Cod. 19 - Anti-Money Laundering Course AI - European School of Banking Management

About This Course

Introduction

The course examines the fundamentals of artificial intelligence (AI) and machine learning (ML), clarifying the distinctions between AI, ML and deep learning and their categories, such as supervised, unsupervised, semi-supervised and reinforcement learning. It explains the how ML algorithms work compared to traditional ones, highlighting key concepts such as training and test sets, and error metrics to evaluate model performance. Finally, the source focuses on thePractical application of AI in anti-money laundering, illustrating the benefits, challenges and different implementations of these technologies to improve the effectiveness and efficiency of banking processes.

Structure

The course lasts approximately three hours. The course includes significant hands-on learning with the practical analysis of real-world cases, proposed by the students themselves, which are then analyzed and discussed in plenary sessions or through exercises led by the instructor. Each training course awards two training credits, required for the renewal of the school's certifications (AMLACERT, KYCACERT, RCACERT, WSACERT) and the CAMS certification.

Program

CHAPTER 1
Artificial Intelligence, Machine Learning and Deep Learning
CHAPTER 2
Traditional and automatic append algorithms
CHAPTER 3
Supervised learning in ML algorithms
CHAPTER 4
Unsupervised learning in ML algorithms
CHAPTER 5
Semi-supervised learning and Reinforcement learning
CHAPTER 6
“Batch” and “Online” learning
CHAPTER 7
“Instance-based” and “Model-based” learning
CHAPTER 8
Error and Robustness: In-Sample and Out-of-Sample Metrics
CHAPTER 9 AI in anti-money laundering

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