💡 Inspiration
Financial crime is no longer just about suspicious transactions — it’s about networks.
Cyber teams detect compromised logins. AML teams detect suspicious transactions. But these systems operate in silos.
Criminals exploit that gap.
We built CyberFin Fusion to unify cyber and financial intelligence and move fraud detection from reactive investigation to proactive prevention.
🔎 What it does
CyberFin Fusion:
Ingests cyber events and financial transactions in real time
Builds a relationship graph across accounts, devices, IPs, and beneficiaries
Detects hidden mule rings using community detection
Calculates pre-transaction risk scores (0–100)
Stops high-risk transactions before funds move
Generates AI-powered explanations and SAR drafts
Instead of analyzing rows, we analyze relationships.
🏗 How we built it
Simulated 20,000 cyber events and 2,402 transactions
Built a directed graph using NetworkX
Used the Louvain algorithm to detect dense mule communities
Implemented a composite risk model:
Risk=Cyber(0–40)+Financial(0–30)+Network(0–30)
Built a FastAPI backend with real-time streaming
Added a Streamlit dashboard for visualization
Integrated Gemini API for explainability and SAR generation
⚠️ Challenges we ran into
Bridging structured financial data with semi-structured cyber logs
Selecting a scalable community detection algorithm
Reducing false positives using multi-factor scoring
Building and refining the system within 24 hours
🏆 Accomplishments we're proud of
286 mule rings detected
2,136 high-risk accounts flagged
Real-time pre-transaction kill switch
AI-generated compliance-ready reports
Clean, refactored, Docker-ready architecture
Most importantly, we shifted fraud response from after-the-fact detection to real-time prevention.
📚 What we learned
Financial crime is a graph problem.
Relational databases alone can’t capture structural fraud.
Real-time prevention requires streaming + graph intelligence.
Explainability is critical in regulated environments.
🔮 What’s next
Migrate to distributed graph databases (Neo4j)
Add large-scale streaming (Kafka)
Integrate with banking sandboxes
CyberFin Fusion doesn’t just detect suspicious transactions — it detects suspicious relationships.
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