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Cover image for 🚀 Building a Graph-Based Fraud
🚀 Building a Graph-Based Fraud Detection System with Neo4j A single transaction may look normal. Its connections can tell a different story. I’m building a learning project using Neo4j and Cypher to explore relationships between customers, accounts, transactions, and devices—and flag patterns worth investigating. 🔍 Patterns I’m exploring: • Multiple customers sharing the same device • Unusually large transactions • Rapid transactions within a short period • Multiple accounts sending money to one account • Circular transfers between connected accounts The focus is explainability: showing the relationships behind each flag so the suspicious activity is easier to understand. Through this project, I’m strengthening my graph data modelling, Cypher queries, and rule-based detection skills. Next: FastAPI integration, transaction risk scores, fraud alerts, and an interactive monitoring dashboard. 🛠️ Currently a learning prototype, with more updates coming as I build. What would you investigate first: shared devices or circular transfers? #Neo4j #GraphDatabase #FraudDetection #Cypher #BackendDevelopment #LearningInPublic
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Cover image for LookInsight AI : AI Customer Support Automation System Full-...
LookInsight AI : AI Customer Support Automation System Full-stack AI system that automates customer support workflows. What it does: → Classifies customer intent (complaint, inquiry, refund, support) → Detects urgency and routes to the right team → Looks up customer history via knowledge graph → Generates personalized response drafts in seconds Key features: • Real-time processing pipeline with live visualization • Multi-channel support (Email, Twitter, Slack, WhatsApp) • Business rules engine for tier-based treatment (VIP, Premium, Regular) • Neo4j knowledge graph for customer context Tech stack: Python, FastAPI, OpenAI GPT-4, Neo4j, Next.js, TypeScript Live demo: https://lookinsight.ai Skills: Python AI OpenAI FastAPI Next.js Customer Support Automation Link: https://lookinsight.ai
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Cover image for RAMP-SENTINEL is a Tier-0 Compliance
RAMP-SENTINEL is a Tier-0 Compliance Architecture designed for the FinTech and Crypto sectors. This document is a redacted portfolio version of the Master Blueprint, showcasing an autonomous Level-2 Anti-Money Laundering (AML) investigator swarm. The primary achievement of this architecture is the elimination of the "Black-Box" liability. It replaces opaque machine learning risk scores with a transparent, deterministic graph traversal trail. By mechanically linking fiat ledgers with on-chain cryptographic data, the system drafts mathematically verified Suspicious Activity Reports (SAR) that meet the strict requirements of financial regulators (FCA, SEC, KNF). [ARCHITECTURAL IP & LIABILITY NOTICE] This document is a redacted sample of a Master Engineering Blueprint. It is intended exclusively for demonstrating architectural methodology and strategic framework design. It does not constitute a completed engineering source of truth for any third-party production environment. All proprietary frameworks and "Zero-Trust Fail-Safe" methodologies contained herein are the exclusive intellectual property of Łukasz Bazan. As a Fractional Architect, I provide the "Engineering Brain." I do not perform direct application coding. Any attempt to implement these patterns by internal teams is done at the Client’s own risk.
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