Detail kurzu
Anti Financial Crime – Applied Analytics Training
EDU Trainings s.r.o.
Popis kurzu
The Anti Financial Crime – Applied Analytics Training helps you understand and interpret the current regulation and requirements for the anti-money laundering (AML) area. This program is designed to support you in transforming your organization to become data driven and enable you to obtain business value with analytics. We have created an eight-month learning framework where we give you the capacity to learn and reflect on critical analytics skills connected to AML. Rather than feeding you only technical knowledge that can quickly become obsolete, we cover other critical skills to succeed with analytics to create a permanent behavioral change. The program includes 10 days of mandatory training to translate new knowledge into real business issues and cases. Optional days for deeper tool and lab sessions during the period are available, both for the individual and in plenum.
Obsah kurzu
Session 1Introduction to analytics methods.Segmentation.Threshold tuning.Leadership perspective.Create value.Personal communication.Introduction to AML.Codes and Definitions.Prospective view.Session 2Unsupervised learning.Predictive modelling.Business model.Decision-making process.Identifying stakeholders.Core of transaction monitoring.Improve efficiency and effectiveness.Session 3Text analytics.NLP/NLG.QKB – Match codes.Contextual analysis.Preparing meetings.Interviewing techniques.Building trust.Sanctions.Session 4Entity resolution.Entity consolidation.Dynamic networks.Data collection.Interact with stakeholders.Impact business.Explaining with simplicity.TBML, CB, capital markets.Terrorism financing.Session 5RPA, Deep Learning.Agents, auto learning.Challenges with change.Human stages – transition model.Eight-step model.AI next gen.Cílová skupina
AML investigators (data exploration, data visualization, analytic analysis, report creation, report consumption, domain expertise); compliance officers, AML officers, and risk controllers (domain experts, decision makers); quantitative analysts and data scientists (exploratory analysis, descriptive segmentation, predictive modeling); and IT professionals such as administrators, coders, DBA, ETL personnel, and enterprise architects
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