Delhi Police Busts Gang That Used Fake Govt IDs to Scam Banks of Crores in Loans
Gang used fake govt IDs, salary slips to scam banks for loans

In a significant crackdown, the Delhi Police Crime Branch has dismantled a sophisticated syndicate that allegedly defrauded multiple banks of substantial sums by securing personal loans using fabricated government identities and salary documents. The elaborate scam, operational since 2019, was finally busted with the arrest of three key accused who had previously worked in the banking sector.

The Modus Operandi: A Web of Fake Salaries and Forged Documents

The gang's method was meticulously planned to exploit the trust banks place in government employees. First, they would recruit individuals and open bank accounts in their names using completely fake government employee IDs and documents. These accounts were then meticulously 'seasoned' to appear legitimate.

Every month, the accused would transfer money into these accounts from fraudulent company accounts, marking the transactions as "salary" credits. This created a consistent financial history that mimicked a genuine, stable government income. Banks, upon reviewing these statements, would then proactively offer pre-approved personal loans at preferential rates reserved for low-risk, salaried individuals.

"As soon as the salary accounts received a loan offer from a bank, they would apply for it," explained DCP (Crime Branch) Harsh Indora. To avoid raising immediate red flags, the gang would even pay the Equated Monthly Installments (EMIs) for several months before abruptly stopping all payments and disappearing with the bulk of the loan amount.

How the Scam Unravelled in 2022

The fraud came to light in September 2022 due to the vigilance of Loknarayan Karotiya, an assistant manager at Bajaj Finance's Pitampura office. While reviewing loan accounts, he noticed EMIs were bouncing from accounts supposedly belonging to three employees of the Comptroller and Auditor General (CAG) office at Bahadur Shah Zafar Marg.

Suspicious, Karotiya directed his team to visit the CAG building to verify the borrowers' employment. The investigation revealed that the three individuals—Manish, Manpreet, and Harveen—had never worked at the office. They had secured loans totalling over Rs 28 lakh (Rs 9.8 lakh, Rs 10 lakh, and Rs 8.5 lakh respectively) from Bajaj Finance in 2019 using entirely fabricated documents, including fake IDs for positions like Senior Audit Officer.

An internal probe further uncovered that the salary accounts were emptied soon after each "salary" credit, a major red flag. This prompted the filing of a formal complaint, leading to a deeper police investigation.

The Arrests and Banking Background of the Accused

After a prolonged investigation spanning over two dozen raids, the Crime Branch's Western Range-II unit arrested the three masterminds on December 29, 2025. The accused were identified as:

  • Atul Agarwal (40), alias Manish Kumar, from Patna, Bihar: Studied till Class 10, he used his prior banking experience to pose as 'Manish Kumar' and personally obtained a loan using a fake government identity.
  • Ajay Chaurasia (46), from Nihal Vihar, Delhi: Educated up to Class 8, his alleged role involved opening the fake bank accounts and managing the fraudulent salary credits.
  • Deepak Dhoundiyal (49), from Dehradun, Uttarakhand: A graduate, he used his banking knowledge to orchestrate the fraud and navigate verification checks.

Police confirmed that all three had previously worked in banks, which gave them insider knowledge of loan approval processes and verification loopholes. The person whose identity was used would receive a small cut, while the gang members divided the majority of the illicit funds.

The Delhi Police is continuing its investigation to identify more victims, trace other gang members, and ascertain the full financial scale of this long-running fraud. This case highlights critical vulnerabilities in the digital loan approval ecosystem and the need for enhanced due diligence, even for seemingly secure customer profiles.