New Delhi: HGS, a digital experience, business process management, and digital media services company, has announced the launch of AMLens, an AI-powered solution aimed at transforming Anti-Money Laundering (AML) operations for financial institutions.
AMLens uses machine learning and Natural Language Processing to help banks reduce investigation time, cut false positives, and meet strict regulatory requirements more effectively.
AMLens is positioned as a dynamic AI solution that evolves with changing risk patterns and regulations. It addresses common challenges in AML operations such as manual case handling, alert fatigue, and gaps in regulatory alignment. The platform supports AML teams across the entire investigation lifecycle, including detection, triage, contextual analysis, and case summarisation.
Explainable and Modular AI Architecture
The solution is built as a modular, explainable AI ecosystem that focuses on transparency and human oversight. AMLens automates the collection and consolidation of fragmented data from multiple sources, including structured transaction data and unstructured analyst notes. It also integrates external information from public records and third-party platforms such as Google and LexisNexis, presenting all inputs in a single, analyst-friendly view.
Designed with an API-first architecture, AMLens can be integrated into existing banking systems with minimal disruption. It is available for financial institutions across retail and consumer banking, payments and fintech, credit cards and lending, and wealth management.
Human-in-the-Loop Approach
Eric Purdum, Head of Sales – Americas at HGS, said that legacy AML systems overwhelm analysts with fragmented data and false alerts. He noted 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, allowing analysts to focus on high-risk cases.
A key feature of AMLens is its three-stage workflow that blends automation with human judgment. When an alert is triggered, the system first assesses risk, then supports analysts in gathering additional context, and finally assists in generating policy-aligned narratives for Suspicious Activity Reports. This approach enables faster and more confident decision-making, reducing case resolution time to under an hour.
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