Featured Publication — IEEE · 2025
Risks to Financial Inclusion by Anti-Money Laundering and Financial Counterterrorism Algorithms
AML and counterterrorism algorithms are built to detect illicit financial flows. This paper examines how those same systems exclude the populations they were built to protect.
Anti-money laundering and counterterrorism financing algorithms are deployed at scale across global banking infrastructure. Pattern-matching systems trained on historical data inherit the biases in that history, and threshold calibration optimised for aggregate accuracy can mask systematic errors concentrated in specific demographic groups.
The populations most likely to be flagged are often those with the least recourse: immigrants, the unbanked, people with non-standard transaction histories. Opacity in these systems makes it nearly impossible for affected individuals to understand or challenge an adverse decision.
“Algorithms designed to protect financial systems can construct invisible barriers to financial participation for the communities they were meant to serve.”
The authors argue that financial inclusion must be an explicit design requirement at the architecture level. Responsible AI principles applied during development can preserve the protective intent of AML and CFT systems while materially reducing harm to vulnerable populations.
Authors
Katie Grillaert
Chief Strategy Officer, Assessed Intelligence
Joshua Scarpino
CEO & Founder, Assessed Intelligence
Publication
IEEE
2025
Published Research
Read the Full Article
Risks to Financial Inclusion by Anti-Money Laundering and Financial Counterterrorism Algorithms, published in IEEE, 2025.