Cyber psychologist, police say elderly are mostly non-tech savvy and trust humans more than technology, making them an easy target
Fraudsters gain the trust of senior citizens and get them to share their credit card details. Representation pic
Mumbai police received more than 7,500 cyber fraud cases from January to June, but they could solve only 401 of them, wherein mostly non-tech savvy senior citizens were the targets, according to the official data.
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Cyber fraud cases started rising in the city since COVID-19 hit the country, as most people started working from home and spending more time on the Internet, and the crooks took advantage of increased online presence.
The Mumbai police’s data also showed that more than 2,500 cases were registered in June alone. Although the Mumbai police have not mentioned the different cyber fraud cases related to senior citizens, the FIRs available on its website suggest that the elderly population is the most vulnerable.
Also read: After electricity bill scam, cyber crooks adopt new method to dupe people
“Senior citizens are more vulnerable because they come from a generation that trusts humans more than technology. Another reason is they panic on receiving messages about electricity disconnection, KYC of SIM card and banking details, and they immediately call on the numbers provided in the text. The moment they see someone extending a helping hand, they become more vulnerable, particularly when their children are not around,” said cyber psychologist Nirali Bhatia.
Recent victims
In one of the recent cases, 81-year-old Subhash Zaveri approached the Malabar Hill police station on July 20 seeking help, as he had lost Rs 50,000 to cyber criminals.
Assistant Inspector (cyber) Nilesh Bankar, who was dealing with another case of cyber fraud, took his details and found out that the money was transferred through payment gateways to different platforms, including Flipkart where Rs 36,000 was sent.
He immediately wrote to Flipkart's nodal officer and contacted him on the phone to get the amount blocked before fraudsters could purchase something using the fraud amount. Another transaction of Rs 6,000 was made on MobiKwik, which was also blocked. The remaining amount was used in small amounts, hence Bankar managed to secure Rs 42,000.
"I was scrolling through Facebook when I saw an ad about ‘buy one get one free’ thali offer from Bhagat Tarachand Restaurant. I clicked on the link, contacted the number provided on it and shared my debit card details and then the OTP I got. I was not aware what OTP was and ended up losing Rs 50,000,” Zaveri told mid-day.
In another case, Vijay Shah, 70, lost over Rs 97,000 after he contacted a number he had found on Google while looking for AC repair services of Usha brand. He filed a complaint at Gamdevi police station on July 20.
“The person on the call shared a link with me and asked me to make a payment of R10, which I did through my credit card. Thereafter, two transactions of Rs 48,696 each took place,” Shah said in his statement to the police.
Niranjan Nanavati, 70, a Cuffe Parade resident, was duped of Rs 1,22,918 after he received an email about his Netflix membership expiring. "I clicked on the restart membership tab in the email and filled in my credit card details, after which multiple transactions took place on my card. Later, I realised I had been duped,” he states in his statement to Gamdevi police.
“In most of the cases, senior citizens are more vulnerable because they are not tech savvy and fraudsters take advantage of that. We have been trying our best to stop such frauds, but cyber awareness is also necessary,” said Neelotpal, deputy commissioner of police (Zone 2).
Types of frauds
The types of cases are related to phishing, spoofing of email, pornography, obscene email, social media fraud, hacking, cheating, which includes customer gifts, purchase fraud, job fraud, loan fraud, insurance fraud, investment fraud, matrimonial fraud, cryptocurrency fraud , credit card fraud, extortion and data theft.
From 302 cases to over 2,500 in six months
Month Total no. of cases No. of cases solved
January 302 19
February 534 35
March 952 61
April 1,468 76
May 1,957 90
June 2,506 120
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