Securing Financial Transactions with Real-Time AI
Our AI-powered real-time fraud detection system is designed to safeguard your business and customers by instantly identifying and preventing fraudulent activities. By leveraging advanced machine learning algorithms and intelligent transaction monitoring, it minimizes financial losses while ensuring every transaction remains secure and seamless.
4B+
Transactions per Years
100%
Yoy Growth Managed
1,000+
Real-time Features
<150ms
P99 Latency
Business Overview and Strategic Direction
EXECUTIVE SUMMARY
JazzCash faced the challenge of monitoring billions of transactions in real time while minimizing false positives that could disrupt legitimate customer activity. The company needed a scalable and intelligent system that could detect and prevent fraudulent behavior instantly, ensuring both financial security and a seamless customer experience.
To achieve this, stixor helps JazzCash in implementing an AI-driven fraud detection strategy combining advanced scoring algorithms, graph analytics for uncovering complex fraud networks, and rules-based controls to protect revenue. This multi-layered approach not only reduces financial losses but also builds customer trust, enabling the platform to operate securely and efficiently at scale.
Operational Challenges in Real-Time Fraud Detection
Building a real-time fraud detection system demands accuracy, scalability, and seamless transactions. Key challenges:
PROBLEM STATEMENT
False Positives
Avoid unnecessary friction for legitimate customers while maintaining detection accuracy.
Fraud Evolution
Continuously adapt to new fraud techniques using predictive analytics.
Infrastructure Scaling
Support a projected 8 billion transactions in Year 2 while maintaining low latency and high reliability.
High transaction volume
Handling peaks of approximately 625 transactions per second (TPS) in real time.
Multi-Stage AI Platform for Fraud Prevention
A powerful AI-driven engineering platform designed to improve calculation accuracy, streamline technical workflows, and support faster, more consistent decision-making across all engineering tasks.
Our Solution
Deep Learning Architecture
24+ months of historical data, verified fraud cases, and records. Feature engineering includes 1,000+ real-time features for accurate, scalable fraud detection.
Model Training & Evaluation
Ensemble models, graph analytics, and semi-supervised learning detect fraud patterns and rings efficiently.
System Deployment & Monitoring
Real-time scoring API, rule engine, automated retraining, and monitoring with Prometheus, Grafana, MLFlow ensure reliabilit
01
Data Collection & Preparation
- 24+ months of historical transactions and verified fraud cases.
- Clean, standardize, and validate data for accuracy and consistency
- 1,000+ real-time features (transaction, user, device, network).
02
Development & Deployment
- Deploy real-time scoring API with rules for known patterns.
- Test system for peak load, targeting high fraud reduction.
- Train Gradient Boosting, Neural Network ensemble, graph analytics.
03
Scaling & Monitoring
- Scale to 8B transactions/year with cloud infrastructure.
- Continuously retrain models with new data and detect fraud evolution.
- Monitor with Prometheus/Grafana dashboards and alerts.
Transformative Outcomes Across All Metrics
IMPACT & RESULTS
Fraud Detection Performance
≥20%
Fraud Loss Reduction(year 1)
≤2%
False Positive Rate
<150ms
Real-time Scoring Latency(P99)
≥99.95%
System Uptime
Operational Efficiency
4B+
Transactions Processed(yearly)
8B
Scalability (year 2)
~625 TPSS
Peak Throughput
Technology Stack
TOOLS USED
HIPAA Compliance
Docker / K8s
Python / PyTorch
Apache Kafka
Discuss Your Enterprise
Use Case
From small to large scale enterprises, we deliver next-gen AI, data engineering, and actionable insights.
