Home Technologies Artificial Intelligence (AI) 48% Enterprises Flag Mule Networks as Top Fraud Threat: Bureau India Fraud Report 2026
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48% Enterprises Flag Mule Networks as Top Fraud Threat: Bureau India Fraud Report 2026

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Bureau, an AI-powered unified risk decisioning platform, has released the India Fraud Report 2026, revealing a sharp shift in how fraud is being executed across India’s digital economy. With fraud losses surging to ₹36,014 Crore in the banking sector, as per the RBI Annual Report 2024 – 25, the Bureau Fraud Report finds that fraud operations are becoming faster, more organised, and increasingly industrialised, leveraging real-time payments, instant onboarding, and interconnected digital platforms to scale attacks.

A key finding from the report highlights the growing dominance of mule networks, with 48% of Indian enterprises identifying them as the most difficult fraud threat to detect and control, ahead of phishing, synthetic identities, account takeover, and social engineering (Which came second with 33%). Designed to appear legitimate at every touchpoint, these networks distribute funds across large clusters of connected accounts, making them difficult to detect without cross-platform visibility.

The report also reveals a growing operational challenge for risk teams. 58% of organizations identified false positives as their primary risk, indicating that risk teams are spending a significant portion of their time investigating legitimate users while sophisticated threats slip through undetected. In 2025, identity became the main entry point for fraud in India’s digital economy, with fragmented and reusable identity data being misused at scale. As a result, organisations saw decision error as the biggest risk, as it is becoming harder to tell genuine users apart from fraudsters. This is made worse by advanced AI tools that can create highly realistic fake images, documents, and even identities, often slipping past traditional verification systems. At the same time, fraud has become more accessible and scalable. With the rise of Fraud-as-a-Service, toolkits available on the dark web now offer plug-and-play access to stolen personal data, malicious APIs, and ready-to-use scam scripts, lowering the barrier to entry and enabling even low-skill actors to execute complex fraud operations.

Compliance practices are also contributing to the exposure gap. Only 20% of organizations treat compliance as a strategic tool that strengthens detection and informs proactive risk investment. In contrast, 50% continue to view it as an obligation or protective shield against retaliatory measures or reputational damage. In an age where fraud tactics are constantly evolving and exploiting even minor vulnerabilities, organisations without built-in, adaptive anti-fraud systems leave their customers, especially first-time digital users, highly exposed.

Commenting on the findings, Sandesh GS, CTO, Bureau, said, “What gives us a real edge today is the network effect at scale. When you analyse identities and devices across ecosystems, you begin to see how the same fraud patterns, tools and behaviours repeat across platforms and even industries. Fraudsters reuse what works, which makes it important to look beyond isolated data points. By building contextual intelligence, we can identify signals that should not logically repeat, such as the same identifiers appearing across different systems, and flag risks in real time. This is critical because fraud today operates as an interconnected network, not in silos.”

Bureau’s platform intelligence illustrates the scale of this challenge. The company identified and disrupted multiple organized fraud operations, including one network involving more than 2,700 linked users operating across platforms, a pattern that becomes visible only when identity, device, and behavioural signals are analyzed together rather than in isolation.

Fraud now moves across networks and enterprises, making isolated controls ineffective. To keep pace, risk teams need detection systems that combine device, behavioural, and contextual intelligence across platforms. Graph analysis helps map relationships between identities, devices, and transactions, allowing organizations to uncover coordinated fraud activity that would otherwise appear legitimate in isolation. 

The India Fraud Report 2026 serves as a call to action for organizations to rethink how they approach fraud prevention. The report highlights that incremental improvements to point controls will not close the gap. Instead, enterprises must adopt coordinated, lifecycle-based risk orchestration, enable intelligence sharing across institutions, and use compliance frameworks as enablers rather than constraints. As fraud continues to evolve into an organized industry, defences must advance with similar coordination and scale.

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