Low-cost AI models are fuelling India's growing deepfake fraud threat

Customised applications built on open-source AI models are enabling sophisticated deepfake frauds at lower costs, raising concerns across banking, fintech and digital platforms

India's escalating deepfake threat is being fuelled less by frontier artificial intelligence (AI) models and more by customised applications built on open-source models, enabling fraudsters to create increasingly sophisticated scams at a much lower cost.

 

Deepfakes are becoming so convincing that they can scarcely be distinguished from genuine images and videos, raising fears that fraudsters could use synthetic identities and manipulate live Know Your Customer (KYC) checks to slip through defences designed to prevent impersonation.

 

An industry source, who requested anonymity, said the threat has begun to permeate the banking, financial services and insurance (BFSI) sector.

 

“People are generating synthetic bank statements and there are issues of deepfakes during video KYC. There have been cases where such applications have been processed and underwritten, leading to a fraud of Rs 15 crore-Rs 20 crore at an NBFC (non-banking financial company),” the person said.

 

Moreover, the cost of organised fraud is becoming increasingly affordable, making it a major security encumbrance for the BFSI industry, thanks to the rapid pace of development of AI models.

 

Executives said the frauds typically follow a familiar pattern: customised tools built on top of these models are distributed through Telegram channels and dark-web marketplaces.

 

“The challenge is that fraudsters are not using the standard models. Frontier models use trillions of tokens to train and are sophisticated. Instead, fraudsters use smaller models to generate a deepfake and run them on a local consumer hardware like a gaming computer. It does not cost much. These models are shared across Telegram channels and other dark web platforms,” said Sandesh GS, chief technology officer (CTO) at Bureau, a technology service provider (TSP).

 

Deepfake injection involves injecting images into a compromised device that can defeat likeness checks, as well as AI-based document tampering.

 

These deepfakes pass have the ability to pass most likeness-matching checks, especially at a time when KYC is designed around the principle that a live human face on camera is the proof of that person's presence as well as proof of life, in many cases.

 

The rapid proliferation of such tools has even prompted the Indian Cyber Crime Coordination Centre (I4C) to issue an advisory warning that fraudsters are using AI-powered techniques to circumvent existing cybersecurity safeguards.

 

Industry executives explained that some of the fraud models were specifically trained for deepfakes and could be built on open source models.

 

“These are wrappers built on top of an open source LLM (large language model), which is easily available at a fairly cheap cost. Also, the expertise required to do all these sophisticated attacks has come down significantly,” said Prakarsh Paritosh, principal product manager at Idfy, also a TSP.

 

He explained that the company was working on rolling out deepfake detection modules that could have the ability to detect synthetic identities after analysing an image.

 

Banks in the country reported fewer fraud cases in the financial year 2025-26 (FY26), but the total value of money involved rose to its highest in three years, driven by cheating in loans and advances, and concentrated in state-owned lenders.

 

The industry reported frauds worth Rs 48,021 crore in FY26, up 46.4 per cent from Rs 32,803 crore in FY25, according to data released by the Reserve Bank of India (RBI) in its latest annual report.

 

“Deepfakes are extremely hard to detect to a normal person. The reason why this issue is also a growing concern is because devices themselves are compromised. Fraudsters can inject images or deepfakes in that case. First priority should be securing the devices,” Sandesh said.

 

These frauds do not stop just at the BFSI sector.

 

They are also a growing threat to e-commerce and quick commerce platforms, social media sites, and dating apps, among other things.

 

“There are sophisticated mule networks which carry out all such frauds. Even the frontier models around identity accounts such as creating a fake cohort to exploit vulnerabilities, is a possibility. Especially for documentation, where some documents are generated by ChatGPT or Claude,” Paritosh said.

 

Industry executives added that since most of these deepfakes are generated using models trained for malicious intent, they bypass GAN (Generative Adversarial Network) fingerprints or synthetic IDs, which are unique identifiers which leave invisible patterns behind in images or data and are used to detect deepfakes.

 

The I4C has also recommended that ‘customer onboarding systems, including fintech companies should integrate deep fake and synthetically generated content detection mechanisms’.

 

It has called on users to report suspicious activity and identity theft to national cybercrime reporting portal (cybercrime.gov.in).