Gen AI for Fraud Detection Analytics

Taught in English

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Course

Gain insight into a topic and learn the fundamentals

Instructor: Edureka

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Beginner level

Recommended experience

2 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Working with Generative AI, delve into email spam classification models, and explore ethical challenges in the field of Fraud Detection.

Skills you'll gain

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There is 1 module in this course

Welcome to the 'Unlocking the Power of 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.

This short course is designed for learners to enhance their capabilities in the field of fraud detection. Throughout the course, participants embark on a journey that explores the innovative intersection of generative AI and fraud analytics. The curriculum covers essential topics, including the principles of generative AI, practical application in fraud detection scenarios, ethical considerations, and regulatory compliance. By combining theoretical knowledge, hands-on experiences, and real-world examples, learners gain the expertise to leverage generative AI effectively in fraud detection. Upon completion, students are equipped with the skills to detect fraud with precision, ensure ethical practices, and comply with regulatory standards, making them proficient in this evolving field.

What's included

12 videos8 readings4 assignments3 discussion prompts

12 videosTotal 51 minutes

Gen AI for Fraud Detection Analytics3 minutesPreview module
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

Instructor

Edureka

37 Courses11,848 learners

Offered by

Edureka

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Frequently asked questions

This course is a comprehensive exploration of the application of generative AI in the field of fraud detection and prevention. It covers a range of topics, including the fundamentals of generative AI, the development of email spam classification models, and the ethical challenges associated with fraud detection using AI.

This course is suitable for Data Scientists, IT/Cybersecurity professionals, AI enthusiasts, students, and business leaders, offering a broad audience the opportunity to master generative AI for fraud detection and prevention.

While prior experience in Python programming is recommended, it's important to note that it's not mandatory to enroll in this course. This means that learners with varying levels of familiarity with Python can still benefit from the course.

In this comprehensive course, you'll embark on a journey to gain a deep understanding of how generative AI can be effectively employed in the field of fraud detection and prevention. You'll develop practical skills in building and optimizing email spam classification models, a crucial component of contemporary fraud detection efforts. Additionally, the course emphasizes the ethical considerations and challenges associated with the use of AI in fraud detection, equipping you with the knowledge and ethical awareness to navigate this specialized domain responsibly.

This course is designed to span approximately two hours, encompassing a diverse range of learning materials and activities. Throughout this course, learners will engage with various educational resources, including video content on the Generative AI and Fraud Detection , reading materials to deepen understanding, graded quizzes to assess comprehension, and thought-provoking discussion prompts to encourage collaborative learning and critical thinking.

Within this course, we extensively utilize Python programming as the primary language for developing an Email Spam Classification model. This model is specifically designed using the advanced GAN (Generative Adversarial Network) model, which is a prominent deep learning technique. Through hands-on exercises and practical examples, you'll gain proficiency in Python programming and explore the intricacies of GAN models for email spam classification.

You won't require any prerequisites for software installation or setup because all the tasks and activities are conveniently conducted within the Google Colab environment. This means you can seamlessly follow along with the course content without the need to install additional software or configure specific settings on your local machine. Google Colab provides a user-friendly and cloud-based platform for hands-on learning, making it accessible and hassle-free for all learners.

Throughout the course, we have extensively explored and utilized essential libraries and frameworks to empower your understanding of generative AI and its applications in fraud detection. Two key frameworks covered in detail are Tensorflow and Keras. Tensorflow, an open-source machine learning framework developed by Google, forms the foundation of our practical exercises. Keras, a high-level neural networks API, is seamlessly integrated with Tensorflow, offering a user-friendly interface for building and training deep learning models.

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.