What you'll learn

Understand the foundations of fraud detection and how Generative AI transforms this field.
Apply advanced AI models such as GANs, NLP, and LSTM to fraud detection use cases.
Build hands-on project for fraud detection and anomaly analysis with AI.
Evaluate future trends, ethical considerations, and challenges in AI-driven fraud analytics.

Skills you'll gain

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Assessments

4 assignments

Taught in English

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There is 1 module in this course

Welcome to the 'Generative AI in Fraud Detection Analytics' course, where you'll embark on a transformative journey to acquire practical expertise in generative AI for fraud prevention.

Throughout this course, you'll delve into the world of AI-driven fraud detection, mastering the fundamentals and exploring real-world applications. By the end of this course, you will be able to: - Gain a comprehensive understanding of generative AI in fraud detection. - Utilize generative AI techniques, especially the LSTM and GAN model, for practical email fraud detection projects, strengthening the capacity to employ AI in real-world fraud prevention scenarios. - Grasp the key concepts of generative AI's role in fraud detection, encompassing ethical considerations and best practices for data handling, establishing a strong foundation in AI-driven fraud analytics. This course is tailored for learners from diverse backgrounds, including data scientists, fraud analysts, AI enthusiasts, and professionals aiming to enhance their skills in fraud analytics. Prior experience in AI and fraud detection is beneficial but not required. Embark on this educational journey to master Generative AI for Fraud Detection Analytics and elevate your expertise in fraud prevention.

Enhance your fraud detection skills with Generative AI. Learn core principles, real-world applications, and ethical practices to detect fraud with accuracy and compliance.

What's included

12 videos8 readings4 assignments3 discussion prompts2 plugins

12 videosTotal 51 minutes

Gen AI for Fraud Detection Analytics3 minutes
Introduction to Generative AI4 minutes
Understanding Gen AI's part in Fraud Detection5 minutes
Technological Advancements of Generative AI in Fraud Detection6 minutes
Overview of the Project2 minutes
Project Development2 minutes
Data Collection and Pre-Processing5 minutes
Setting-up LSTM Model4 minutes
Setting-Up GAN Model Architecture4 minutes
Ethical Challenges in Fraud Detection4 minutes
Regulatory compliance and Privacy protection4 minutes
Course Summary2 minutes

8 readingsTotal 68 minutes

Course Overview5 minutes
How to Use Discussion Forums2 minutes
Unleashing the Potential of Natural Language Processing (NLP)10 minutes
Introduction to LSTM- A deatiled Explanation7 minutes
Introduction to Generative Adversarial Networks- From core principles to diverse application7 minutes
Unveiling Vital TensorFlow Keras Imports for GAN Development7 minutes
Real world Application of Fraud Detection using GenAI5 minutes
Practice Project25 minutes

4 assignmentsTotal 33 minutes

Knowledge Check: Overview of Fraud detection and Generative AI5 minutes
Knowledge Check: Email Fraud Detection using GAN model5 minutes
Knowledge Check: Best Practices3 minutes
End Course Knowledge Check: Module Wrap Up and Assessment20 minutes

3 discussion promptsTotal 25 minutes

How do you envision the integration of generative AI in fraud detection transforming the landscape of fraud prevention? 10 minutes
How can generative AI models like GANs (Generative Adversarial Networks) be effectively utilized to improve the accuracy of email spam classification?10 minutes
What ethical challenges do you foresee in implementing AI-driven fraud detection systems, and how can these challenges be mitigated?5 minutes