India’s fraud landscape is undergoing a worrying shift: the problem is no longer simply about how many fraudulent transactions or applications are being detected, but about how sophisticated—and financially consequential—the underlying activity has become.
₹48,021 crore linked to suspected fraud applications in FY26 as organised networks, identity misuse and account takeovers reshape the threat landscape
According to Experian’s latest report, The New Frontier: Emerging Trends in Fraud Prevention, the amount involved in suspected fraud-linked applications in India rose nearly fourfold, from ₹12,230 crore in FY24 to ₹48,021 crore in FY26. Experian says the trend points to a move from isolated fraud incidents towards more organised, network-driven activity.
The distinction matters. The ₹48,021 crore figure cited by Experian refers to the amount involved in suspected applications, drawing on RBI data and Experian’s analysis; it should not be read as ₹48,021 crore of confirmed consumer losses from online payment scams. RBI’s broader FY26 banking-fraud data separately puts the total amount involved in reported bank frauds at the same ₹48,021 crore, with advances accounting for the overwhelming majority.
From individual scams to organised fraud networks
Experian’s findings indicate that fraudsters are increasingly operating through interconnected ecosystems involving stolen identities, synthetic identities, mule accounts and false borrower information.
Account takeover has emerged as the most widespread growing threat, with 77% of surveyed organisations reporting an increase. Money muling and identity theft followed at 71% each, pointing to the growing importance of compromised identities and intermediary accounts in enabling fraud networks.
The research, conducted with Forrester Consulting in 2025, surveyed 109 senior fraud decision-makers in India. It also found that 62% of organisations had seen an increase in overall fraud attacks, while 66% said fraud-related losses were rising year-on-year.
That suggests a more complex equation for financial institutions: fewer or more targeted incidents can still translate into substantially greater financial exposure.
The hardest fraud to stop may be the one the customer authorises
Among the fraud categories examined, Authorised Push Payment (APP) fraud emerged as the most difficult to detect and prevent, with 58% of organisations identifying it as a challenge.
APP fraud is particularly difficult because the transaction itself may appear legitimate. The customer authorises the payment, but does so after being manipulated through tactics such as impersonation, phishing or fraudulent investment propositions.
Identity theft was considered challenging by 54% of respondents, while money mule activity was cited by 53%. Deepfakes, synthetic business fraud and first-party fraud were also identified as significant challenges.
The implication is significant: traditional fraud controls designed around suspicious transactions may not be enough when the transaction is technically legitimate but the context surrounding it is fraudulent.
The technology gap is becoming a fraud gap
The report also highlights a gap between the speed at which fraud tactics evolve and the ability of organisations to respond.
48% of organisations said they struggle to rapidly update fraud models, rules and scores. Meanwhile, 47% cited a lack of device data and 44% pointed to inadequate real-time transaction monitoring as barriers to effective fraud prevention.
This is where fraud prevention increasingly becomes an enterprise technology problem rather than a narrow compliance function.
Financial institutions need to connect signals across identity, devices, behaviour, applications and transactions rather than examine each event in isolation. Experian’s research argues for greater use of application-level analytics, behavioural intelligence and alternative data to identify anomalies earlier.
AI is becoming part of the fraud arms race
The shift is also pushing organisations towards machine learning and more adaptive detection systems.
Among organisations already using ML-based fraud solutions, 58% reported improved ability to identify emerging fraud types, while 54% reported better detection accuracy. Another 56% said passive fraud checks helped reduce friction for genuine customers.
The balance is increasingly difficult: financial institutions need to identify sophisticated fraud without turning every unusual customer behaviour into a false alarm.
That makes the quality, breadth and real-time availability of data increasingly important.
The bigger lesson for financial institutions
India’s rapid expansion of digital financial services has made payments and lending faster, more accessible and more connected. It has also given fraud networks a larger attack surface.
The emerging challenge is therefore not simply to build stronger fraud rules. It is to understand relationships between seemingly unrelated signals—and identify suspicious behaviour before it becomes a financial event.
As Manish Jain, Country Managing Director, Experian India, noted, fraud prevention can no longer be treated as a standalone control function; it increasingly needs to become part of decision-making across the customer lifecycle.
For banks, lenders and fintechs, that means moving from transaction-level detection to network-level intelligence—and from static controls to systems capable of adapting as quickly as the fraud itself.
The fraudster is no longer necessarily looking for one victim.
Increasingly, the target is the system around the victim.
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