Introduction
Financial crime in the digital economy has undergone a profound transformation over the past decade, with mule accounts emerging as one of the most pervasive and difficult-to-detect threats to banks, fintech companies and payment institutions. A mule account, in simple terms, is a bank account used—knowingly or unknowingly—to transfer, receive or layer proceeds of fraud or money laundering. What makes mule accounts particularly dangerous is not merely their role in facilitating illicit movement of funds, but the increasing sophistication of the networks that operate them and the near-industrial scale at which they are proliferating across regions.
In India alone, the Reserve Bank of India (RBI) reported a 33% rise in digital payment frauds in FY 2023–24, with a significant share attributed to accounts later identified as part of mule networks. Globally, the Financial Action Task Force (FATF) estimates that over US$1.6 trillion is laundered annually, a portion of which flows through mule accounts that serve as temporary holding and transit points during the layering phase of laundering. The rise of instant payments, BNPL models, neo-banking, gig economy payouts and micro-lending apps has created countless opportunities for fraudsters to exploit vulnerable individuals and create recruitment pipelines for new mules.
The challenge for banks and fintech companies is that traditional fraud controls—such as rule-based monitoring, static KYC, device fingerprinting and anomaly detection—are no longer sufficient. Mule accounts do not always exhibit overtly suspicious transactional patterns at the outset. Many are operated by first-time offenders, students, gig workers or financially vulnerable individuals whose behaviour blends in with millions of genuine customers. Fraudsters increasingly rely on “clean skins”—accounts with seemingly normal onboarding attributes but subtle behavioural anomalies during login, navigation, transaction authorisation or customer support interactions.
Against this backdrop, behavioural biometrics has emerged as a powerful additional layer in financial fraud detection. It provides the ability to analyse how a user interacts with a device or application—rather than relying solely on what information they provide. This behavioural layer captures micro-patterns that are extraordinarily difficult to fake or transfer, enabling institutions to detect mule activity even when identity documents, IP addresses and transaction flows appear normal. As mule networks grow more sophisticated, behavioural biometrics offers a way to identify risk through the “human layer”, revealing deviations that correlate strongly with coercion, account takeover, scripted behaviour or remote-control manipulation.
Understanding Behavioural Biometrics and Its Relevance to Mule Account Detection
Behavioral biometrics, unlike traditional forms of biometric identification such as facial recognition or fingerprint scanning, focuses on the patterns of behaviour that individuals exhibit when interacting with digital systems. These behavioural traits are subconscious, consistent and inherently difficult for fraudsters to replicate at scale. They include micro-patterns such as typing cadence, scroll velocity, swipe pressure, mouse trajectory, gyro-sensor movement, touchscreen rhythm, hesitation intervals and navigation sequences. Over time, these behaviours create a stable “behavioural signature” that can be used to differentiate legitimate users from coerced, compromised or fraudulent ones.
The technology behind behavioural biometrics relies on advanced machine learning models—often recurrent neural networks or deep sequence classifiers—that continuously learn and refine the behavioural profile of each user. According to a 2024 study by the MIT Media Lab, behavioural biometrics provide 92–98% accuracy when distinguishing between genuine users and impostors, with accuracy improving further when layered with device intelligence and session context. Because these behavioural signals do not depend on physical attributes or static identifiers, they remain highly effective even when users change devices or locations, making them extremely valuable in fraud scenarios involving mule networks.
Mule accounts often behave differently from genuine customer accounts, not because of the identity submitted during onboarding, but because the real operator of the account demonstrates behaviour inconsistent with that identity. For instance, mule accounts are frequently accessed from different devices than the ones used to open the account. Fraudsters may control accounts remotely using remote-access tools (RATs), resulting in abnormal cursor speed, abrupt navigation jumps, or robotic scrolling patterns. Behavioural biometrics excels at identifying these anomalies. In fact, BioCatch’s 2023 Fraud Trends Report highlighted that nearly 48% of mule accounts analysed displayed behavioural inconsistencies within the first 72 hours of activation, even though their KYC documents appeared clean.
The relevance of behavioural biometrics becomes even more pronounced in instant payment ecosystems. With the rise of UPI in India, Faster Payments in the UK and instant SEPA transactions in Europe, financial institutions have seconds—not hours—to detect mule-related anomalies. Behavioural biometrics provides real-time intelligence that helps institutions identify risk signals as they occur, thereby preventing illicit fund flows before they leave the banking perimeter. A study conducted by the UK’s National Economic Crime Centre found that instant payment fraud increased by 22% in 2023, with nearly one-fifth of accounts involved showing abnormal behavioural markers prior to the fraudulent transaction. This reinforces the argument that behavioural biometrics is no longer a niche technology but a practical necessity for institutions fighting sophisticated mule networks.
Why Mule Accounts Are Hard to Detect Using Traditional Methods
Detecting mule accounts in India has become increasingly challenging as digital payments penetrate every layer of society and financial services become more accessible through smartphones. Traditional fraud detection mechanisms—largely built around static KYC checks, rule-based transaction monitoring, device fingerprinting and manual reviews—were designed for a slower, branch-led banking environment. In today’s hyper-digital India, where over 131 billion UPI transactions were recorded in 2023 alone (NPCI), these legacy controls struggle to keep pace with the velocity, volume and variety of mule behaviours.
A fundamental limitation of traditional KYC processes is that they validate identity only once—at the point of onboarding. In India, where Aadhaar-based eKYC enables near-instant account creation, fraudsters exploit this speed by onboarding “clean” identities obtained through leaks, social engineering or purchase from illegal identity markets. The identity documents may be genuine, but the account is controlled by a fraudulent actor. This phenomenon has been repeatedly observed by cybercrime units in states like Telangana and Karnataka, where a surge of fraudulent loan app scams in 2022–2023 involved thousands of legitimate Aadhaar-linked accounts being repurposed as mule accounts, often without the knowledge of the account-holder.
Traditional transaction monitoring systems also face structural challenges in India. Rule-based systems typically flag transactions that exceed predefined thresholds, follow unusual timings or involve suspicious geographies. However, mule accounts involved in UPI, wallet and IMPS frauds often engage in micro-transactions designed to bypass these static rules. For example, the Maharashtra Cyber Cell found that mule accounts used in “digital job frauds” frequently transferred illicit funds in multiple low-value transactions, avoiding scrutiny while moving large sums within minutes. With UPI enabling instant transfers across banks, these patterns unfold too quickly for manual or semi-automated systems to respond in real time.
Device fingerprinting, once a widely used defence mechanism, has also lost effectiveness in the Indian context. Fraudsters increasingly rely on parallel ecosystems of burner phones, cloned devices, virtual machines and spoofed device IDs. In a 2024 report by the Indian Cybercrime Coordination Centre (I4C), investigators revealed that nearly 40% of mule accounts associated with loan app frauds were accessed through multiple devices, often using identical remote-access tools. This makes device-based risk scoring unreliable, as the same device signature may be shared by dozens of mule operators.
Geographical markers—such as IP-based location analysis—are equally unreliable in India. The widespread use of VPNs, public Wi-Fi networks, shared mobile hotspots and remote device control applications masks the real location of the mule operator. Fraud syndicates operating from outside India, particularly in Southeast Asia, exploit cloud-hosted infrastructure to access Indian bank accounts without triggering geolocation red flags. Law enforcement agencies reported in 2023 that thousands of Indian mule accounts were operated from call centres in Dubai, Cambodia and Laos, demonstrating how easily traditional geolocation fences can be bypassed.
Finally, manual investigation capacity in India is limited compared to the scale of digital fraud. Banks often rely on internal fraud teams that are overwhelmed by the sheer volume of alerts generated daily. A 2023 EY–FICCI report noted that Indian banks experience up to 40% false positives in their fraud monitoring systems, which leads to investigative fatigue and delayed action. Mule accounts thrive in this environment because their transactional signatures blend in with millions of legitimate low-value financial activities occurring daily, making them difficult to prioritise.
How Behavioural Biometrics Helps Detect Mule Accounts in the Indian Context
One of the most powerful advantages of behavioural biometrics is its ability to detect coerced or remote-controlled behaviour—both of which are common in Indian mule networks. In many fraud schemes uncovered by state cybercrime units, mule accounts were operated using remote-access applications such as AnyDesk, TeamViewer or Android mirroring tools. These methods leave subtle but detectable behavioural traces: perfectly linear mouse movement, abrupt cursor jumps, uniform swiping rhythms and unnatural typing patterns. Behavioural biometric systems can flag such anomalies within seconds. A 2023 BioCatch study found that over 52% of mule accounts in Asia exhibited “RAT behaviour signatures”, where operator movements mirrored the pattern of remote desktop control rather than natural human interaction.
India’s multilingual, device-diverse and socio-economically varied digital ecosystem also makes behavioural biometrics uniquely advantageous. Genuine customers have stable, personal behaviour patterns that remain consistent despite changes in device, network or environment. Mule operators, however, frequently switch between devices, use cloud-based emulators or operate multiple accounts from the same hardware. In a 2024 report by the Indian Cybercrime Coordination Centre (I4C), investigators found that nearly 60% of mule accounts showed inconsistent behavioural patterns within the first fortnight of usage, a finding strongly aligned with behavioural biometric risk indicators.
Importantly, behavioural biometrics integrates seamlessly with India’s instant payment infrastructure. Given that fraudulent UPI flows often occur within a 5–15 second window, banks cannot rely on manual or traditional monitoring systems to respond in time. Behavioural biometrics provides real-time risk scoring, enabling platforms to challenge or block suspect transactions before funds are irreversibly transferred. According to a 2023 Deloitte India survey, banks that deployed behavioural biometrics saw a 35–48% reduction in mule account-related fraud attempts, highlighting its growing relevance as a frontline defence in India’s digital banking ecosystem.
Key Behavioural Indicators That Banks Can Use to Flag Mule Accounts
Building on the behavioural and contextual anomalies seen in India’s fraud patterns, the detection of mule accounts using behavioural biometrics hinges on recognising subtle cognitive and motor deviations in app, web or device interactions. These signals do not rely on explicit identity markers — their value lies in how strongly they correlate with intent, coercion, operator-switching and fraudulent session control.
Below are the key categories of behavioural indicators that banks and fintechs can incorporate into their mule detection strategies:
1. Cognitive Strain Signatures
Coerced or fraudulent operators exhibit micro-behavioural signs of stress, hesitation and decision lag.
Examples include:
delayed response time before clicking key transaction buttons
repeated navigation back-and-forth between screens
inconsistent typing cadence when entering sensitive data or PINs
abrupt scroll pauses when reading legal statements or warnings
In 2024, the I4C observed that mule accounts tied to extortion-based job scams displayed abnormally long “thinking intervals” at the point of transaction confirmation, especially when the operator was following instructions from remote handlers.
2. Deviations in Device Interaction Consistency
Every genuine customer builds a behavioural baseline over time — swipe pressure, typing patterns, or PIN-entry rhythm.
Mule accounts break these patterns through:
sudden shift in typing cadence
different keyboard layouts
inconsistent scroll friction
altered mouse trajectory curvature
Banks in South India that deployed behavioural biometrics noted that accounts subsequently flagged as mule-linked exhibited device-interaction variance 2.5X higher than normal retail users within the same 90-day window.
3. Remote-Control & Emulation Indicators
RAT-controlled sessions and emulator-based access leave strong behavioural traces:
near-linear mouse strokes
zero inertia scrolling
perfectly timed keystrokes
uniform cursor acceleration
absence of micro-corrections normally seen in human movement
BioCatch benchmarks indicate that machine-assisted sessions can exhibit up to 40–65% fewer micro-movement anomalies than normal users, making them instantly distinguishable under behavioural scrutiny.
4. Multi-Operator Signature Conflicts
Mule accounts rarely belong to a single operator. They may be jointly run by:
recruitment syndicates
tele-fraud callers
cyber gangs
payment intermediaries
This results in sudden behavioural “identity swaps” such as:
different grip pressure on the device
varying hand orientation signatures
conflicting scroll patterns
typing styles indicative of multiple users
In 2023, a Mumbai-based fintech collaborating with state cyber-law enforcement found that almost 62% of mule accounts under investigation showed sequential changes in operator style within the same day — a key hallmark for detection systems.
5. High-Velocity Intent Patterns
Mule operators typically have high intent when transacting and low engagement in other areas of the application.
This behavioural pattern often reflects:
direct navigation to fund transfer screens
bypassing of savings, loan or product pages
minimal browsing history
rapid exit after successful transfer
Whereas genuine users show broader exploratory trails, mule operators are task-driven, often mirroring scripted navigation instructions.
6. Behavioural Mismatch with Onboarding Persona
India’s fraud ecosystem frequently recruits young students, migrant workers and gig earners to open accounts, while real operators are older, professionally trained cybercriminals.
Behavioural AI picks this discrepancy by correlating:
scroll friction patterns
latency during text entry
average pressure & rhythm
biometric-style markers of cognitive maturity
These produce strong “persona mismatch” scores, now being used in multiple Asian banking systems to route high-risk accounts for deeper review.
How Behavioural Profiling Integrates with AML, Transaction Monitoring & UPI Rails
Traditional Anti-Money Laundering (AML) frameworks were built around attribute-level checks — verifying identity, monitoring transaction thresholds, scanning against sanctions lists, and tracing fund-flow anomalies. But mule networks today operate beneath those layers, blending seamlessly with compliant onboarding credentials and micro-transaction patterns that mimic genuine consumer activity.
Behavioural biometrics introduces a new stream of intelligence that complements AML, UPI risk rails and transaction monitoring by enriching decision-making at critical checkpoints:
1. Pre-Transaction KYC Risk Scoring
While AML relies on static onboarding attributes, behavioural biometrics builds a parallel “human authenticity” score.
During login, session initiation or profile modification, behavioural signals confirm whether the account operator is the same person who originally onboarded.
Banks can automatically:
increase AML risk weight
re-run PEP/negative list scans
initiate enhanced due diligence (EDD)
for accounts exhibiting behavioural drift, persona mismatches or remote-control indicators.
2. Behavioural Data as a Trigger for Transaction Monitoring
Transaction monitoring systems primarily rely on monetary thresholds, timing rules and destination mapping.
Behavioural triggers enrich this by detecting intent and control.
For example:
If UPI transfer looks “normal” in value and timing
but the device movements match RAT patterns or coerced operator traits,
the transaction can be escalated, auto-held or revalidated.
UPI PSPs and acquiring banks are increasingly routing suspicious real-time sessions into additional verification challenges based on behavioural anomalies alone.
3. Linking Behavioural Identity with AML Network Graphs
AML network engines today map:
common beneficiaries
money movement loops
shared devices
cluster IPs
Behavioural profiles add another node layer:
✔ operator-movement fingerprint
✔ typing signatures
✔ navigation rhythm
When the same mule controller operates multiple accounts across banks, even using different identities and devices, the behavioural layer exposes linkages invisible to pure data-driven AML tools.
This dramatically reduces the “multi-bank blind spot” that mule networks exploit.
4. Instant Risk-Scoring for UPI Rails
UPI transactions clear in seconds. That leaves no time for batch AML checks.
Behavioural risk engines generate:
live operator authenticity scores
RAT threat markers
device-emulation confidence flags
coercion probability models
within the same session window in which a UPI transaction is being authorised.
Banks can:
hold payouts
add secondary authentication
disable AutoPay mandates
block high-risk beneficiary additions
in milliseconds — long before laundering is completed.
5. Strengthening Suspicious Transaction Reporting (STR)
Behavioural indicators are strong grounds under RBI and FATF guidance for filing STRs, especially when traditional evidence is insufficient.
When AML analysts see behavioural drift that matches known mule typologies:
inconsistent operator signatures
rapid KYC-to-activity abnormalities
behavioural mismatch with declared persona
it can be added as reinforcement evidence in STR narratives, enhancing investigative confidence and regulatory defensibility.
6. Behavioural Biometrics as a Fraud & AML Convergence Layer
Indian regulators are increasingly nudging BFSI institutions toward unified fraud-risk and AML stacks, especially for UPI.
Behavioural profiling supports this convergence through:
shared risk analytics
consolidated investigation workbenches
reduction of false positives
stronger case-building against mule operators, job scam syndicates and laundering networks
It enables banks to make risk decisions with the who, not just the what behind an account’s activity.
How AuthBridge Can Help Banks & Fintechs Detect Mule Accounts
As mule networks evolve, institutions need multi-layered identity assurance that goes far beyond basic eKYC.
AuthBridge strengthens mule-risk intelligence through identity grounding, data triangulation, network exposure checks and onboarding risk scoring, helping financial institutions validate whether the person behind an account is traceable, legitimate and historically clean.
Here are the pillars of how AuthBridge fits in:
1. Strong Foundational Identity Anchoring
Mules thrive when they:
onboard using bogus documents
exploit identity leaks
misuse credentials belonging to vulnerable individuals
AuthBridge eliminates weak onboarding by running:
Aadhaar‐based identity checks (where permissible)
PAN & OVD validation
liveness-bound face match
document authenticity checks
official data-source match
When identity anchor is clean, behavioural analytics from the institution becomes far more reliable — eliminating false positives caused by synthetic or spoofed onboarding.
2. Data Triangulation to Validate True Persona
Mule accounts often provide:
patchy employment data
non-residential addresses
fake references
invalid active phone/email trails
AuthBridge strengthens verification through correlations across:
employment history
HR reference checks
address validation
phone & email existence checks
GST/UDYAM/ROC entity lookups (for merchant onboarding)
If behavioural analytics says “risk anomaly”, and triangulated persona signals also look weak or unverifiable → likelihood of mule risk dramatically rises.
3. Court, Criminal, Cyber & Compliance Screening
Many mule handlers and repeat offenders surface in:
cybercrime FIRs
fraud-linked charge sheets
CIBIL delinquency patterns
court proceedings
AuthBridge’s screening database and bureau-linked filters help flag:
prior fraud listings
criminal prosecution history
identity misuse complaints
This provides AML teams “ground truth” context alongside behavioural anomalies.
4. Device, Contact & Address Intelligence (Where Available)
When banks supply device or session-level metadata, AuthBridge can correlate it against known red-flag parameters from our verification ecosystem:
repetitive addresses tied to multiple high-risk profiles
common employment references among unrelated applicants
shared phone/email identifiers
multiple identities referencing the same coordinates
These linkages are often strong signals of mule syndicates and job-fraud factories.
AuthBridge does not generate behavioural signals but can connect the dots around identity clusters triggered by behavioural suspicion.
5. Bureau-Grade Enhanced Due Diligence (EDD) Triggers
When behavioural risk is high, platforms need to quickly increase scrutiny of the operator.
AuthBridge can power EDD in minutes, including:
physical address verification
criminal court check
employment verifier calls
litigation search
identity re-verification
If a mule handler is masquerading as a clean applicant, these deeper checks expose cracks fast.
6. Automated Document, Profile & Pattern Red-Flagging
AuthBridge flags identity inconsistencies that strongly correlate with mule behaviour:
multiple conflicting addresses
mismatched employment timelines
unverifiable company references
extremely short employment history
use of high-fraud-density addresses
sudden identity attribute change during re-KYC
When this aligns with behavioural risk anomalies → the probability of mule operation spikes.
7. Consent, Data Security & Auditable Evidence
Behavioural triggers alone are often not considered sufficient grounds for STR filing.
Banks need traceable, regulatory-defensible evidence.
AuthBridge strengthens STR narratives by providing:
verified identities
historical documentation
location & address proof
court or negative record data
employer confirmations
timestamped audit trails
This materially strengthens FIU-IND submissions and internal AML investigations.
