Financial institutions around the globe are aggressively deploying artificial intelligence (AI) to combat fraud, money laundering and other forms of financial crime — and many are already seeing significant cost savings and efficiency gains. According to a survey of 600 fraud-fighting professionals across 11 countries, nearly three-quarters of organisations say they use AI to detect financial crime, and 87% report that AI has increased their speed of response.
One prominent use case is in transaction monitoring and identity verification, where AI is used to detect synthetic identities, deepfakes, voice-clones and other evolving threats. These systems are designed to go beyond traditional rule-based engines by analysing behavioural signals and networked patterns across large data sets in real time.
The savings are meaningful. For example, major banks report that they are able to cut investigation times, reduce false positives and flag higher-risk activity much earlier. One global bank noted that after introducing an AI system, they found two to four times more instances of financial crime while reducing false positive alerts by about 60%.
Still, challenges remain. Despite the adoption of AI, many banks feel out-paced by criminals who also deploy AI tools, and internal siloes, legacy systems and governance concerns hinder full realisation of benefits. Experts note that effective human-machine teaming, data quality, cross-institution collaboration and regulatory readiness will determine whether the potential savings turn into sustained advantage.
