Post- Quantum Cryptography (PQC) and quantum key distribution (QKD) to protect security and privacy in digital infrastructure.

2. Market Monopoly: Quantum computing demands significant investments in infrastructure, talent, and research, which only a handful of countries and companies currently possess. As a result, firms that develop early quantum advantage may gain unprecedented competitive leverage through offerings such as quantum-as-a-service, disrupting encryption-dependent industries, or accelerating innovation in pharmaceuticals, finance, and logistics. This could reinforce the existing power asymmetries in the global digital economy. Given these challenges, proactive and forward-looking policy frameworks are critical.

What Should Quantum Computing Policy Cover?

Commercial quantum computing will transform many industries. Policy will have to be flexible and be developed in iterations to account for fast-paced developments in the field. It will also require enduring international collaboration to effectively address a broad range of concerns, including ethics, security, privacy, competition, and workforce implications.

1. Cybersecurity and Encryption: Quantum policy should prioritise the development and standardisation of quantum-resistant encryption methods. This includes ongoing research into Post-Quantum Cryptography (PQC) algorithms and their integration into digital infrastructure. Global policy will need to align national efforts with international standards to create unified quantum-safe encryption protocols.

2. Market Competition and Access: Given the high barriers to entry, regulatory frameworks should promote fair competition, enabling smaller players like startups and developing economies to participate meaningfully in the quantum economy. Frameworks to ensure equitable access, interoperability, and fair competition will become imperative as the quantum ecosystem matures so that society can reap its benefits as a whole.

4. Ethical Considerations: Policymakers will have to consider the impact on privacy and security, and push for the responsible use of quantum capabilities. This includes ensuring that quantum advances do not contribute to cybercrime, disproportionate surveillance, or human rights violations.

5. International Standard-Setting: Setting benchmarks, shared terminologies, and measurement standards will ensure interoperability and security across diverse stakeholders and facilitate global collaboration in quantum research and infrastructure.

6. Military and Defence Implications: Militarisation of quantum technologies is a growing concern, and national security affairs related to quantum espionage are being urgently explored. Nations will have to develop regulations to protect sensitive data and intellectual property from quantum-enabled attacks.

7. Workforce Development and Education: Policies should encourage quantum computing education at various levels to ensure a steady pipeline of talent and foster cross-disciplinary programs that blend quantum computing with fields like machine learning, AI, and engineering.

8. Environmental and Societal Impact: Quantum computing hardware requires specialised conditions such as extreme cooling. Policy will have to address the environmental footprint of the infrastructure and energy consumption of large-scale quantum systems. Broader societal impacts of quantum computing, including potential job displacement, accessibility issues, and the equitable distribution of quantum computing benefits, will have to be explored.

Conclusion

Like nuclear power and AI, the new wave of quantum technologies is expected to be an exciting paradigm shift for society. While they can bring numerous benefits to commercial operations and address societal challenges, they also pose significant risks to global information security. Quantum policy will require regulatory, strategic, and ethical frameworks to govern the rise of these technologies, especially as they intersect with national security, global competition, and privacy. Policymakers must act in collaboration to mitigate unethical use of these technologies and the entrenchment of digital divides across countries. The OECD’s Anticipatory Governance of Emerging Technologies provides a framework of essential values like respect for human rights, privacy, and sustainable development, which can be used to set a baseline, so that quantum computing and related technologies benefit society as a whole.

References

• https://www.weforum.org/stories/2024/07/explainer-what-is-quantum-technology/
• https://www.paconsulting.com/insights/what-is-quantum-technology
• https://delinea.com/blog/quantum-safe-encryption#:~:text=This%20can%20result%20in%20AES,%2D128%20to%20AES%2D256.
• https://www.oecd.org/en/publications/a-quantum-technologies-policy-primer_fd1153c3-en.html

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AI and other technologies are advancing rapidly. This has ensured the rapid spread of information, and even misinformation. LLMs have their advantages, but they also come with drawbacks, such as confident but inaccurate responses due to limitations in their training data. The evidence-driven retrieval systems aim to address this issue by using and incorporating factual information during response generation to prevent hallucination and retrieve accurate responses.

What is Retrieval-Augmented Response Generation?

Evidence-driven Retrieval Augmented Generation (or RAG) is an AI framework that improves the accuracy and reliability of large language models (LLMs) by grounding them in external knowledge bases. RAG systems combine the generative power of LLMs with a dynamic information retrieval mechanism. The standard AI models rely solely on pre-trained knowledge and pattern recognition to generate text. RAG pulls in credible, up-to-date information from various sources during the response generation process. RAG integrates real-time evidence retrieval with AI-based responses, combining large-scale data with reliable sources to combat misinformation. It follows the pattern of:

• Query Identification: When misinformation is detected or a query is raised.
• Evidence Retrieval: The AI searches databases for relevant, credible evidence to support or refute the claim.
• Response Generation: Using the evidence, the system generates a fact-based response that addresses the claim.

How is Evidence-Driven RAG the key to Fighting Misinformation?

• RAG systems can integrate the latest data, providing information on recent scientific discoveries.
• The retrieval mechanism allows RAG systems to pull specific, relevant information for each query, tailoring the response to a particular user’s needs.
• RAG systems can provide sources for their information, enhancing accountability and allowing users to verify claims.
• Especially for those requiring specific or specialised knowledge, RAG systems can excel where traditional models might struggle.
• By accessing a diverse range of up-to-date sources, RAG systems may offer more balanced viewpoints, unlike traditional LLMs.

Policy Implications and the Role of Regulation

With its potential to enhance content accuracy, RAG also intersects with important regulatory considerations. India has one of the largest internet user bases globally, and the challenges of managing misinformation are particularly pronounced.

• Indian regulators, such as MeitY, play a key role in guiding technology regulation. Similar to the EU's Digital Services Act, the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021, mandate platforms to publish compliance reports detailing actions against misinformation. Integrating RAG systems can help ensure accurate, legally accountable content moderation. 
• Collaboration among companies, policymakers, and academia is crucial for RAG adaptation, addressing local languages and cultural nuances while safeguarding free expression. 
• Ethical considerations are vital to prevent social unrest, requiring transparency in RAG operations, including evidence retrieval and content classification. This balance can create a safer online environment while curbing misinformation.

Challenges and Limitations of RAG

While RAG holds significant promise, it has its challenges and limitations.

• Ensuring that RAG systems retrieve evidence only from trusted and credible sources is a key challenge. 
• For RAG to be effective, users must trust the system. Sceptics of content moderation may show resistance to accepting the system’s responses. 
• Generating a response too quickly may compromise the quality of the evidence while taking too long can allow misinformation to spread unchecked.

Conclusion

Evidence-driven retrieval systems, such as Retrieval-Augmented Generation, represent a pivotal advancement in the ongoing battle against misinformation. By integrating real-time data and credible sources into AI-generated responses, RAG enhances the reliability and transparency of online content moderation. It addresses the limitations of traditional AI models and aligns with regulatory frameworks aimed at maintaining digital accountability, as seen in India and globally. However, the successful deployment of RAG requires overcoming challenges related to source credibility, user trust, and response efficiency. Collaboration between technology providers, policymakers, and academic experts can foster the navigation of these to create a safer and more accurate online environment. As digital landscapes evolve, RAG systems offer a promising path forward, ensuring that technological progress is matched by a commitment to truth and informed discourse.

References

• https://experts.illinois.edu/en/publications/evidence-driven-retrieval-augmented-response-generation-for-onlin 
• https://research.ibm.com/blog/retrieval-augmented-generation-RAG 
• https://medium.com/@mpuig/rag-systems-vs-traditional-language-models-a-new-era-of-ai-powered-information-retrieval-887ec31c15a0 
• https://www.researchgate.net/publication/383701402_Web_Retrieval_Agents_for_Evidence-Based_Misinformation_Detection 

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