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Enabling intelligent enterprises through agent-led delivery models that scale business value
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From strategy to implementation, Anblicks helps businesses unlock the full potential of AI, driving measurable results.
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Success Stories

Building an AI-Powered Anti-Money Laundering Monitoring Platform on Snowflake

Add Your Heading Text Here

$40B

Revenue

500+

Offices Worldwide

Download Case Study

    Executive Summary

    A leading financial services firm needed to strengthen its Anti-Money Laundering (AML) compliance by introducing continuous transaction monitoring across high-volume property sales and leasing transactions. While Know Your Customer (KYC) processes were already in place, identifying unusual or potentially illegal transaction patterns remained a significant challenge due to the scale and diversity of transactions. Unlike the banking sector, commercial real estate lacked established AML monitoring platforms, requiring a purpose-built solution.

    Anblicks partnered with the client’s AML and Digital & Technology teams to design and implement an AI-powered transaction monitoring platform built entirely within Snowflake. Combining rules-based intelligence, machine learning, and explainable AI, the solution delivers automated, data-driven risk detection, strengthens regulatory compliance, and establishes a scalable foundation for global AML monitoring.

    The Breakthrough
    • Established a fully governed AML monitoring control, replacing manual transaction reviews with automated enterprise-wide monitoring. 
    • Improved financial crime detection by combining deterministic rules with AI-powered anomaly detection to identify risks beyond predefined typologies. 
    • Delivered investigation-ready alerts with explainable AI, peer comparisons, and contextual intelligence, significantly improving analyst productivity. 
    • Strengthened regulatory readiness through complete auditability of rules, models, alerts, investigation outcomes, and operational execution. 
    • Enabled continuous learning by incorporating investigator feedback into quarterly model retraining, improving detection precision over time. 
    • Reduced operational complexity and total cost of ownership by running data engineering, machine learning, orchestration, and analytics entirely within Snowflake.
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