AI and ML for Fraud Detection
3-Day Training | Hybrid
| 21-23 November 2021

AI and ML for Fraud Detection

The course will not focus on the mathematics or theory, but on the practical applications: the course will provide a mix of technical and theoretical insights and shows you how to practically implement fraud detection models.Moreover, during the course you will understand how to deal with the typical challenges of the fraud analytics task (e.g., data scarcity and imbalancing) and will get advice from real-life experience to help you prevent making common mistakes in the fraud detection domain.
Postdoctoral Researcher & Co-founder of Banksealer, Politecnico di Milano

Available seats

TBA

Difficulty

Advanced
US$ 3,299

Attend in-person

at ADNEC Abu Dhabi

Attend online

via livestream

Date

21-23 November 2021

Time

09:00 to 17:00 GST/GMT+4
To be announced

It is estimated that a typical organization loses about 5 percent of its revenues due to fraud each year. In this course, you will learn how machine learning can be used to fight frauds: you will understand when and how to apply supervised learning algorithms to detect fraudulent behavior similar to past ones, as well as unsupervised learning methods to discover novel types of fraudulent activities.

The course will not focus on the mathematics or theory, but on the practical applications: the course will provide a mix of technical and theoretical insights and shows you how to practically implement fraud detection models.

Moreover, during the course you will understand how to deal with the typical challenges of the fraud analytics task (e.g., data scarcity and imbalancing) and will get advice from real-life experience to help you prevent making common mistakes in the fraud detection domain.

This course is intended for students who have basic experience in the application of data science and machine learning to security-related tasks.

Furthermore, the course is also interesting for anyone who wants to understand how to effectively manage and detect frauds by exploiting data science and machine learning techniques.

  • A basic understanding of data analytics: descriptive statistics (e.g., mean, standard deviation, correlation, confidence intervals, hypothesis testing), data handling (using for example, Microsoft Excel, SQL, etc.), and data visualisation (e.g., bar plots, pie charts, histograms, scatter plots, etc.).
  • A basic understanding of machine learning techniques
  • Familiarity with Python
  • Get to know the fraud phenomenon: fraud detection, prevention, and analytics.
  • Understanding the basic ingredient of any fraud analytical model, Data: data sources, visual exploration, descriptive statistics, data preprocessing activities and feature engineering techniques.
  • Building, applying, and evaluating machine learning algorithms to identify potential frauds: Unsupervised and supervised learning techniques.
  • Virtualbox installed, 6/8GB of RAM and 10GB of storage.
  • Students will be provided with a Linux VM containing all necessary tools and setups

+Testimonials

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+agenda

Title

Details

Date

Introduction to fraud detection and Data Exploration and Preprocessing

– Frauds: Definition and Types
– Fraud Detection and Prevention
– Big data and Analytics
– Fraud analytics process model
– Develop a data-driven fraud-detection system
– Data Preprocessing Step

 

21-23 November 2021
Fraud detection using unlabeled data

– Graphical and Statistical Outlier Detection
– Clustering
– Evaluating and Interpreting Clustering Solutions

21-23 November 2021
Fraud detection using labeled data

– Linear Regression
– Logistic Regression
– Decision Trees
– Neural Networks
– Support Vector Machines
– Ensemble Methods: Random Forests

21-23 November 2021
Evaluating a Fraud Detection Model

– Splitting Up the Data Set
– Performance Metric

 

21-23 November 2021
The Real-life Experience

– Demo and hands on a real fraud analytical engine

21-23 November 2021

Book your spot for this training

+TRAINERS

Michele Carminati
Postdoctoral Researcher & Co-founder of Banksealer, Politecnico di Milano

Michele received his Ph.D. degree cum laude in Information Technology from Politecnico di Milano in Italy, where he is currently a Contract Professor and a Postdoctoral Researcher working as part of the System Security group inside the Dipartimento di Elettronica, Informazione e Bioingegneria.

His research revolves around the application of machine learning methods to various cybersecurity-related fields, ranging from cyber-physical and automotive systems to binary analysis, going through fraud and intrusion detection. In particular, his research focuses mainly on the financial fraud detection task, where he worked on the analysis of advanced financial threats such as banking Trojans, on the design of frameworks to identify and timely detect fraudulent transactions, and on the security evaluation of detection systems against adversarial machine learning attacks.

He is actively involved in research projects funded by the European Union, and he is also co-founder of Banksealer, a Fintech spinoff of Politecnico di Milano.

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