Identifying Money Laundering in Bitcoin with Machine Learning

Analytical presentation by MSc. André Monteiro (AI Manager at Banco do Brasil, Master in BI) — summary of Mahmood, H., & Desta, A. (2023) on detecting money laundering in cryptocurrencies.

What you will find:

• The problem: an estimated 3.5% of Europe’s annual GDP (2018) laundered through cryptocurrencies; pseudo-anonymity breaks traditional KYC
• Method: Elliptic Bitcoin Dataset with Logistic Regression, SVM, Decision Trees and Random Forests
• Evaluation: precision, recall and F1-score across the four algorithms
• Findings: ML significantly enhances precision and recall of illicit transaction detection
• Implication: stronger regulatory frameworks through advanced analytics

Format: PDF, 3 pages, English. Immediate download after purchase.