DeepSite - 🧬 RemixAn end-to-end solution with real-time anomaly detection, behavioral analysis, and graph features - built in just 2 months.
Potential Fraud Detected
Our MVP focuses on rapidly developing a fraud detection system for M-Pesa transactions using machine learning and graph-based features. The solution will identify suspicious patterns in real-time while providing actionable insights.
End-to-end implementation from data pipeline to frontend dashboard in 2 months.
Leverage network analytics to detect complex fraud patterns that traditional methods miss.
API prototype for immediate fraud scoring of incoming transactions with basic alerting.
Amount, type, timestamp
Counts in time windows
New recipients count
Node degree, path counts
Primary supervised model for tabular fraud data
Unsupervised anomaly detection for outliers
Network metrics fed into tree-based models
Our full-stack architecture includes robust backend services built in just 8 weeks.
Our accelerated 2-month development plan focuses on delivering core functionality while building essential AI skills.
Deep dive into M-Pesa fraud types. Design and implement initial simulated data generation with basic transactions + 2-3 fraud patterns.
Refine data generation. Develop temporal and uniqueness features. Prepare data for ML models.
Train and evaluate core tree-based models (LightGBM/XGBoost) on initial features. Establish baseline performance.
Introduce graph representation using NetworkX. Engineer basic graph features. Retrain models with new features.
Implement Isolation Forest for anomaly detection. Explore ensembling/combining predictions.
Build lightweight Flask/FastAPI endpoint for real-time inference. Integrate chosen model(s).
Rigorous testing with generated streaming data. Fine-tune models based on performance.
Performance analysis and reporting. Prepare MVP demonstration and documentation. Outline future enhancements.
Experience our simulated fraud detection system in action. The demo processes synthetic transaction data in real-time.
| Time | Sender | Receiver | Amount | Risk |
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Graph visualization will appear here when demo is running