1、Practical applications for FinCrime prevention in 2025 AI adoption guideContentsIntroductionTypical AML processUnderstanding generative AIUnderstanding predictive AIUnderstanding agentic AIAI-driven AMLDetect new and hidden riskAlert prioritizationStreamlining investigations and workflowsEnhancing i

2、nvestigation processesAutomate complex workflows with agentic AIHow to start your AI adoptionSymphonyAI solutions1IntroductionFinancial crime prevention is changing.With an influx of new technology,criminals are finding it easier than ever to commit money laundering,fraud,and sanctions evasions.The

3、tools available to criminals such as using AI are also available to financial institutions,enabling them to detect and prevent financial crimes.Tech spend is the priority for 69%of banks and more than 80%are scoping or engaging in AI initiatives in financial crime.Despite this,just 46%of banks repor

4、ted to the Bank of England that they have only a partial understanding of the AI technologies they use.Though parameters and cautions remain,there is increasing acceptance by regulators that using AI can help to mitigate crime.This guide aims to help you understand the practical applications of AI i

5、n AML processes.Sources:Chartis analysis 2024|Bank of England and FCA report,2025.2Typical AML processAlthough it varies by institution,a typical AML process follows some basic principles:Detection Detection engines rely on rules-based scenarios to identify risk.When rules are triggered,they generat

6、e an alert that necessitates a review.Investigation A level 1 investigator will use multiple sources(customer risk scoring,internal&third-party data,etc.)to initially assess risk.Alerts deemed to represent genuine risk are escalated to L2 investigators for further research and will potentially requi