Anti-Money Laundering: Traditional Methods, Artificial Intelligence Innovations

This study analyzes the evolving landscape of anti-money laundering efforts in the era of artificial intelligence (AI), highlighting the significant transformations brought about by these technologies compared to traditional methods. Historically, money laundering detection relied heavily on rule-based systems and manual analysis. However, modern tools such as AI and machine learning can now simulate and swiftly identify suspicious transactions with high accuracy. Furthermore, blockchain technology enhances the security and transparency of financial systems through its immutable and transparent transaction records. Despite their advantages, these technologies face challenges including high implementation costs, limited availability of high-quality data, and various legal and regulatory barriers that may hinder their widespread adoption. This research also explores operational challenges and data privacy concerns related to the application of emerging technologies, offering recommendations to enhance existing processes. Ultimately, the paper emphasizes the need to integrate blockchain with traditional financial systems and to develop more advanced AI-driven models for improved effectiveness.

Keywords:

Money laundering , Artificial Intelligence (AI) , Blockchain , Machine Learning , Financial Transparency