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IAC - Indicator of Behavioral Atypicality

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.


Mission and Goals

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:

  • Analyze customer operations;
  • attribute an index of atypicality that allows the NDGs under analysis to be reordered, according to a Ranking List sufficient on the risk of each subject;
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