Estudio de caso

Un gran banco estadounidense vigila las sanciones

08.29.2024 | SymphonyAI team
Descargar

In 2021, a large American financial institution signed a five-year, US$13 million agreement with SymphonyAI for Watchlist Management to monitor sanctions risk. Name screening is essential for any bank, as failing to keep up with data volume and pace can lead to financial crime and hefty regulatory fines. A large US bank faced this challenge and needed an efficient solution. Having lost trust in their previous provider, the bank turned to SymphonyAI, which set out to demonstrate their capabilities. SymphonyAI illustrated their name screening capabilities through a proof of concept (POC) that specifically addressed the bank’s needs. A dedicated team worked closely with the bank to understand and tackle their challenges. During the process, SymphonyAI showcased their expertise, which includes more than 25 years of experience protecting tier 1 banks. They screened over 400 million names to identify risks, processed 100 million customers in just 2.5 hours, and provided unmatched processing power and support. SymphonyAI also offered a holistic, end-to-end product that integrates with other solutions like NetReveal CDD/KYC. Over three years, the partnership has strengthened, with the bank now also using PEP and adverse media screening, including an AI upgrade for enhanced sanctions screening and financial crime management transformation.

Descargar estudio de caso

Últimas noticias

AI-led compliance for insurance
 
10.09.2024 Data sheet

AI-led compliance for insurance

Servicios financieros Icono cuadrado Svg
Craig Robertson talks about using AI to redefine financial crime strategies
 
10.09.2024 Blog

Redefining financial crime prevention in the AI era

Servicios financieros Icono cuadrado Svg
The domino effect: How breaking down silos amplifies financial crime prevention
 
10.08.2024 Webinar

The domino effect: How breaking down silos amplifies financial crime prevention

Servicios financieros Icono cuadrado Svg