HGS, a provider of digital experience, business process management and digital media services, has launched AMLens, an AI-powered solution aimed at transforming and accelerating anti-money laundering operations for financial institutions. AMLens integrates machine learning and natural language processing to reduce case analysis time and false positives, helping banks meet stringent regulatory requirements while improving investigator productivity. The platform is designed to treat artificial intelligence as an evolving practice rather than a static system, addressing industry challenges such as manual case resolution, alert fatigue and gaps in regulatory alignment. The solution supports AML teams across detection, triage, contextualisation and summarisation through a modular and explainable AI framework. It automates the collection and consolidation of fragmented data, ranging from structured transaction records to unstructured notes and external public sources, into a single, analyst-friendly view. Built on a modular, API-first architecture, AMLens can be integrated with existing client systems and is targeted at financial services segments including retail and consumer banking, payments and fintech, credit cards and lending, and wealth management. Commenting on the launch, Eric Purdum, Head of Sales – Americas at HGS, said legacy AML systems overwhelm analysts with false positives and fragmented information, limiting their ability to act quickly. He added that AMLens combines explainable AI with human-in-the-loop validation to automate routine tasks such as transaction monitoring, sanctions screening and customer due diligence, enabling analysts to focus on high-priority investigations. Early client deployments have demonstrated strong performance improvements. Case analysis time has been reduced by around 75 per cent, from approximately two hours to about 30 minutes, while false positives have fallen by more than 60 per cent, from about 18 per cent to 7 per cent. Investigator productivity has increased threefold, from an average of eight cases per day to 24 cases per day, with overall turnaround times improving by 75 per cent, from 48 hours to 12 hours. A key feature of AMLens is its integration of AI-generated narratives with human oversight. When a third-party system flags suspicious activity, AMLens applies risk categorisation, enables analysts to request additional information within the workflow, and supports escalation through AI-assisted suspicious activity report preparation. This approach preserves human judgement while streamlining workflows, reducing end-to-end case resolution time to under an hour.