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Fraud Detection Models and Machine Learning – CR000533

โ‚น800.00



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Subject – AI in Fraud Detection and Prevention in Banking

Industry – Banking Industry

Introduction:

Welcome to the eLearning course on Fraud Detection Models and Machine Learning in the context of the Banking Industry, brought to you by T24Global Company. This course aims to provide you with a comprehensive understanding of fraud detection models and how machine learning can be leveraged to combat fraudulent activities in the banking sector.

In today’s digital age, the banking industry has witnessed a significant increase in the number and complexity of fraudulent activities. Fraudsters are constantly evolving their techniques, making it challenging for traditional rule-based systems to keep up. As a result, banks are increasingly turning to advanced technologies like machine learning to enhance their fraud detection capabilities.

This course will begin by introducing you to the concept of fraud detection and its importance in the banking industry. We will explore the various types of fraud that banks encounter, such as credit card fraud, identity theft, and money laundering. Understanding these different fraud types is crucial for developing effective detection models.

Next, we will delve into the fundamentals of machine learning and its applications in fraud detection. You will learn about supervised and unsupervised learning algorithms, as well as the role of feature engineering in building robust fraud detection models. We will also discuss the challenges and limitations of using machine learning in this context.

Furthermore, this course will provide you with insights into the different machine learning techniques commonly employed in fraud detection models. You will gain a deep understanding of anomaly detection, neural networks, decision trees, and ensemble methods. Real-world case studies and practical examples will be used to illustrate the application of these techniques in detecting and preventing fraudulent activities.

Additionally, we will explore the concept of data preprocessing and its significance in fraud detection. You will learn how to clean and transform data to improve the accuracy and efficiency of your fraud detection models. We will also discuss the importance of data privacy and security in handling sensitive customer information.

Throughout the course, you will have access to interactive quizzes, assignments, and hands-on exercises to reinforce your learning. You will also have the opportunity to engage with industry experts and fellow learners through discussion forums and live webinars.

By the end of this course, you will have gained a solid foundation in fraud detection models and machine learning techniques in the banking industry. You will be equipped with the knowledge and skills to develop and implement effective fraud detection strategies that can help banks mitigate risks, protect their customers, and safeguard their financial assets.

Join us on this exciting learning journey and unlock the potential of fraud detection models and machine learning in the banking industry. Let’s work together towards a safer and more secure banking environment.

NOTE – Post purchase, you can access your course at this URL – https://mnethhil.elementor.cloud/courses/fraud-detection-models-and-machine-learning/ (copy URL)

 

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Lessons Included

 

LS004717 – Fraud Detection Models and Machine Learning – Challenges & Learnings

LS003671 – Case Studies in AI Fraud Detection in Banking

LS002625 – AML and Fraud Integration

LS001579 – Identity Verification and Fraud Prevention

LS000533 – Transaction Monitoring and Anomaly Detection

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