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model training page
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transactions
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Dashboard
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alerts
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analytics
Inspiration
Financial fraud is a growing global issue, with billions lost annually due to fraudulent transactions. With the rise of digital payments, traditional fraud detection methods struggle to keep up. Inspired by the need for smarter, faster, and more reliable fraud detection, we created SentinelAI, an AI-powered fraud detection system that provides real-time transaction monitoring and risk analysis.
What We Learned
Throughout the development process, we gained deeper insights into:
Implementing real-time AI-driven fraud detection.
Optimizing machine learning models for high accuracy and low false positives.
Ensuring secure API integrations with financial institutions.
Leveraging cloud computing for scalable processing.
How We Built It
We followed a structured development process:
Data Collection & Preprocessing – Gathered and cleaned historical transaction data to train AI models.
Machine Learning Development – Built anomaly detection models using supervised and unsupervised learning.
Backend API – Developed a RESTful API to handle transaction analysis requests.
Web & Mobile Dashboard – Created an interactive dashboard for fraud analysts and end-users.
Testing & Optimization – Fine-tuned the model for performance and accuracy.
Deployment – Deployed the system on cloud infrastructure for scalability.
Challenges We Faced
Data Imbalance – Fraud cases are rare compared to legitimate transactions, requiring techniques like SMOTE for better model training.
Real-Time Processing – Optimizing AI models for speed without sacrificing accuracy.
Security & Compliance – Ensuring GDPR and PCI-DSS compliance for secure transaction handling.
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