Bank-Grade Screening at Scale: What Banks Can Learn from High-Volume Non-Banks

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Banks are being asked to do something difficult: screen faster, support digital customer journeys, and reduce noise, all without weakening control. 

That tension sits at the centre of this discussion. Supervisory defensibility on one side; speed, scale and customer experience on the other. Banks cannot give up the first, but they increasingly need more of the second. The firms operating furthest along that second axis are, in many cases, not banks at all. 

Platforms, insurers, gaming and hospitality operators, and payment firms now screen customers and transactions at scale. For banks, these sectors offer a useful comparison rather than a model to copy. Yet, while high-volume non-banks have been forced to solve for entity resolution, alert prioritisation, and real-time decisioning, they do not face the same supervisory scrutiny or low tolerance for missed matches. 

This webinar brings bank and non-bank practitioners together to examine what works, what doesn’t, and what can be transferred. The discussion will focus on where precision can be improved without loosening control, how prioritisation decisions can be explained to supervisors, and which non-bank techniques are worth testing in a bank environment to make screening faster, smarter and safer. 

Key Themes  

• Same exposure, different design: How banks and non-banks build screening around different pressures: supervisory defensibility on one side, and speed, scale and customer experience on the other. 

• Evidence, not architecture: Why banking supervisors want proof that screening works in practice and contributes to better outcomes, such as higher-quality STRs. 

• The push for real-time: How the speed of modern payments forces screening to happen in near real-time, and the implications this has for risk management and the customer journey. 

• Screening smarter, not less: The drive to reduce noise without weakening control, including through AI-driven approaches like risk-based prioritisation, predictive scoring and entity resolution. 

• What banks can take from non-banks: Where high-volume non-banks can offer useful lessons on throughput and efficiency, and where their models are unsuitable for a banking environment.