Risks to Financial Inclusion by Anti-Money Laundering and Financial Counterterrorism Algorithms

This paper examines the risks that anti-money laundering and countering the financing of terrorism algorithms pose to financial inclusion. Artificial intelligence can enhance the detection of illicit financial activity, yet it may also perpetuate existing biases, leading to the disproportionate exclusion of dis-advantaged groups. Federated learning expands dataset diversity and preserves privacy, but it also introduces challenges related to transparency, fairness, and bias in AI -driven financial compliance systems, requiring mitigation strategies aligned with financial inclusion.