High false positives continue to be a primary area of concern for financial firms, which are a substantial drain on resources that could be used more productively. Rules-based systems without advanced analytical capabilities are not adequate to identify suspicious activity and meet regulatory requirement and expectations.
Goals and Objectives
Improve efficiency and effectiveness of AML processes to reduce operational costs while more accurately identifying suspicious activity. As a result, compliance and reputational risks are reduced.
Hardware: Enterprise hardware
Services: Business consulting and managed services (BPO and SaaS)
Software: Analytics, AI, application platforms, and content workflow and management applications
Innovation accelerators: Cognitive machine learning
3rd Platform technologies: Big Data and analytics
Use Case Summary
Firms leverage AI to drive down false positives and automate elements of the investigative process. Explainable machine learning models are continually tuned by feeding investigative results and external data. Predictive analytics prioritizes case work. Regulatory reporting of suspicious activity is highly automated through natural language processing and generation.