Real-Time Fraud Detection and Prevention Platform
A real-time fraud detection and prevention platform leveraging AI/ML behavioural analytics, device fingerprinting, velocity checks, and network graph analysis to identify and block fraudulent transactions across digital channels. Features a case-manage
Multi-Layer Detection.
Before Settlement.
Karsaaz Solutions designs and develops secure, scalable fintech platforms for regulated environments. Our approach combines structured system architecture, integration-ready engineering, and governance-aware delivery to support reliable digital financial operations. We operate strictly as a technology development partner and do not provide financial services.
- Real-time detection across all digital channels
- AI and ML behavioural analytics and device fingerprinting
- Velocity checks and network graph analysis
- Automated chargeback handling
- Case management for analyst review
- Continuous ML model retraining
Key Features
Network Graph Analysis
Entity relationship mapping identifying fraud rings, money mule networks, and coordinated activity.
Case Management Console
Investigation workflow with case assignment, evidence collection, analyst review, and decision documentation.
AI and ML Behavioural Analytics
ML models analysing transaction behaviour and spending patterns to identify fraud anomalies in real time.
Device Fingerprinting
Device identity linking across transactions identifying suspicious patterns and account takeover attempts.
Velocity Checks
Real-time monitoring detecting high-frequency patterns indicative of fraud or account compromise.
Automated Chargeback Handling
Chargeback workflows with automated evidence collection, response generation, and dispute tracking.
Rules-Based Detection Engine
Configurable rules for instant blocking of known fraud patterns and high-risk transaction types.
Continuous Model Retraining
Automated retraining on new fraud data to keep detection accurate as patterns evolve.
Real-Time Fraud Alerts
Instant notifications for detected fraud, blocked transactions, and high-risk activity.
Achieve Measurable Results
Faster Fraud Detection
ML models detect and block fraudulent transactions before they reach settlement.
Reduced Fraud Losses
Multi-layer detection significantly reduces fraudulent transaction volumes.
Fewer False Positives
Behavioural analytics trained on local data reduce false alerts and customer friction.
Streamlined Chargeback Management
Automated workflows reduce manual effort and improve dispute resolution outcomes.
Complete Audit Trails
Every alert, investigation, and model decision is logged for full regulatory traceability.
Scalable Infrastructure
The platform scales with transaction volumes without compromising detection speed.
Why Choose Karsaaz Solutions
Karsaaz Solutions designs and develops secure, scalable fintech platforms for regulated environments. Our approach combines structured system architecture, integration-ready engineering, and compliance-aware delivery. ● Real-time AI and ML detection a
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Device Fingerprinting Across Sessions
Suspicious devices are linked across sessions and accounts, identifying account takeover and synthetic identity fraud patterns.
Chargeback Automation Built In
Evidence is collected automatically, responses are generated, and cases are tracked to resolution within the platform.
Network Graph for Fraud Ring Detection
Entity relationship mapping identifies coordinated fraud rings and money mule networks that individual transaction rules cannot catch.
Multi-Layer Detection From Day One
Rules engine, ML models, device fingerprinting, and network graph analysis run simultaneously on every transaction.
Models Retrained Continuously
Automated retraining on new fraud data means detection keeps pace with evolving patterns without manual model updates.
How We Deploy Your Fraud Detection Platform
Launching your platform with continuous ML model improvement.
Understanding your channels, fraud patterns, risk appetite, and regulatory expectations.
Structuring detection rules, ML models, device fingerprinting, and core banking integration.
Building core banking, payment gateway, and fraud data integrations.
Building fraud detection engine, case management, chargeback handling, and model training.
Validating detection accuracy, false positive rates, chargeback workflows, and compliance.
How We Deploy Your Fraud Detection Platform
Deployment and Model Optimisation
Launching your platform with continuous ML model improvement.
Discovery and Risk Requirements
Understanding your channels, fraud patterns, risk appetite, and regulatory expectations.
Architecture and ML Model Design
Structuring detection rules, ML models, device fingerprinting, and core banking integration.
Integration Build
Building core banking, payment gateway, and fraud data integrations.
Platform Development
Building fraud detection engine, case management, chargeback handling, and model training.
Testing and Validation
Validating detection accuracy, false positive rates, chargeback workflows, and compliance.
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Rules engine, ML models, device fingerprinting, and network graph analysis run simultaneously on every transaction to catch fraud before it reaches settlement.
Automated retraining on new fraud data runs continuously so detection accuracy keeps pace with evolving fraud patterns without manual model updates.
Device fingerprinting links device identities across multiple sessions and accounts to identify account takeover attempts and synthetic identity fraud patterns.
Evidence is collected automatically, responses are generated, and cases are tracked to resolution within the platform. No manual chargeback processing required.
Yes. Network graph analysis maps entity relationships to identify coordinated fraud rings and money mule networks that transaction-level rules alone cannot catch.
Yes. Our platforms connect with core banking systems, payment gateways, and approved third-party fraud data providers.
