HGS launches AMLens to triple AML speed

By Vriti Gothi

HGS has launched AMLens, an AI-powered anti-money laundering (AML) solution aimed at reducing investigation times and improving analyst productivity amid rising regulatory scrutiny and transaction volumes across the financial sector.

Announced on 12 January 2026, the platform is designed to address persistent challenges in AML operations, including alert fatigue, manual case handling, and fragmented data environments. Financial institutions globally are under pressure to strengthen financial crime controls while managing costs, making efficiency and explainability in AI systems increasingly critical.

AMLens combines machine learning and natural language processing with an explainable AI framework, enabling analysts to understand and validate how risk decisions are made. The solution automates data aggregation across structured and unstructured sources—such as transaction records, internal notes, and external databases including public records—presenting them in a consolidated workflow to support faster and more consistent case resolution.

According to HGS, early client deployments show measurable performance gains. Case analysis time has been reduced by around 75%, from roughly two hours to about 30 minutes, while false positive rates have dropped by more than 60%. The company said these improvements have led to a threefold increase in investigator throughput, allowing analysts to process up to 24 cases per day compared with eight previously.

The platform follows a modular, API-first architecture designed to integrate with existing AML and core banking systems. It is targeted at banks and financial services firms across retail and consumer banking, payments and FinTech, credit and lending, and wealth management—segments facing heightened regulatory expectations and growing transaction complexity.

A central feature of AMLens is its human-in-the-loop approach, combining AI-driven risk scoring and narrative generation with analyst oversight. For example, when a third-party system flags a suspicious account, AMLens supports detection, contextualisation, and reporting within a single workflow, enabling analysts to escalate and submit cases with AI-assisted suspicious activity report (SAR) narratives while retaining final judgment.

As regulators increasingly emphasise accountability and transparency in the use of AI for compliance, solutions such as AMLens reflect a broader shift in the AML technology market toward explainable, outcome-driven systems that balance automation with human control.