ClickCease
case study
Banking

TIER 1 BANK DEPLOYS NAPIER AI CLIENT SCREENING

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Summary

A major UK retail and commercial bank serving 20 million global customers, sought to create better customer experiences by consolidating its client screening technologies into a single solution with improved data quality and controls. With operations spanning multiple regions and subsidiaries, the bank needed a cloud-native solution to replace its legacy system, which struggled with high false positives and lacked AI capabilities. In the very first phases of testing, Napier AI was able to outperform the legacy solution, and this name screening accuracy was further improved with tight alignment to the bank’s NextGen strategy, and translation of its risk-based assessment into effective and efficient screening configurations. After a proof of concept (POC) in which Napier AI achieved a 57% false positive reduction, and 100% true matches for sanctions and Politically Exposed Persons (PEPs), the bank selected Napier AI Client Screening. This cloud-native solution provides enhanced name matching accuracy, streamlined compliance workflows, and a flexible, AI-powered approach to financial crime prevention that significantly reduces the Total Cost of Ownership.

Challenge

The bank faced growing regulatory demands and operational inefficiencies due to its outdated screening system. The existing platform required excessive manual intervention, suffered from legacy workarounds, and could not leverage AI effectively.

The bank needed a scalable, adaptable system that could:

• Reduce false positive alerts by 80% and enhance efficiency.

• Consolidate sanctions, PEPs, and adverse media screening into a single platform, and have all true matches identified.

• Perform real-time and batch screening in a unified, automated process for improved user experience.

• Allow financial crime compliance teams greater control over system configurations, without vendor reliance or internal IT change requests.

As new regulations emerged, the ability to make rapid updates became essential. The bank sought a solution that could integrate seamlessly across its global operations and adapt to varied jurisdictional requirements, like real-time screening in specific regions such as Luxembourg.

Solution

Data makes decisions

Napier AI Client Screening was selected after a rigorous POC, testing on real client data from one million customers, during which its adaptability and performance were evaluated over three months.

State-of-the-art APIs for steady integration

The solution allows for seamless integration with existing systems using state-of-the-art APIs, which was a specific need of the customer, ensuring minimal disruption during deployment. Additionally, the no-code sandbox allows for easy configuration, and testing in real-time. This enabled the bank’s teams to adjust screening parameters frequently without having to rely on external support, unlike the legacy solution which incurred significant delays and costs for every screening update.

Scalable screening for millions of customers

Napier AI’s cloud-native design ensures scalability, supporting large-scale customer screening while optimising operational efficiency. Its ability to process high volumes of data, from a single source, with reduced manual intervention aligns with the bank’s strategic goals of automation and innovation.

Results

Results from the phases demonstrate performance improvements following implementation of the new client screening solution. The platform delivered enhanced accuracy, reduced false positives, and stronger compliance outcomes. These findings highlight the effectiveness of a cloud-native, AI-driven approach aligned to strategic objectives and optimised risk-based screening across complex, multi-jurisdictional banking operations.

100%
of true matches for sanctions and PEPs were successfully identified in real-time, more exact matches than the legacy system. Napier AI outperformed the legacy solution in effectiveness and efficiency.
57%
Napier AI achieved a 57% false positive reduction, significantly more in comparison to the legacy solution, based on a limited sample of production data.