The real decision today is whether an individual can be trusted enough to onboard, transact with, or be allowed into a session in real time. In an exclusive conversation Sandesh GS, CTO, Bureau explains to Rajneesh De, Group Editor, CXO Media & APAC Media, that what makes Bureau genuinely different is that this decisioning spans the entire customer journey.
Digital fraud is becoming more sophisticated across fintech, banking, and marketplaces. What are the biggest shifts you are currently seeing in the global and Indian fraud landscapes?
The single biggest shift is that fraud is no longer opportunistic but has become an organized industry. Bureau’s India Fraud Report 2026 captures this well, toolkits available on the dark web now offer plug-and-play access to stolen data, malicious APIs, and ready-to-use scam scripts. This has lowered the barrier to entry so dramatically that even low-skill actors can execute attacks that once required real technical sophistication.
Fraud-as-a-Service has industrialised what was once a fragmented problem, and the consequence is that the same methods of operation now travel across institutions and industries very quickly. A technique that surfaces at one bank is deployed at a competing bank within days, because the actors behind it have no reason to change what is already working.
With digital businesses scaling rapidly, many organisations still rely on fragmented risk tools. Why is a unified risk decisioning platform becoming critical for modern digital businesses?
Risk teams are spending the bulk of their time investigating legitimate users while sophisticated threats slip through. That is the direct consequence of fragmented tools, each optimising for its own signal, with no one assembling the full picture.
Fraud does not confine itself to the slice of the journey that any one team or tool is watching. A fraudster who passes onboarding cleanly can still show meaningful risk signals during a session, in how their device behaves, how they navigate the app, the transactions that follow. Think of a device team sitting in one room, or the transaction monitoring team sitting in another. If those tools are not connected, that context is lost and every touchpoint starts from scratch.
A unified platform ensures context travels with every decision, drawing on device intelligence, identity signals, behavioural history, and the network of identities it is connected to, simultaneously. The outcome is fewer false positives for genuine users and fewer gaps for fraudsters to exploit.
Bureau positions itself as a decision-first platform for digital trust. What differentiates Bureau from traditional fraud detection or identity verification solutions?
Identity verification answers a narrow question whether a document is genuine or a phone number is real. These are important sub-tasks, but they don’t address what a business actually needs to know. The real decision is whether an individual can be trusted enough to onboard, transact with, or be allowed into a session in real time.
What makes Bureau genuinely different is that this decisioning spans the entire customer journey. One specific example would be how before onboarding, our Mule Score flags whether an identity or device is linked to known mule networks, stopping compromised accounts before they enter the system. At onboarding, checks run across global databases, criminal history, and adverse media to validate not just identity, but intent.
From there, risk-based authentication applies at every touchpoint, a new beneficiary added, a dormant account suddenly active after 90 days, a transaction pattern that breaks from history. Trust is re-evaluated continuously, not assumed. Post-transaction, Bureau’s decisioning extends further still, catching downstream fraud like chargebacks and return-to-origin abuse before they become losses.
And none of this is assessed in isolation. Every decision accounts for a user’s known network, prior transactions with identified fraudsters, devices linked to fraud rings, connections to flagged mule accounts. A user can look entirely clean on their own and still carry serious network risk. That is what verification will never see, and what decisioning is built for.
Bureau’s Graph Identity Network plays a central role in risk detection. Could you explain how network intelligence and link analysis help detect fraud patterns that traditional systems may miss?
Bureau’s proprietary Graph Identity Network (GIN) is a dynamic knowledge graph that links users across devices, emails, mobile numbers, and IPs to surface hidden financial crime networks at scale. The core insight behind it is simple, fraudsters are good at making individual data points look clean, but what they cannot control is how those data points connect across a network they cannot see. GIN is built to see exactly that.
The way it works in practice is through multiple interlocking capabilities. For instance, a priority is to distinguish legitimate users from those connect to known fraudsters using graphs built on shared traits, industries, and linkages, so risk teams can take targeted action rather than casting a wide net. We also identify connected clusters of devices and identities to expose coordinated financial crime networks that would be invisible to any single institution looking only within its own data. And we ensure we make each decision explainable by tracking identity signals across devices, sessions, and industries, so external teams also understand not just what the outcome is but why.
A system confined to one customer’s data boundary sees a clean, unknown identifier with no basis for concern. We recently identified an organised operation involving more than 2,700 linked users across platforms, a pattern that only became visible when identity, device, and behavioural signals were analysed together. Any individual institution examining just their own data would have seen isolated accounts with no obvious connection. We saw the network.
The outcomes this produces are measurable, a 95% reduction in collusion-based fraud, a 93% drop in promo abuse, and a 70% reduction in account takeover.
India’s digital payments ecosystem has expanded rapidly through UPI. At this scale, institutions are not just dealing with individual fraud attempts but increasingly organised activity such as mule networks and coordinated abuse. How should financial institutions rethink trust and risk management in such a large, interconnected ecosystem?
Mule networks are specifically designed to pass a one-time verification check cleanly. The account is opened with real documents, sits dormant, then gets activated as part of a network distributing funds across large clusters of connected accounts before any flag is raised. What institutions need is continuous evaluation, where every session, login, and transaction is an opportunity to re-examine whether a customer’s risk profile has changed, whether their device shows signs of compromise, whether their behaviour is consistent with what has been seen before.
A user who has transacted in the same pattern for two years suddenly behaving entirely differently is a signal. Trust needs to be continuously re-earned. And because mule networks operate across multiple institutions simultaneously, moving money in a chain where each hop involves a different account at a different bank, the intelligence that reveals the full picture has to come from outside any single institution’s walls.
Bureau verifies billions of identities globally every year. What patterns or risk signals become visible at that scale that individual organisations might miss, and how do those insights help improve fraud detection and decision-making across the ecosystem?
Scale gives you a network effect that no individual organisation can manufacture from its own data. The same bad actor moves between competing banks or fintechs or any other industry with the same fraud MOs. The only way to catch them is when you have a powerful AI that can identify threat vectors on unrelated platforms but show up with the same signatures.
Account opening patterns that correlate with known mule networks, device sharing for reseller abuse, account takeovers for promo abuse, none of that is visible from within a single institution’s boundaries. When we see the same identifier appearing in a fraudulent context at a different platform, we can flag the risk immediately, even if the current interaction looks entirely clean. And our presence across industries, platforms, as well as economies give us this breadth and depth of insight.
In fact, the economies of the devices that are identified should also be highlighted. Global competitors claiming to have modelled billions of devices will usually have a large share of iPhones. These are mature markets where fraud vectors are constrained. Our 400 to 500 million verified devices are predominantly from India and developing economies, where low-cost Android devices dominate and exploitation techniques are far more varied. The signals that matter here are genuinely different. Intelligence built on a Western device population will miss them, and that is not a small gap.
Bureau’s platform brings together identity, device, behavioural and transaction signals. How does this holistic approach improve fraud detection compared to siloed risk tools?
Each signal becomes more informative in the presence of the others. A device with a clean integrity score looks very different if the behavioural signals in that session are inconsistent with the identity claiming to use it. Unusual navigation patterns, transaction amounts that do not fit account history, timing anomalies suggesting automation, individually none of these trigger a flag in siloed tools, but together they form a picture that is hard to argue with.
What are the key pillars of Bureau’s GTM strategy for India?
India is our deepest market, and the strategy reflects both the maturity of those relationships and the specific dynamics of the ecosystem.
The RBI’s direction on risk-based authentication has created genuine urgency and organisations treating compliance as a strategic asset, is a conversation we are having constantly with prospective customers. Bureau is positioned for it because our platform is built for the depth those requirements demand, not surface-level coverage.
Within existing accounts, Bureau’s value compounds when it covers more of the lifecycle beyond the initial integration point. Institutions that have seen a 70% reduction in manual reviews or are identifying 20% more fraudulent applications without adding friction are the best evidence of what a cohesive unified fraud prevention strategy’s outcome looks like.
