I’m a London-based aspiring AML / Financial Crime Data Scientist with a background in data science + software engineering, passionate about using ML and analytics to detect suspicious behaviour, reduce false positives, and support investigators with clear, explainable insights.

• 🔎 AML & Financial Crime Analytics (transaction monitoring, typologies, alert triage, customer risk)
• 🧠 Machine Learning (anomaly detection, classification, model monitoring, explainability)
• 🕸️ Graph / Network Analysis (entity resolution, suspicious networks, relationship patterns)
• 🗣️ NLP (case narratives, adverse media signals, summarisation for investigators)
• 🧰 Data Engineering basics (clean pipelines, data quality checks, reproducible workflows)

Python (pandas, numpy, scikit-learn) • SQL • Jupyter • Git/GitHub
Web: React, Node.js, Express • API integration • Testing

Play here
A hybrid game blending Tic-Tac-Toe + Minesweeper:

• 9x9 grid with hidden mines
• Real-time multiplayer (Socket.IO)
• React.js (frontend) + Node.js (backend)

Why it matters: real-time state, event streams, and probability-style thinking — useful habits for detection systems.

Repo
A Korean language learning app designed to make Hangul engaging:

• Interactive lessons + quizzes

Why it matters: structured content, UX iteration, and building learning loops (useful for investigator tools & internal enablement).

• MAMA GRIDVIEWGAME — WIP game prototype
• Pantone Color Tetris Game — visual/pattern-based gameplay

Note: I removed public login credentials from this README for security.

• 🧩 Synthetic transaction dataset + AML typology simulator (layering, smurfing, mule chains)
• 📈 Alert prioritisation model (reduce false positives with explainable features)
• 🕵️ Investigator dashboard prototype (case summary, entity graph, timeline)

If you’re working on AML / fraud / financial crime data, I’d love to collaborate or learn from your approach.

• GitHub: https://github.com/tiffjai
• LinkedIn: (https://www.linkedin.com/in/tiffanywingkaho)