Effective Anti-Money Laundering (AML) solutions can help financial services fight global crime, including human trafficking. Traditional approaches to AML consisted of event scanning based on risk tables. The adoption of ML techniques has helped organizations scale AML to a broad range of data sources and transaction data in real time. Still, ML-only approaches bring inevitable bias that may hinder minorities.
This session explores the technical design and the ethical considerations for an AML solution to extract and ingest data from watch lists and multiple data sources, making data available quickly for analysis, and improve accuracy in detecting AML activity while reducing wasted effort in investigating false alerts. Design and implementation of the ML components of the anti-money laundering solution are also seen in respect of the principles of responsible AI.