A continuous read on the Device behind every customer interaction.
An SDK that reads the device at the point of interaction and returns a comprehensive risk assessment: persistent device identity, integrity evaluation, remote-access detection, network and location analysis, and installed-application profiling. Real-time, per session.
Trusted by 20+ banks, NBFCs & fintechs across India
Identities are cheap.
Devices are not.
A fraudster can acquire hundreds of stolen identities for a few thousand rupees each. A fraudster cannot acquire hundreds of phones. The device is the one asset in the fraud chain that does not scale cheaply. Device Intelligence reads it as the ground truth for identity, producing a persistent fingerprint alongside a real-time assessment of whether the device, the network, and the session are genuine.
What Device Intelligence Reads
One SDK call. Every layer of the device read in parallel — identity, integrity, control, connection, and what the device reveals about the person holding it.
Device IDa4f9…c21e
PersistenceSurvives reset
Linked accounts3
Device age412 days
Device Identity and Persistence
Every other identifier can be changed. The device cannot be cheaply replaced. Device Intelligence generates a unique fingerprint from hardware-level signals that persists when everything else has been wiped or manipulated.
Stable across factory resets, reinstallations, and identifier changes
Recognises the same device across sessions, accounts, and applications
Multi-account linkage: all identities associated with a single device surfaced
Historical device risk carried forward across the device's full lifecycle
Root / jailbreakClean
EmulatorNot detected
App cloningNone
Install sourcePlay Store
RASPActive
App and Device Integrity
A manipulated device environment is one of the strongest indicators that the session is not genuine. This cluster evaluates whether the device and application are operating in a trustworthy state.
Root, jailbreak, and emulator detection
App cloning, hooking frameworks, and unauthorised modifications
Installation source verification
Keylogger and harmful application detection
Runtime application self-protection (RASP): tamper, hook, and manipulation assessment
Remote-access apps2 present
Screen mirroringActive
Accessibility abuseFlagged
InteractionCoached
Remote Access and Session Threats
In authorised-push-payment scams, the victim’s own device is used against them, controlled remotely while the fraudster guides the transaction by phone. This cluster identifies sessions where the device is under external control.
Remote-access application detection (presence and count)
Active screen mirroring identification
Accessibility-service abuse detection
Indicators of coached or externally directed interaction
VPN / proxyDetected
GPS spoofingClean
ISPJio · AS55836
SIM2 · swap 6d
Network and Location Intelligence
The network context of a session reveals what the device’s own identifiers may conceal. In the Indian market, where CGNAT renders IP-based signals unreliable across most mobile networks, this layer provides a materially more reliable read.
The applications installed on a device carry risk, affluence, and behavioural signals that no document or bureau score contains. This cluster reads the app portfolio and derives category-level intelligence from it.
Lending application density as a credit-seeking signal
Premium application presence as an affluence indicator
What Device Intelligence Returns
Real-time per session. Three consumption tiers.
Where It Applies
Four deployments, each reading the device for a different decision.
Fraud prevention
Emulator and device-farm detection, multi-accounting, fraud ring linkage, SIM-swap identification
Onboarding
Device integrity verification at account creation or loan application
Monitoring
Continuous device and session consistency across the account lifecycle
Scam prevention
Remote-access detection during live sessions
Integration
The SDK sits alongside your existing stack. No change to your decisioning logic, no data migration, no replacement of systems already in production.
Delivery
SDK (Android, iOS, Web).
SDK footprint
Under 1MB.
Response time
Real-time, per session.
Timeline
3–4 weeks.
Compliance
Non-PII data collection. DPDP Act compliant. GDPR compliant.
Delivery
SDK (Android, iOS, Web).
SDK footprint
Under 1MB.
Response time
Real-time, per session.
Timeline
3–4 weeks.
Compliance
Non-PII data collection. DPDP Act compliant. GDPR compliant.
Trusted by 20+ banks, NBFCs & fintechs across India
Identities are cheap.
Devices are not.
A fraudster can acquire hundreds of stolen identities for a few thousand rupees each. A fraudster cannot acquire hundreds of phones. The device is the one asset in the fraud chain that does not scale cheaply. Device Intelligence reads it as the ground truth for identity, producing a persistent fingerprint alongside a real-time assessment of whether the device, the network, and the session are genuine.
What Device Intelligence Reads
One SDK call. Every layer of the device read in parallel — identity, integrity, control, connection, and what the device reveals about the person holding it.
Device IDa4f9…c21e
PersistenceSurvives reset
Linked accounts3
Device age412 days
Device Identity and Persistence
Every other identifier can be changed. The device cannot be cheaply replaced. Device Intelligence generates a unique fingerprint from hardware-level signals that persists when everything else has been wiped or manipulated.
Stable across factory resets, reinstallations, and identifier changes
Recognises the same device across sessions, accounts, and applications
Multi-account linkage: all identities associated with a single device surfaced
Historical device risk carried forward across the device's full lifecycle
App and Device Integrity
A manipulated device environment is one of the strongest indicators that the session is not genuine. This cluster evaluates whether the device and application are operating in a trustworthy state.
Root, jailbreak, and emulator detection
App cloning, hooking frameworks, and unauthorised modifications
Installation source verification
Keylogger and harmful application detection
Runtime application self-protection (RASP): tamper, hook, and manipulation assessment
Remote Access and Session Threats
In authorised-push-payment scams, the victim’s own device is used against them, controlled remotely while the fraudster guides the transaction by phone. This cluster identifies sessions where the device is under external control.
Remote-access application detection (presence and count)
Active screen mirroring identification
Accessibility-service abuse detection
Indicators of coached or externally directed interaction
Network and Location Intelligence
The network context of a session reveals what the device’s own identifiers may conceal. In the Indian market, where CGNAT renders IP-based signals unreliable across most mobile networks, this layer provides a materially more reliable read.
The applications installed on a device carry risk, affluence, and behavioural signals that no document or bureau score contains. This cluster reads the app portfolio and derives category-level intelligence from it.
Lending application density as a credit-seeking signal
Premium application presence as an affluence indicator
What Device Intelligence Returns
Real-time per session. Three consumption tiers.
Raw Signals
The full set of device-level attributes as structured data. Every signal is individually accessible for institutions that integrate directly into their own decisioning logic.
Device identifier and persistence data
Integrity flags and RASP indicators
Remote-access and session-threat markers
Network intelligence and SIM data
Appographic analysis and category affinity scores
Geolocation
Pre-Built Scores
Two risk scores per session, each accompanied by the rules that fired and the factors that contributed. The verdict is transparent and auditable.
Device risk score (full device history across all sessions)
Session risk score (current session signals)
Applied rules with per-rule breakdown
Recommended action: allow, warn, or block
Custom Risk Models
Scoring models and rule configurations calibrated to the institution's own fraud patterns and device population. The same device signal carries different weight at a bank versus a gaming platform. Sign3 configures accordingly.
Where It Applies
Four deployments, each reading the device for a different decision.
Fraud prevention
Emulator and device-farm detection, multi-accounting, fraud ring linkage, SIM-swap identification
Onboarding
Device integrity verification at account creation or loan application
Monitoring
Continuous device and session consistency across the account lifecycle
Scam prevention
Remote-access detection during live sessions
Integration
The SDK sits alongside your existing stack. No change to your decisioning logic, no data migration, no replacement of systems already in production.
Delivery
SDK (Android, iOS, Web).
SDK footprint
Under 1MB.
Response time
Real-time, per session.
Timeline
3–4 weeks.
Compliance
Non-PII data collection. DPDP Act compliant. GDPR compliant.
Run your last week of
traffic through Sign3.
We'll score it, surface the fraud patterns your current stack missed, and walk you through what we found. Your data, our scoring. No commitment, no integration, no decision required until you've seen the result.
