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More than 30,000 professionals make up the ecosystem of Cetif: we facilitate the meeting and exchange between banks, insurers and companies in an academic Center, competent and independent environment to share knowledge, experience and strategies on the most innovative drivers of change.
16 Research Hubs focused on dynamics of strategic evolution, regulatory updates, organizational and process practices, and the effects of digitization: we study innovation trends and best practices and share them with our communities.
Over 60 events including Main events (Workshop and Summit) and Community events (related to research activities) and Webinar: we bring together banks, insurance companies and businesses for shared growth on trends and challenges to outline innovative development strategies.
More than 40 Executive Education tracks, 4 Master's programs and numerous Company Specific Programs: we transfer innovative financial-oriented content with a scientific approach.
An experimental spin off combining academic research and entrepreneurial approach: we turn innovation and digitization into a concrete business advantage.
The project is positioned with a view to applying new Data Driven approaches within the Bank, which makes use of Machine Learning algorithms capable of ensuring a greater degree of efficiency and effectiveness of decision-making in the area of AML. The project aims to emphasize the concept of Transaction Monitoring, i.e., continuous monitoring referring to transactions carried out by customers.
The objective of the Use Case is to identify and assign to each customer an Anti-Money Laundering Behavioral Atypicality Index (AML IAC), i.e., a counterparty riskiness score, with reference to AML areas.
This score does not replace the customer profiling already in place and adopted by the Bank, but is intended to be additional and complementary (not alternative) information, providing a more detailed and dynamic view of the existing risk profile.
The development and grounding of the Use Case also make it possible to: