Home

Services

Risk & Fraud Intelligence

Real-Time Fraud Detection · 12 Risk Modules · Live Engine

Detect Fraud Faster
with Real-Time Risk
Intelligence

Protect onboarding, lending, payments, and operations using AI-powered fraud detection, risk scoring, anomaly signals, and automated decision routing — before fraud happens.

Book a Demo Get API Access

🔴 Real-Time Detection

🛡️ AI Fraud Models

🔐 AES-256 Encrypted

📋 DPDP Compliant

✅ RBI-Ready

Fraud Blocked / Day

2,847 🛡️

↑ 18% vs last week

False Positive Rate

0.8% ✓

Industry avg: 4.2%

Risk & Fraud Control — SafematePlus

Live Engine

Fraud Risk Score

High Risk

Identity Score

Verified

Device Risk

Review

🚨

Synthetic Identity Detected

PAN-Aadhaar mismatch · Device spoofing · VPN detected

REJECT

⚠️

Duplicate Profile — 96% Match

Same device fingerprint · 3rd application this week

REVIEW

✓

Identity Verified — Low Risk

All signals clean · Genuine device · Geo matched

APPROVE

✓

Approve

68.4%

⟳

Review

22.1%

Reject

8.2%

↑

Escalate

1.3%

🛡️ Fraud Intelligence Modules

12 Purpose-Built Fraud Detection Engines

Not generic fraud rules. AI models trained on India-specific fraud patterns, document types, identity databases, and lending fraud vectors.

🪪

Identity Fraud Detection

Real-time cross-referencing of submitted identity against government databases — catches mismatches, forgeries, and borrowed identities at intake.

PAN, Aadhaar, DL, Passport cross-check

Name, DOB & address mismatch flags

Liveness detection & face match

🔴 <1 second

📱

Device Intelligence

Fingerprints the device used for application — detects emulators, VPN usage, device spoofing, rooted devices, and devices linked to previous fraud.

Device fingerprint & uniqueness check

VPN, proxy & Tor node detection

Emulator & rooted device flags

🔴 <200ms

👥

Duplicate Account Detection

Identifies repeat applicants attempting to bypass rejection through multiple profiles — linking device, phone, PAN, and behavioural signals across applications.

Cross-application identity matching

Device + phone + PAN correlation

Fuzzy name & DOB matching

🔴 <500ms

🤖

Synthetic Identity Risk

Detects AI-fabricated or assembled synthetic identities — combining real and fake PII that pass individual checks but fail cross-signal coherence analysis.

PII coherence analysis across signals

Credit profile age vs. identity age mismatch

AI-generated image detection

🔴 <1 second

🌍

Geo / IP Risk Checks

Validates applicant location against claimed address and device location — flags impossible travel, high-risk geographies, and location-identity inconsistencies.

IP geolocation vs. claimed address

Impossible travel distance & speed flags

High-risk IP range database

🔴 <300ms

📄

Document Tampering Detection

AI-powered document forensics — detects pixel-level forgeries, metadata manipulation, font inconsistencies, and structural anomalies in submitted documents.

Pixel-level forgery & edit detection

Metadata & EXIF data analysis

Font, layout & structural integrity check

🔴 <2 seconds

📋

Application Fraud Scoring

Composite application fraud score — combines identity, device, document, geo, and behavioural signals into a single 0–100 fraud probability score with explanation.

Multi-signal fraud probability score

Score rationale with top risk factors

Configurable threshold routing

🔴 <1 second

🏦

Bank Statement Anomaly Detection

Detects manipulated, fabricated, or inconsistent bank statements — catches income inflation, transaction fabrication, and round-trip fund manipulation.

Fabricated transaction pattern detection

Round-trip & circular fund flow flags

Income inflation anomaly scoring

🔴 <2 minutes

💸

Transaction Monitoring

Real-time and post-disbursement transaction monitoring — detects mule account behaviour, unusual fund flows, and suspicious transaction patterns.

Mule account pattern detection

Unusual velocity & flow monitoring

Layering & structuring flags

🔴 Real-time

⚖️

AML Risk Signals

Anti-money laundering risk indicators at onboarding and post-disbursement — PEP screening, sanctions list check, and unusual fund source analysis.

PEP & sanctions list screening

Adverse media & news check

Fund source risk indicators

🔴 <500ms

🔍

Blacklist Screening

Checks applicant against internal and external blacklists — RBI defaulter list, court judgment records, previous fraud flags, and cross-institution sharing data.

RBI & court record blacklist lookup

Cross-institution fraud sharing flag

Internal watchlist & previous reject match

🔴 <300ms

📡

Early Warning Engine

Post-disbursement portfolio monitoring — AI-driven signals that flag borrowers showing pre-default stress indicators before they miss a payment.

Bureau inquiry spike monitoring

Cash flow deterioration alerts

Behavioural change detection

🔴 Real-time monitoring

⚙️ How It Works

Real-Time Fraud Decisioning Pipeline

From data submission to fraud decision in under 1 second — fully automated, with full analyst workflow for cases requiring human review.

Step 1 · Intake

User / Applicant Submits Data

Applicant data submitted via API — name, PAN, device ID, IP address, phone, and application details. DPDP Act consent captured digitally and logged immutably. Fraud scoring begins immediately at intake.

REST API

Digital Consent

Device Fingerprint

🔴 Instant

Step 2 · Signal Fetch

APIs Fetch Identity & Behaviour Signals

Simultaneously fetches government identity data, device intelligence signals, IP/geo data, blacklist lookups, and bureau inquiry patterns. All signal sources run in parallel — not sequentially.

Govt ID APIs

Device Intel

Blacklist DB

Geo/IP

🔴 <500ms (all parallel)

Step 3 · AI Scoring

AI Engine Scores Fraud Probability

12 fraud detection modules process all signals simultaneously — identity fraud, synthetic identity, device risk, document tampering, geo anomaly, and AML indicators. Composite fraud score (0–100) generated with top contributing factors.

12 AI Modules

Parallel Processing

Fraud Score 0–100

🔴 <1 second

Step 4 · Decision Routing

Rules Engine Routes Approve / Review / Reject

Configurable rules engine applies your institution's risk thresholds — automatically approving clean applications, routing borderline cases for analyst review, and auto-rejecting high-risk profiles. Policy changes require no code deployment.

Auto-Approve

Manual Queue

Auto-Reject

Escalate

🔴 <100ms

Step 5 · Logs & Monitoring

Audit Logs + Dashboard Monitoring

Every decision, signal, flag, and analyst action logged immutably. Real-time fraud monitoring dashboard for risk teams. Post-disbursement monitoring continues via Early Warning Engine. Exportable for RBI inspection.

Immutable Logs

Live Dashboard

Portfolio Monitoring

RBI Export

🔴 Continuous real-time

📋 Use Cases

One Platform. Every Fraud Vector.

Fraud doesn't respect product boundaries. SafematePlus detects fraud across lending, onboarding, payments, insurance, and workforce operations.

📋

Loan Onboarding Fraud

Catch synthetic identities, document forgeries, and income fabrication at the loan application stage before any disbursement decision.

Synthetic ID

Doc Forensics

Income Anomaly

🏦

Account Opening Risk

Detect mule account creation attempts, stolen identity use, and bot-driven bulk account opening at digital KYC onboarding.

Mule Detection

Device Intel

Velocity Checks

🛒

BNPL Abuse Prevention

High-velocity BNPL fraud — repeat device applicants, synthetic identities, and address clustering for buy-now-pay-never schemes.

Repeat Applicant

Address Cluster

High Velocity

🏪

Merchant Fraud Screening

Onboard genuine merchants while detecting shell businesses, fake GST registrations, and merchants linked to previous fraud networks.

GST Fraud

Shell Business

Network Analysis

🏥

Insurance Claims Risk

Detect fraudulent insurance claims — document manipulation, staged incident patterns, and claimants with histories of suspicious claims.

Claim Pattern

Doc Fraud

History Check

💸

Mule Account Detection

Identify accounts being used as money mule conduits — unusual transaction patterns, rapid fund transfers, and behavioural inconsistency signals.

Fund Flow

Velocity

Behaviour

💼

Staffing Identity Fraud

Detect borrowed identities, forged employment records, and document fraud in background verification for enterprise hiring workflows.

Identity Fraud

Doc Forgery

BGV Fraud

🔄

Repeat Applicant Detection

Link rejected applicants who return with altered details — name variation, new phone numbers, or different devices — using fuzzy matching and device fingerprinting.

Fuzzy Match

Device Link

Cross-Portfolio

⚖️ Comparison

SafematePlus vs Traditional Fraud Control

Why fraud and risk teams are replacing fragmented, manual fraud controls with AI-powered unified intelligence infrastructure.

Swipe horizontally to view full comparison

Capability / Process	✅ SafematePlus	❌ Traditional Fraud Control
Fraud Detection	Real-time AI — <1 second per application	Manual review — hours to days
Synthetic Identity Detection	AI-powered PII coherence analysis	Not detected — passes individual checks
Fraud Tool Coverage	Unified — 12 modules in one API call	Disconnected — multiple tools, no correlation
False Positive Rate	0.8% — AI precision reduces analyst burden	4.2%+ — high analyst fatigue & cost
Investigation Speed	AI-ranked case queue — analysts focus on real fraud	Manual triage — slow and resource-intensive
Portfolio Monitoring	Real-time early warning signals post-disbursement	Reactive — identified post-default
Audit Trail & Explainability	Immutable logs — every decision documented	Weak logs — regulatory audit risk
Policy Configuration	No-code threshold & routing configuration	Code-heavy rule changes — slow iteration

⚙️ API & Developer

Fraud Intelligence
as a REST API

One API call returns fraud score, risk flags, device intelligence, identity match, and decision recommendation — with full documentation and sandbox access in 48 hours.

🌐

Base URL

https://api.safemateplus.com/v1/

🔑

Authentication

Bearer Token (JWT) — per-client provisioning

📡

Webhooks

POST callbacks on fraud flag or decision

📊

Dashboard

Real-time fraud monitoring & case management

99.9%

Uptime SLA

<1s

Fraud Score

24/7

P0 Support

Get API Access Book a Demo

REQUEST — Fraud Intelligence Score
POST /v1/fraud/score
Authorization: Bearer
                                    {API_KEY}
 
{
 "pan": "ABCDE1234F",
 "mobile": "9876543210",
 "device_id": "d8e2a4f1b9c3...",
 "ip_address": "103.24.56.78",
 "modules": ["identity", "device", "geo", "blacklist", "synthetic"],
 "consent": "Y"
                            
}

200 OK — Fraud Score Returned

Response: 847ms

{

"fraud_score": 92,

"risk_level": "HIGH",

"decision": "REJECT",

"flags": [

"SYNTHETIC_IDENTITY_DETECTED",

"DEVICE_LINKED_TO_3_APPLICATIONS",

"VPN_USAGE_DETECTED"

"processing_time_ms": 847

}

🔐 Security & Compliance

Enterprise-Grade Security Infrastructure

Built for regulated financial institutions — every layer of SafematePlus meets Indian financial sector data security and compliance requirements.

🔒

AES-256 Encryption at Rest

All identity data, fraud signals, and decision logs encrypted at rest using AES-256. Keys managed via HSM with quarterly rotation. Zero plaintext exposure at any layer.

FIPS 140-2 Compliant

🛡️

TLS 1.3 In-Transit Security

All API communications encrypted via TLS 1.3 minimum. Mutual TLS (mTLS) available for enterprise integrations requiring certificate-based authentication.

mTLS Available

📋

RBI & DPDP Act Compliance

Audit logs, consent management, and data handling designed for RBI inspection readiness. DPDP Act 2023 consent framework built into every API call.

Compliance Documented

🇮🇳

India Data Residency

All fraud signal data and identity information processed and stored within Indian data centres. Zero cross-border data transfer. DPDP Act localisation compliant.

India-Hosted

⏱️

Immutable Audit Logs

Every fraud decision, analyst action, and API call logged immutably with timestamps. Tamper-proof audit trail ready for RBI inspection and internal compliance review.

Tamper-Proof

🔑

Role-Based Access Control

Granular RBAC — fraud analysts, risk managers, compliance officers, and developers each get scoped access with full SSO/SAML 2.0 support for enterprise identity providers.

SSO / SAML 2.0

📁 Case Studies

Fraud Outcomes That Move Business Metrics

Anonymised client results — full documentation available under NDA upon request.

🏦 Mid-Size NBFC · Personal Lending

Digital NBFC (AUM: ₹5,800 Cr)

Fraud losses reduced by 41% while approval rate improved by 18%

Challenge: High fraud rate in digital lending — synthetic identities and duplicate applications bypassing manual KYC checks. Analysts reviewing 100% of applications manually.

Fraud losses down 41% — Synthetic identity & device linking detection at intake

Manual reviews: 100% → 22% — AI auto-approved 78% of clean applications

Approval rate +18% — Better signal precision reduced false rejects

💳 Fintech Platform · BNPL & Lending

High-Growth Fintech (300K+ Monthly Applications)

Manual fraud reviews cut by 68% — analyst productivity tripled

Challenge: Fraud analyst team of 12 reviewing 300,000 monthly applications. 4.8% false positive rate creating massive analyst fatigue and customer friction on legitimate applications.

Manual reviews reduced 68% — AI handles 78% of volume automatically

False positive rate: 4.8% → 0.9% — Analyst team focus only on real fraud

Analyst productivity 3× — Same team, 3× the fraud case throughput

🏦 Regional Private Bank · Savings Accounts

Private Sector Bank (400+ Branches)

Suspicious account opening detection improved 52% — mule network busted

Challenge: Significant mule account creation in digital account opening channel. Manual KYC process missing device-linked fraud rings operating across multiple branches.

Suspicious onboarding detection +52% — Device intelligence linked fraud ring of 840 mule accounts

Mule account network identified — 840 linked accounts flagged and reported to FIU

RBI audit pass — Full immutable audit trail met regulatory inspection requirements

💬 What Risk Leaders Say

Trusted by Fraud & Risk Decision-Makers

★★★★★

"The synthetic identity detection module caught fraud patterns that our manual KYC team had been missing for months. The cross-application device linking alone surfaced a fraud ring we would never have found otherwise. The RBI audit trail documentation was exceptional — our compliance team was impressed."

Vijay Krishnamurthy

Chief Risk Officer · Lending NBFC, South India

★★★★★

"Our fraud analyst team was drowning in manual reviews with a 4.8% false positive rate. After deploying SafematePlus, we dropped to under 1% false positives and our analysts now only see real fraud cases. The same team handles 3× the volume. That's the operational impact we needed."

Riya Mehta

Head of Fraud Control · Digital Bank

★★★★★

"What convinced us was the API-first architecture. We integrated fraud scoring directly into our loan origination flow without replacing our existing tech stack. SafematePlus plugged in within 4 weeks — sandbox to production. The configurability of routing thresholds without code changes is exactly what fast-moving fintechs need."

Arjun Sharma

COO · Fintech Lending Platform

Detect Fraud Fasterwith Real-Time RiskIntelligence

Detect Fraud Faster
with Real-Time Risk
Intelligence