SimhVerify Fraud Intelligence Architecture

1. SimhVerify Fraud Engine Architecture

SimhVerify fraud engine is designed as a realtime, scalable and modular fraud prevention infrastructure. The architecture supports fintech-grade onboarding security, realtime fraud analytics, AI-assisted risk scoring and explainable fraud intelligence.

Architecture Goals

Realtime fraud detection
Explainable risk decisions
Scalable event processing
AI-assisted onboarding intelligence
OTP abuse prevention
Document tampering detection
Behavioral anomaly detection

Complete Architecture Flow

Client Apps
     ↓
Verification APIs
     ↓
Fraud Orchestrator
     ↓
Fraud Engines
 ├── Device Intelligence
 ├── OTP Risk Engine
 ├── Behavioral Analytics
 ├── Document Fraud Engine
 ├── Identity Intelligence
 ├── Network/IP Reputation
 ├── Velocity Monitoring
 └── AI Fraud Models
     ↓
Risk Scoring Engine
     ↓
Decision Engine
 ├── Allow
 ├── Monitor
 ├── Review
 └── Block
     ↓
Fraud Dashboard
          

Fraud Engines

Engine	Purpose
Device Intelligence	Detect emulator, rooted device, cloned apps
OTP Risk Engine	Monitor OTP abuse and SMS attacks
Behavior Engine	Detect bots and automation
Document Fraud	Detect fake PDFs and manipulated statements
Identity Intelligence	Cross-verification of user identities

Risk Scoring Philosophy

SimhVerify risk engine should operate using a multi-signal fraud intelligence model. Instead of depending on a single suspicious signal, the system combines device, network, behavioral, OTP, document and identity intelligence into a unified risk score.

Realtime Risking

Every onboarding action should generate realtime fraud signals. Risk recalculation must happen instantly during user flow.

Explainable Decisions

The system should always explain why a user was blocked, reviewed or step-up verified.

Adaptive Intelligence

Risk rules and weights should evolve based on new fraud patterns.

2. Risk Score System Design

The risk engine converts multiple fraud signals into a realtime fraud probability score.

Risk Score Categories

Category	Examples
Device Risk	Emulator, rooted device, VPN
OTP Risk	Rapid retries, SMS abuse
Behavior Risk	Bot clicks, instant OTP entry
Document Risk	Edited PDF, OCR mismatch

Weighted Formula

Final Score =
(Device × 0.25)
+ (OTP × 0.20)
+ (Behavior × 0.15)
+ (Document × 0.25)
+ (Identity × 0.10)
+ (Network × 0.05)
          

Risk Decision Matrix

Score	Risk	Action
0–20	Trusted	Allow
21–40	Low Risk	Monitor
41–60	Medium Risk	Step-up Verification
61–80	High Risk	Manual Review
81–100	Critical	Block

Dashboard Objectives

Provide realtime fraud visibility
Help operations teams review fraud quickly
Enable fraud analysts to investigate suspicious activity
Track OTP abuse, fake onboarding and risky devices
Visualize fraud trends across regions and partners

Realtime Monitoring Layer

Live Fraud Events
   ↓
Risk Engine
   ↓
Realtime Dashboard Feed
   ↓
Alerts & Notifications
            

Dashboard should refresh automatically and show live onboarding attacks, emulator detection, fake documents, OTP abuse and suspicious account creation.

3. Fraud Dashboard UI

Core Dashboard Modules

Realtime fraud monitoring
OTP abuse analytics
Device intelligence panel
Risk heatmaps
Fraud review queue
High-risk onboarding sessions
Blocked IP monitoring

Dashboard Widgets

Widget	Purpose
Fraud Feed	Realtime suspicious events
OTP Abuse Panel	Track SMS attacks
Geo Heatmap	Fraud hotspots by region
Device Analytics	Risky devices and emulators

Database Design Principles

Realtime event ingestion
Scalable analytics
Audit-ready storage
Fraud signal traceability
High-speed risk lookups
Historical fraud analysis

Data Architecture Flow

User Actions
    ↓
Kafka Events
    ↓
Fraud Workers
    ↓
Risk Calculation
    ↓
PostgreSQL Storage
    ↓
ClickHouse Analytics
            

4. Fraud Detection Database Schema

Recommended Tech Stack

Layer	Technology
Transactional Database	PostgreSQL
Realtime Cache	Redis
Event Streaming	Kafka
Analytics	ClickHouse

Core Tables

users
verification_sessions
fraud_signals
risk_scores
devices
otp_events
document_analysis
behavioral_events
fraud_cases
audit_logs
          

Fraud Signal Schema

CREATE TABLE fraud_signals (
  id UUID PRIMARY KEY,
  session_id UUID,
  signal_type VARCHAR(100),
  risk_points INTEGER,
  severity VARCHAR(50),
  metadata JSONB,
  created_at TIMESTAMP
);
          

OTP Fraud Objectives

Prevent OTP bombing attacks
Reduce fake onboarding attempts
Detect automation and bots
Prevent SIM farm abuse
Identify suspicious velocity patterns

OTP Risk Signals

Signal	Meaning
Rapid OTP Retries	Possible automation or abuse
Multiple Devices	Possible fraud ring
Instant OTP Entry	Bot behavior
Geo Mismatch	Possible VPN or remote access

5. OTP Fraud Detection Flow

Realtime Flow

OTP Request
    ↓
Device Check
    ↓
IP Reputation Check
    ↓
Velocity Analysis
    ↓
OTP Generation
    ↓
Behavior Monitoring
    ↓
Risk Scoring
    ↓
Decision
          

Detection Rules

Rule	Action
5 OTP/minute	Medium Risk
10 OTP/minute	Temporary Block
Multiple numbers/device	High Risk

AI Fraud Detection Objectives

Detect manipulated financial documents
Prevent fake salary inflation
Detect edited balances and transactions
Identify synthetic banking behavior
Classify genuine vs suspicious statements

AI Detection Layers

OCR Intelligence

Extract account details, balances, transactions, IFSC codes and statement metadata.

Visual Analysis

Detect overlays, edited fonts, fake logos and PDF inconsistencies.

Transaction Intelligence

Analyze suspicious credits, circular transactions, salary fraud and synthetic entries.

6. AI-Based Bank Statement Fraud Detection

Processing Pipeline

PDF Upload
   ↓
OCR Extraction
   ↓
Metadata Analysis
   ↓
Transaction Parsing
   ↓
AI Fraud Analysis
   ↓
Risk Score
          

Fraud Indicators

Fake salary entries
Edited balances
Manipulated transactions
PDF metadata mismatch
Duplicate statement patterns
Template cloning

Recommended AI/OCR Stack

Tool	Purpose
AWS Textract	OCR extraction
Google Document AI	Financial parsing
OpenAI Models	Fraud reasoning

Compliance Objectives

Ensure lawful data processing
Support fintech audit readiness
Maintain secure data storage
Provide explainable fraud decisions
Enable consent-driven onboarding

Compliance Framework

Area	Requirement
DPDP Act	User consent and data minimization
RBI Readiness	Audit logs and explainable risking
Security	Encryption and access control
Data Retention	Retention lifecycle management

7. Indian Compliance Checklist

Mandatory Compliance Areas

DPDP Act compliance
RBI guidelines
KYC audit readiness
Consent management
Data retention policy
Encryption standards

Security Controls

Requirement	Status
TLS Encryption	Mandatory
AES-256 Storage	Mandatory
RBAC Access	Mandatory
Audit Logging	Mandatory

            Fraud systems should be explainable, auditable and consent-driven to support Indian fintech compliance.
          

Final Positioning

SimhVerify
Verification Infrastructure
+ Fraud Intelligence Platform
for Indian Fintechs
          

SimhVerify should position itself not only as a KYC verification provider, but as a complete fraud-aware onboarding and fintech security infrastructure platform.

SimhVerify Fraud Intelligence Platform

Documentation Sections