Beyond Transactions: Detecting Mule Accounts with Business-Centric Observability

Mule Accounts: The Silent Threat in Digital Transactions

Digital payments are being embraced across businesses and consumer segments at an unprecedented scale. But alongside this surge, a quieter menace has taken root: mule accounts — bank accounts used to receive and move illicit funds. Recent estimates suggest that over ₹11,000 crore has been lost to fraud involving such accounts, with authorities flagging over 19 lakh mules and preventing suspicious transactions exceeding ₹2,000 crore.

What makes these accounts particularly dangerous is that they often look and act legitimate — until behavioral patterns reveal otherwise. From individuals unknowingly drawn into fraud rings through work-from-home scams, to accounts rented out for a fee, the variety and volume of mule activity make detection a significant challenge.

This blog explores how Business-Centric Observability, supported by a contextual data lake, enables the detection of such behaviors. Not as a fraud detection system in itself — but as a foundational layer to build adjacent use cases that go beyond traditional IT metrics and truly serve business resilience and customer protection.

Who Are the Mules? And Why They’re Hard to Spot

A mule account is a bank account used as a conduit for fraudulent money transfers — often unknowingly by individuals who’ve either been tricked, paid to “rent out” their account, or whose identities have been forged.

These accounts are dangerous because:

• They pass as legitimate: Verified KYC, valid transactions, normal metadata.
  
• They behave erratically, not obviously: Sudden bursts of activity, followed by silence.
  
• They’re often disposable: Used briefly, then abandoned or rotated out.

Here are some examples from the ground :

Fig 1: Examples of Frauds in Digital Transactions

Traditional systems struggle to flag these as they lack context, timeliness, and behavioral insight.

Why Traditional Monitoring Falls Short

Most fraud detection today is built around:

• Static rules (“flag if more than 10 credits in 10 minutes”)
  
• Post-incident analysis (fraud is discovered after a complaint)
  
• Siloed signals (switch logs, infra metrics, and fraud complaints live in different systems)
  

As a result, fraud is often detected too late, after it’s been routed, withdrawn, or reversed.

This isn’t just a tooling issue — it’s a visibility gap.

To detect mule-like behavior early, teams need a connected view of transaction behavior, not just outcomes. They need to understand how, when, and from where transactions happen — not just whether they succeed.

That’s where Business-Centric Observability enters.

Detecting Mule Accounts with a New Lens: Business-Centric Observability

VuNet’s Business-Centric Observability platform doesn’t just track transactions; it observes the entire business journey — correlating log data, telemetry, and behavioral patterns into a unified, time-aligned view. Supported by a contextual data lake, it makes it possible to ask and answer questions that traditional monitoring systems can’t.

With the platform:

• Digital transactions are mapped, patterned, and clustered.
  
• Every hop — from initiation to routing to fulfillment — becomes traceable.
  
• Behavior becomes a signal: volume, frequency, path, and anomalies.
  

VuNet’s platform isn’t a point-solution for fraud detection, but a telemetry-rich foundation with a business-contextual data lake to build risk detection use cases with speed and precision.

Signals From the Digital Core: Detecting Mules Using Logs Alone

Logs from digital transaction switches provide a rich and underutilized source of insight — a window into how transactions unfold, how frequently they occur, who initiates them, and where they land. These logs, when interpreted with the right lens, offer early signals of mule activity and suspicious behaviour patterns.

Every transaction log captures a set of critical data points: a Transaction ID and RRN to trace the journey end-to-end, timestamps to detect frequency spikes, and payer/payee VPAs or account numbers to map relationships. Additional fields such as transaction amounts, response/error codes, device IDs, IP addresses, and channel metadata help form a unique behavioral fingerprint, while bank and PSP codes further reveal issuer/acquirer patterns.

VuNet’s Business-Centric Observability platform enables this intelligence extraction through its domain-centric adaptors, which identify and transform key fields in real time. This makes the data immediately available for analytics, pattern correlation, and detection workflows — all without requiring invasive integrations or custom rule engines.

On their own, each of these fields might seem rather ordinary. But together, they uncover repeatable fraud signals — especially when analyzed at scale.

Here are some of the possible analytics:

Fig 2: Insights from VuNet’s Business-Centric Observability Platform

Fig 3: VuNet delivers drill-down of data by beneficiary for better mule account detection insights

All of this intelligence is derived purely from log data — demonstrating how VuNet’s Business-Centric Observability platform, through its ContextStreams data pipeline and domain-centric adaptors, applies domain logic and contextual modeling to surface high-risk behaviors — all without requiring deep integrations or proprietary fraud systems.

Enhancing Accuracy with Multisource Intelligence

To refine signals further, business-centric observability platform can ingest adjacent data sources — layering more context for higher confidence:

These aren’t mandatory, but when available, they improve intelligence.

How This Empowers Product and Fraud Teams

With this approach, product managers, fraud analysts, and customer service gain:

• Early warning systems based on behavior, not just failures
  
• Cross-team visibility: infra, risk, and ops teams speaking the same language
  
• Fewer false positives, thanks to correlated issue alerting
  
• Faster grievance resolution, with full transaction trail
  
• Compliance-ready visibility, supporting audits and regulatory engagements
  

And most importantly,  the ability to build custom, high-impact detection models without starting from scratch. VuNet’s Business-Centric Observability platform, with its contextual data lake, becomes the base layer for continuous risk innovation.

Fig 4: Business-Centric Observability – A Solution for Detecting Threats from Mule Accounts

Looking Ahead: Shift Left on Fraud with Observability

Fraudsters are rapidly changing their game — and so must the defenses. Waiting for reversals or complaints is no longer sustainable.

Business-Centric Observability, especially when powered by a contextual data lake, enables organizations to detect threats earlier, respond smarter, and adapt faster. It’s not a fraud engine, but it offers the raw material and behavioral visibility to power one — on your terms.

Mule account detection is one example. With the right foundation, dozens of such adjacent use cases are within reach — spanning fraud, partner performance, complaint intelligence, and more.

In Conclusion – From Monitoring to Meaning

Business-Centric Observability isn’t just about detecting issues faster.
It’s about understanding the why behind behaviors, the how behind risks, and the what’s next for response.

Mule detection is just one of many high-impact use cases enabled by a unified, contextual observability platform.

“The value of observability is no longer limited to uptime. It’s about insight. And insight — when tied to business context — becomes foresight. That’s what today’s digital payment ecosystems need.”