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Introduction
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The recent investigation of Patan Cyber Crime Police as part of Operation Mule Hunt 2.0 reveals the sheer scale and intricacy of India's burgeoning cyber fraud economy. Police found that a total of 13 current accounts were being operated at a cooperative bank in the Patan district of Gujarat and used for siphoning 398.43 crore of cyber fraud transaction data on 228 cybercrime cases across states. Further investigations against 14 current account holders and intermediaries show the indispensability of mule accounts in laundering criminal money. The recent incident cannot be taken as isolated; the story points at a formalised and industrialised fraud economy with a robust banking infrastructure, a growing payment gateway, and complex networks.
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What Is a Mule Account and Why Should You Care?
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The term "mule account" is benign but plays a critical role in modern cybercrime networks. The Reserve Bank of India defines a mule account as a bank account that serves as a vehicle to transfer money proceeds from unlawful transactions and can be operated by people coerced by the prospect of high earnings or by way of inducement.

This mechanism can be witnessed through the investigation of the Patan cybercrime incident, where an investor can be defrauded by a fake investment website, employment fraud, or a digital arrest scheme. After transactions from the victim account, funds would quickly flow into the mule account, which would be held by a legitimate KYC customer. These transactions would then be passed on, between 1 lakh and 5 lakh transactions within hours, to multiple accounts as alleged by the Indian Cyber Crime Coordination Centre (I4C) before they get difficult to trace by being passed through informal channels or converted to cryptocurrency.

In the Patan case, it is alleged that the middlemen enticed locals and offered commissions to open firms and current accounts at Harij Nagrik Sahakari Bank and subsequently gave up their ATM cards, checkbooks, SIMs, and net banking facilities to the operators of the account. It is estimated that such accounts channeled an amount of 398.43 crore to 228 Indian cybercrime cases.

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The Scale of India's Mule Account Crisis

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The scale of the mule account ecosystem is reflected in India's rapidly worsening cybercrime statistics. As of data from the National Cyber Crime Reporting Portal (NCCRP), a total of 22.68 lakh complaints were registered in 2024, a jump by 42% from 2023. This was not even half the rate of financial loss, which jumped by 206% in 2023 (22,845 crore) and stood at 22,495 crore in 2025 (complaints jumped to 28.15 lakh). The increase in fraudulent transactions therefore outweighs the stability in financial losses significantly.

Mule accounts are the backbone of this crime network. To curb this phenomenon, the Indian Cyber Crime Coordination Centre (I4C) launched a Suspect Registry along with Indian banks and financial institutions in September 2024. 24.67 lakh accounts of suspected mules were identified in this, preventing over 8,031 crore in fraudulent transactions. Despite these efforts, a recent statement from the ED found over 12,000 crore being routed via mule accounts, shell firms, and cryptocurrency.

This isn't isolated to certain banks. 2024 alone saw over 65,000 mule accounts detected in Karnataka. By analyzing the Citizen Financial Cyber Frauds Reporting and Management System, about 40,000 such accounts were detected in SBI branches, and thousands more were detected across the PNB, Canara Bank, Kotak Mahindra Bank, and Airtel Payments Bank. The Patan case also clearly highlights that cooperative banks' lack of compliance and lower levels of transaction-monitoring systems contribute to easily creating and using mule accounts.

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Operation Mule Hunt: Gujarat's Coordinated Offensive

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This bust in Patan is just one manifestation of a much wider coordinated effort by the state government. Operation Mule Hunt 1.0, which ran from November to December 2025 across the state of Gujarat, was a month-long campaign by Gujarat Police's Cyber Centre of Excellence (CCOE) that unearthed 2,289 crore of fraudulent transactions, led to the registration of 565 FIRs, arrest of 638 accused, and impounding of 913 mule accounts with connections to over 4,000 cases of cybercrime nationwide.

This was followed up with the second installment of the operation, which was kicked off in all districts of Gujarat in 2026. The two-week campaign, which began across the state on January 8 this year, resulted in the Surat City Police alone arresting 77 people and uncovering close to 23.85 crore in fraudulent transactions. In what looks like one of the single largest single-district bust-ups in the operation, the Patan incident itself, with a staggering 398.43 crore routed through only 13 accounts, is remarkable.

The extraordinary nature of the operation is seen in the intelligence capabilities that drove it. It wasn't that police accidentally stumbled upon the Patan network; they worked back on it. After using data from the union government’s inter-agency platform, SAMANVAYA, a coordination platform for data on cybercrimes and the NCCRP, they traced suspicious clusters of transactions in the Harij Nagrik Sahakari Bank accounts to build a chain of evidence connecting the accountholders to the middlemen and, from the middlemen, to the whole ring of fraud. Twenty accused have been chargesheeted under the Bharatiya Nyaya Sanhita (BNS), and fourteen have been arrested, while six are still absconding.

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The Human Cost Nobody Talks About

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Behind every crore of scam money lies a real person who actually lost the real money. Of the 75%+ fraud losses incurred in 2025, 75% are from investment scams alone. Victims of stock trading scams lost ₹4,636 crore, spread across 2.28 lakh complaints filed in 2024. "Digital arrest" scams, in which fraudsters posing as law enforcement officials psychologically blackmail the victims to transfer money,  claimed ₹2,576 crore between 2022 and the first quarter of 2025.

For the victims it's never about the money: it's the retired teacher's lifetime savings from Chhattisgarh, the small trader's capital from Rajkot, the emergency money of the Bhopal family, or just savings from an ordinary person. And the mule accounts' networks are why most of it is never retrieved. Once the money is thrown into the layering chain, it's exponentially more difficult to trace it after every jump.

Then there's another category of victims that often gets overlooked, and they are the mule account holders themselves, many being semi-literate people from semi-urban or rural backgrounds approached with ₹10,000 in commission and with no awareness about the legalities of lending their bank details. With the BNS now they stand to get convicted for grave crimes, but the awareness of this trap is very low.

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Recommendations and Suggestions

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This isn't something India is facing passively. I4C, along with RBI, has developed Mule Account Hunter software. This software can be used by banks for the detection of suspect accounts through the use of behavioral analysis, device intel, and transaction pattern recognition. The Union Home Minister has directly asked all cooperative banks across the country to adopt this software at the earliest. Failure to do so, he warned, would make consumer safety from cyber fraud incomplete.

Apart from technology, three other areas need to go hand in hand: stringent KYC enforcement for cooperative and small finance banks; the prime locations of the mule recruitment network; greater awareness for the masses regarding the criminal liability one takes up when lending their accounts; and efficient inter-agency coordination so that the intelligence gathered on platforms like SAMANVAYA is converted into arrests before the accounts are dumped and the network reforms in another location.

Operation Mule Hunt 2.0 proves that this is feasible. 13 accounts in a small district of Gujarat. 398 crore. 228 victims. 14 arrested. The pipeline did exist, and it has been broken.

Yet, even as one network is broken, another is forming, somewhere right now. The accounts will appear legitimate. The holders of these accounts may not even realize what they have got into. That is the true danger of the mule accounts and work that cannot stop.

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Conclusion

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The Patan investigation has clearly shown that mule accounts have now moved from being a subsidiary tool of financial crime to becoming the infrastructure that underpins the economy of cyber-fraud in India. Every financial fraud, including investment fraud, digital arrest fraud, and phishing scams, is backed by a string of real bank accounts where the proceeds of crime are transferred and the trail is obscured. Though attempts such as the I4C Suspect Registry have made attempts to break down this network, it remains an overwhelming task. Robust KYC norms, real-time monitoring of transactions, and coordination between banks, police, and regulators are the key in preventing further industrialisation of cyber financial fraud in India.

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References

1. https://timesofindia.indiatimes.com/city/ahmedabad/operation-mule-hunt-2-0-gujarat-
2. police-bust-rs-398-43-crore-cyber-fraud-14-held/articleshow/131594240.cms?utm_source=contentofinterest&utm_medium=text&utm_campaign=cppst
3. https://the420.in/india-cybercrime-2024-42-percent-spike-sims-imei-mule-accounts/
4. https://www.thehansindia.com/news/national/ed-explains-how-mule-accounts-and-crypto-networks-enabled-12000-crore-cyber-fraud-1047606
5. https://www.zigram.tech/article/mule-accounts-tier-1-tier-2-cities-india/
6. https://risk.lexisnexis.com/global/en/insights-resources/article/stopping-money-mules-in-india
7. https://timesofindia.indiatimes.com/city/ahmedabad/operation-mule-hunt-2-0-gujarat-police-bust-rs-398-43-crore-cyber-fraud-14-held/articleshow/131594240.cms

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What are Wi-Fi attacks?

Wi-fi is an important area of cyber security and there is no need for physical cable for the network. Wi-Fi has access to a network signal radius everywhere. The devices and systems can have a network without physical access due to Wi-fi. But everything comes with cons and pros, and if we talk about cybersecurity, it has been established that Wi-fi networks are extremely vulnerable to security breaches and it is very easy to be hacked by hackers. Wi-Fi can be accessed by almost every device in the modern day: it can be smartphones, tablets, computers, and laptops. To know whether someone has been tampering with your personal Wi-Fi there are certain signs that can prove it. The first and most important sign is that your internet speed gets slower, as someone else is using your Wi-Fi surf.

Why would anyone hack someone’s Wi-Fi network?

Usually, hackers hack the network because they want access to the confidential data of someone and they can observe all the online activities and data that have been sent through a network. An unauthorize hacker will pretty much be able to see everything you do online. Wi-Fi allows hackers o view information on sites. Any financial information which is saved in the browser can be accessed by hackers and they can alter it and can alter the content you see online. And all the information saved in Wi-fi networks can be used by hackers for their own benefit, they can sell it, impersonate you, or even take money out of your bank through Wi-Fi.

Avoiding vulnerable Wi-Fi networks

The first and foremost rule of protection is that you should not use public networks if that network is easily open to you then that is also available to others and from others, and someone can who wishes to use your confidential and sensitive information, can access that.  If you really need to access the public network in an urgent situation, then you must make sure to limit your activities while connected. And avoid accessing your online banking or pages that require login information. Also, a good measure to take as well is to always delete your cookies after using public WIFI.

How To Secure Your Home Wi-Fi Network

Your home’s wireless internet connection is your Wi-Fi network. Typically, a wireless router is used, which broadcasts a signal into the atmosphere. You can connect to the internet using that signal. However, if your network is not password-protected, any nearby device can grab the signal off the air and connect to your internet. The benefit of Wi-Fi? Wireless access to the internet is possible. The negative? Your internet activity, including your personal information, may be visible to neighboring users who connect to your unprotected network. Furthermore, if someone uses your network to conduct a crime or send out unauthorized spam, you might be held accountable.

Wi-Fi or Li-Fi? –

The common consensus is that Li-Fi technology is more secure than Wi-Fi. Li-Fi systems can be made more secure by integrating a variety of security features. Although these qualities might appear when Li-Fi is widely used in the near future, it is already thought to be safer because of a number of security features. Since the connection’s characteristics make it simpler to lock connections, limit access, and track users even in the absence of encryption and other security features, Li-Fi is seen as being safer. Li-Fi systems will be able to support new security protocols, which will not only enable high-speed networking but also open the door for innovative security techniques to strengthen connections.

Conclusion

A hacker can sniff the network packets without having to be in the same building where the network is located. As wireless networks communicate through radio waves, a hacker can easily sniff the network from a nearby location. Most attackers use network sniffing to find the SSID and hack a wireless network.

Any wireless network can theoretically be attacked in a number of different ways. Use of the default SSID or password, WPS pin authentication, insufficient access control, and leaving the access point available in open locations are all examples of potential vulnerabilities that could allow for the theft of sensitive data. Kismet’s architecture in WIDS mode may guard against DOS, MiTM, and MAC spoofing attacks. routine software updates on the other hand, the use of firewalls may help defend the network against outside intrusion. The act of finding infrastructure issues that could allow harmful code to be injected into a service, system, or organization is known as ethical hacking. They use this technique to prevent invasions by lawfully breaking into networks and looking for weak spots.

Introduction

Artificial intelligence is often hailed as a democratiser of knowledge, opportunity and skill. It is set to improve diagnostics, personalised learning, and productivity to boost the economy, which can assist millions of people to leave poverty. However, this may be an incomplete picture. A report of the United Nations Development Programme in 2025 tells a more complex tale. The Next Great Divergence: Why AI May Widen Inequality Between Countries cautions that, unless acts are taken to intervene, AI will not alleviate inequality between countries but will instead concentrate benefits in already advantaged economies and increase risks in more vulnerable ones.

Two Gaps, One Crisis

AI is not going to create a level playing field: it has been injected into a world where there is unprecedented inequality. The report outlines two structural asymmetries that will influence the ways in which its effects manifest: a capability gap and a vulnerability gap. 

Those countries that have high connectivity, skills, compute and regulation will be in a position to reap a greater portion of the AI dividend. Others will be exposed to greater risks of job losses, information exclusion, misinformation, and the indirect consequences of increased energy and water demands. 

The centre of this transition is the Asia-Pacific region, that harbors a population of more than 55 per cent of the world. More than half of the global AI users are now located in the region, but the initial positions are quite different. Nations such as Singapore and South Korea are already spending a lot of money on AI infrastructure, with others still striving to offer basic broadband services. Two out of three individuals already use AI tools in certain high-income economies. In most countries with low incomes, the utilisation is lower. Such figures are important as they depict not only a gap in technology but also a structural difference in terms of who controls AI and who is controlled by the latter.

When Inequality Becomes a Trust Problem

Any trusted technological system is based on three tenets: transparency, fairness and accountability. AI inequality negatively impacts all three.

If governments implement imported AI systems in areas with limited technical capability, with limited transparency on their operation, their construction, and their biases. Citizens do not really trust when decision-making systems are black boxes and domestic institutions lack the know-how to question them.

Data exclusion also interferes with fairness. The AI systems trained with the datasets not sufficiently representative of the rural population, linguistic minorities, and women will generate poorer results in those groups systematically. Since South Asian women are much less likely to own a smartphone,  this impacts their representation in digital data, and consequently in any AI system trained on such data. 

Safety Risks Are Not Evenly Distributed

The lack of trust has a direct safety aspect. For example, those countries that have less robust information ecosystems have a greater exposure to AI-generated misinformation that can bias the discourse of the populace, alter elections, and cause violence. They also have the weakest capability of screening, tagging, or combating such content.

The same can be said about labour markets. The very same technologies that can speed up marginalisation and destabilise governance increase human insecurity, especially among employees in the informal economy with weak social security. The UNDP report points out that the exposure of female employment to disruption by AI is disproportionate to that of male employment, which further presents a gendered dimension in an already unequal situation.

Risks of infrastructure are skewed as well. Large AI systems may create disproportionately high energy and water demands on countries that host the data infrastructure without there being an equivalent economic payback. The environmental cost is local while profits are outsourced. Dangers of AI spread downwards, and the advantages go upwards.

The Governance Gap and Regulatory Arbitrage

Governance is perhaps the most important aspect. There are only a few states that presently have extensive AI regulation systems. This gives rise to a patchy landscape, in which safety standards differ dramatically and where companies have an incentive to install systems in jurisdictions that have weaker regulation.

The main reason is the lack of capability, as expressed by Philip Schellekens, chief economist of the UNDP in Asia and the Pacific, who says that those countries that invest in skills, computing power and well-run governance structures will gain. The rest will be left far behind. 

This departure has its ramifications outside the nations. When users in other areas are subjected to widely different rates of safety and equity by the same international platforms, the concept of uniform digital norms would no longer be sustainable. Confidence in AI systems is lost not only locally but also on a global scale.

Way Forward

The UNDP report makes it clear that there is no inevitability of divergence. To avert it, however, it is necessary to consider AI governance as a development, rather than a technology problem.

The capacity to govern should be constructed and not presumed. This implies assisting countries in establishing regulatory systems, institutional capacity, and facilitating cross-border collaboration on standards. It can also imply considering some AI features as a public good, with common models and open standards that do not allow a few firms or states to become too powerful.

The UNDP articulates the problem in a simple manner: in the end, the world's people and not machines must decide on what technologies should be given priority and how to utilise them optimally.

Conclusion

AI inequality is often framed as an economic divergence story. But its implications run deeper. It reshapes who is protected, who is visible in data, and who has the power to challenge harmful outcomes. The risk is not just that some countries fall behind economically. It is that the global digital ecosystem fragments into zones of high trust and low trust, high protection and low protection. The choices made now will determine which path prevails. AI can reinforce existing divides or help bridge them. 

But that outcome will not be decided by the technology itself. It will be decided by how societies choose to distribute access, power, and responsibility in the systems they build.

References

1. https://www.undp.org/sites/g/files/zskgke326/files/2025-12/undp-rbap-the-next-great-divergence_1.pdf
2. https://www.undp.org/asia-pacific/press-releases/ai-risks-sparking-new-era-divergence-development-gaps-between-countries-widen-undp-report-finds
3. https://www.undp.org/asia-pacific/blog/next-great-divergence-how-ai-could-split-world-again-if-we-dont-intervene
4. https://www.aljazeera.com/news/2025/12/2/ai-threatens-to-widen-inequality-among-states-un
5. https://www.undp.org/asia-pacific/next-great-divergence
6. https://www.eco-business.com/press-releases/ai-risks-spark-new-era-of-divergence-as-development-gaps-widen-undp-report/

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