#FactCheck - Viral Video of Burning Aircraft Falsely Linked to UAE, Found to Be AI-Generated

Introduction

The Telecom Regulatory Authority of India (TRAI) has directed all telcos to set up detection systems based on Artificial Intelligence and Machine Learning (AI/ML) technologies in order to identify and control spam calls and text messages from unregistered telemarketers (UTMs).

The TRAI Directed telcos

The telecom regulator, TRAI, has directed all Access Providers to detect Unsolicited commercial communication (UCC)by systems, which is based on Artificial Intelligence and Machine Learning to detect, identify, and act against senders of Commercial Communication who are not registered in accordance with the provisions of the Telecom Commercial Communication Customer Preference Regulations, 2018 (TCCCPR-2018). Unregistered Telemarketers (UTMs) are entities that do not register with Access Providers and use 10-digit mobile numbers to send commercial communications via SMS or calls.

TRAI steps to curb Unsolicited commercial communication

TRAI has taken several initiatives to reduce Unsolicited Commercial Communication (UCC), which is a major source of annoyance for the public. It has resulted in fewer complaints filed against Registered Telemarketers (RTMs). Despite the TSPs’ efforts, UCC from Unregistered Telemarketers (UTMs) continues. Sometimes, these UTMs use messages with bogus URLs and phone numbers to trick clients into revealing crucial information, leading to financial loss.

To detect, identify, and prosecute all Unregistered Telemarketers (UTMs), the TRAI has mandated that Access Service Providers implement the UCC.

Detect the System with the necessary functionalities within the TRAI’s Telecom Commercial Communication Customer Preference Regulations, 2018 framework.

Access service providers have implemented such detection systems based on their applicability and practicality. However, because UTMs are constantly creating new strategies for sending unwanted communications, the present UCC detection systems provided by Access Service providers cannot detect such UCC.

TRAI also Directs Telecom Providers to Set Up Digital Platform for Customer Consent to Curb Promotional Calls and Messages.

Unregistered Telemarketers (UTMs) sometimes use messages with fake URLs and phone numbers to trick customers into revealing essential information, resulting in financial loss.

TRAI has urged businesses like banks, insurance companies, financial institutions, and others to re-verify their SMS content templates with telcos within two weeks. It also directed telecom companies to stop misusing commercial messaging templates within the next 45 days.

The telecom regulator has also instructed operators to limit the number of variables in a content template to three. However, if any business intends to utilise more than three variables in a content template for communicating with their users, this should be permitted only after examining the example message, as well as adequate justifications and justification.

In order to ensure consistency in UCC Detect System implementations, TRAI has directed all Access Providers to deploy UCC and detect systems based on artificial intelligence and Machine Learning that are capable of constantly evolving to deal with new signatures, patterns, and techniques used by UTMs.

Access Providers have also been directed to use the DLT platform to share intelligence with others. Access Providers have also been asked to ensure that such UCC Detect System detects senders that send unsolicited commercial communications in bulk and do not comply with the requirements. All Access Providers are directed to follow the instructions and provide an update on actions done within thirty days.

The move by TRAI is to curb the menacing calls as due to this, the number of scam cases is increasing, and now a new trend of scams started as recently, a Twitter user reported receiving an automated call from +91 96681 9555 with the message “This call is from Delhi Police.” It then asked her to stay in the queue since some of her documents needed to be picked up. Then he said he works as a sub-inspector at the Kirti Nagar police station in New Delhi. He then inquired whether she had recently misplaced her Aadhaar card, PAN card, or ATM card, to which she replied ‘no’. The scammer then poses as a cop and requests that she authenticate the last four digits of her card because they have found a card with her name on it. And a lot of other people tweeted about it.

Conclusion

TRAI directed the telcos to check the calls and messages from Unregistered numbers. This step of TRAI will curb the pesky calls and messages and catch the Frauds who are not registered with the regulation. Sometimes the unregistered sender sends fraudulent links, and through these fraudulent calls and messages, the sender tries to take the personal information of the customers, which results in financial losses.

Introduction

Deepfake have become a source of worry in an age of advanced technology, particularly when they include the manipulation of public personalities for deceitful reasons. A deepfake video of cricket star Sachin Tendulkar advertising a gaming app recently went popular on social media, causing the sports figure to deliver a warning against the widespread misuse of technology.

Scenario of Deepfake

Sachin Tendulkar appeared in the deepfake video supporting a game app called Skyward Aviator Quest. The app's startling quality has caused some viewers to assume that the cricket legend is truly supporting it. Tendulkar, on the other hand, has resorted to social media to emphasise that these videos are phony, highlighting the troubling trend of technology being abused for deceitful ends.

Tendulkar's Reaction

Sachin Tendulkar expressed his worry about the exploitation of technology and advised people to report such videos, advertising, and applications that spread disinformation. This event emphasises the importance of raising knowledge and vigilance about the legitimacy of material circulated on social media platforms.

The Warning Signs

The deepfake video raises questions not just for its lifelike representation of Tendulkar, but also for the material it advocates. Endorsing gaming software that purports to help individuals make money is a significant red flag, especially when such endorsements come from well-known figures. This underscores the possibility of deepfakes being utilised for financial benefit, as well as the significance of examining information that appears to be too good to be true.

How to Protect Yourself Against Deepfakes

As deepfake technology advances, it is critical to be aware of potential signals of manipulation. Here are some pointers to help you spot deepfake videos:

• Look for artificial facial movements and expressions, as well as lip sync difficulties.
• Body motions and Posture: Take note of any uncomfortable body motions or discrepancies in the individual's posture.
• Lip Sync and Audio Quality: Look for mismatches between the audio and lip motions.
• background and Content: Consider the video's background, especially if it has a popular figure supporting something in an unexpected way.
• Verify the legitimacy of the video by verifying the official channels or accounts of the prominent person.

Conclusion 

The popularity of deepfake videos endangers the legitimacy of social media material. Sachin Tendulkar's response to the deepfake in which he appears serves as a warning to consumers to remain careful and report questionable material. As technology advances, it is critical that individuals and authorities collaborate to counteract the exploitation of AI-generated material and safeguard the integrity of online information.

Reference 

• https://www.news18.com/tech/sachin-tendulkar-disturbed-by-his-new-deepfake-video-wants-swift-action-8740846.html 
• https://www.livemint.com/news/india/sachin-tendulkar-becomes-latest-victim-of-deepfake-video-disturbing-to-see-11705308366864.html 

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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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