A 74-year-old Mumbai doctor lost ₹7.17 crore after falling victim to a “digital arrest” cyber scam in which fraudsters impersonated telecom officials, police officers, and judicial authorities. Through staged video-call hearings and repeated threats, the scammers pressured him into transferring large sums of money over several weeks. The case highlights how social-engineering tactics, psychological manipulation, and gaps in digital literacy, especially among older adults are increasingly being used to carry out high-value cybercrimes in India.
A 74-year-old doctor in Mumbai lost ₹7.17 crore after being ensnared in a deeply manipulative cyber-fraud known as a “digital arrest” scam, according to Mumbai Police. In this scheme, fraudsters impersonated regulatory officials, police, and even judges to stage a fake virtual courtroom. The incident underscores how advanced social engineering and technology merge to exploit vulnerable individuals.
On 7 September 2025, the doctor received a call from someone claiming to represent the Telecom Regulatory Authority of India (TRAI), who alleged that his phone number was being used for money laundering. Soon afterward, another person posing as a police officer sent a fabricated digital arrest warrant. The scammers then insisted that, due to his age, he would be placed under “digital arrest” and subjected to trial over a video call, rather than detained in person.
To lend credibility, they arranged a video conference in which actors played the roles of a judge, court clerk, and police personnel. Under intense psychological pressure, the doctor was convinced to disclose details about his bank investments and property holdings. Over the next few weeks, he transferred money in separate tranches into multiple bank accounts controlled by the fraudsters. When the demands continued, he realized something was wrong and filed a complaint with the Mumbai Cyber Crime Branch, submitting his call logs and beneficiary account information.
This scam was effective because it took advantage of common human tendencies. People usually trust someone who appears to be an official, and the fraudsters used this to their benefit.[1] By using threats, formal language, and a staged video-call court session, they created a situation that caused fear and urgency. In such conditions, people often act quickly without thinking clearly.
The scam also isolated the victim. The idea of a “digital arrest” kept him from asking others for help or guidance. Many older adults are not familiar with how real legal or digital processes work, which makes it difficult for them to identify signs of a fake interaction.
The financial loss in such scams is enormous, but the harm goes beyond money. For older adults, the emotional trauma of being deceived by authoritative voices, even fake ones, can lead to chronic stress, anxiety, or sleep disturbances. The fear of legal repercussion, induced by the scam, may also erode trust in real institutions and discourage seeking help.
From a preventive health perspective, education is essential. Geriatric care programs, telemedicine consultations, and community outreach should incorporate awareness about how such cyber-frauds operate, especially how scammers impersonate officials via video conferencing. Encouraging older individuals to verify legal or police claims independently, for example, by contacting their known lawyers or family members may reduce susceptibility.[1]
Advanced research shows that artificial intelligence (AI) can meaningfully contribute to detecting and preventing scam calls. In a study, researchers used techniques such as convolutional neural networks, anomaly detection, and natural language processing to identify scam call patterns. Their system recognized speech and behavior features typical of fraudulent calls and adapted over time to evolving scam strategies. This kind of technology could help telecom providers or security services flag suspicious calls early.[2]
Other studies also validate AI’s effectiveness. Machine-learning models have been trained to detect fraudulent calls by analyzing voice features, call metadata, and linguistic cues.[3] These systems can distinguish malicious calls with high accuracy and learn new scam tactics as they emerge.
1. Ian Wood, Michal Kepkowski, Leron Zinatullin, Travis Darnley, and Mohamed Ali Kaafar, “An Analysis of Scam Baiting Calls: Identifying and Extracting Scam Stages and Scripts,” arXiv, July 5, 2023.
2. Arya Brijith, “Unmasking Scam Calls: Analysing and Detecting Scammers using AI” (unpublished manuscript, International Center for AI and Cyber Security Research and Innovations, Asia University).
3. M. J. Ratnakumari, S. N. A. Thahenath, T. S. Lakshmi, P. N. D. Kumar, and K. Veeraiah, “Detection of Fraudulent or Deceptive Phone Calls Using Artificial Intelligence,” Turkish Journal of Computer and Mathematics Education (TURCOMAT) 15, no. 1 (2024): 96–99.
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