Case study

Australian bank reduces false positives >47% with SensaAI for AML

06.10.2025
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Inefficiencies in AML monitoring prompt AI-led transformation

A leading Australian financial institution serving approximately one million customers in Australia and New Zealand faced significant challenges in monitoring transactions for anti-money laundering (AML) compliance. The bank’s existing methods generated high rates of false positives, creating inefficiencies and unnecessary costs. With the regulatory landscape becoming increasingly complex and criminals leveraging AI to evade detection, the institution sought a modern, AI-driven solution to enhance its ability to identify genuine threats while maintaining operational efficiency. 

47% fewer false positives and a sharper focus on real risk

The institution turned to SymphonyAI’s SensaAI for AML, attracted by its capability to augment existing monitoring systems without a complete overhaul. By employing advanced AI techniques, SensaAI for AML effectively reduced false positives by more than 47%, identified complex criminal behavior patterns that traditional rules-based systems missed, and provided transparent, explainable results. The smooth deployment of the solution, supported by strong collaboration between SymphonyAI and the bank, led to a significant reduction in the investigation team’s workload and enabled them to focus on real financial crime risks. 

Scaling AI to sanctions screening and KYC

Building on these results, the financial institution is exploring expanding AI integration into other areas, such as sanctions screening and customer due diligence within its Know Your Customer (KYC) processes. This ongoing adoption of AI technology positions the bank to continuously strengthen its defenses and adapt proactively to emerging financial crime challenges in the future.

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