Lesson 1: Importance of Feature Engineering in Fraud Detection

AI for Fraud Detection in BFSI

Lesson 1: Importance of Feature Engineering in Fraud Detection

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Module 1: Introduction to AI and Fraud Detection

Chapter 1: Basics of AI in BFSI

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Lesson 1: Overview of AI in BFSI

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Lesson 2: Key Components and Capabilities of AI in Fraud Detection

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Summary of Chapter 1 Basics of AI in BFSI

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Video lesson 1

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Video lesson 2

Chapter 2: Types and Impacts of Financial Fraud

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Lesson 1: Common Types of Financial Fraud

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Lesson 2: Impact of Fraud on BFSI Sector

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Summary of Chapter 2 Types and Impacts of Financial Fraud

Chapter 3: Evolution of Fraud Detection Technologies

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Lesson 1: Historical Development of Fraud Detection Techniques

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Lesson 2: Role of Emerging Technologies in Fraud Prevention

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Summary of Chapter 3 Evolution of Fraud Detection Technologies

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Video lesson 3

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Video lesson 4

Module 2: Mastering Data Handling & Preprocessing for AI in BFSI

Chapter 1: Data Collection and Preprocessing Techniques

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Lesson 1: Data Sources and Challenges in BFSI

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Lesson 2: Preprocessing for Fraud Detection

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Summary of Chapter 1 Data Collection and Preprocessing Techniques

Chapter 2: Feature Engineering and Data Quality

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Lesson 1: Importance of Feature Engineering in Fraud Detection

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Lesson 2: Ensuring Data Quality and Its Impact

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Summary of Chapter 2 Feature Engineering and Data Quality

Chapter 3: Practical Exercises on Real Financial Datasets

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Lesson 1: Hands-On Data Cleaning and Preprocessing

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Lesson 2: Exploration of Financial Fraud Datasets

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Summary of Chapter 3 Practical Exercises on Real Financial Datasets

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Video lesson 5

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Video lesson 6

Module 3: Machine Learning Models for Fraud Detection

Chapter 1: Application of Supervised Learning Models

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Lesson 1: Decision Trees in Fraud Detection

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Lesson 2: Logistic Regression in Financial Fraud

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Summary of Chapter 1 Application of Supervised Learning Models

Chapter 2: Advanced Machine Learning Models

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Lesson 1: Random Forests for Fraud Analysis

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Lesson 2: Gradient Boosting Machines for Enhanced Detection

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Summary of Chapter 3 Building, Training, and Evaluating Models

Chapter 3: Building, Training, and Evaluating Models

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Lesson 1: Model Development Workflow

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Lesson 2: Evaluation Metrics for Fraud Detection Models

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Summary of Chapter 3 Building, Training, and Evaluating Models

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Video lesson 7

Module 4: Deep Learning and NLP for Fraud Detection

Chapter 1: Using Neural Networks for Complex Fraud Patterns

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Lesson 1: Basics of Neural Networks in Fraud Detection

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Lesson 2: Advanced Applications of Deep Learning

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Summary of Chapter 1 Using Neural Networks for Complex Fraud Patterns

Chapter 2: NLP Techniques for Fraud Detection

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Lesson 1: NLP Basics and Use Cases in BFSI

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Lesson 2: Integrating NLP into Fraud Detection Projects

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Summary of Chapter 2 NLP Techniques for Fraud Detection

Chapter 3: Practical Deep Learning Applications

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Lesson 1: Implementing Neural Networks on Real Data

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Lesson 2: Case Study: Using NLP to Identify Fraudulent Activities

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Summary of Chapter 3 Practical Deep Learning Applications

Module 5: Anomaly Detection and Behavioral Biometrics

Chapter 1: Anomaly Detection Techniques

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Lesson 1: Statistical and ML-Based Anomaly Detection

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Lesson 2: Tools and Algorithms for Identifying Anomalies

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Summary of

Chapter 2: Behavioral Biometrics in Fraud Prevention

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Lesson 1: Understanding Behavioral Biometrics

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Lesson 2: Applications of Behavioral Biometrics in BFSI

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Summary of Chapter 2 Behavioral Biometrics in Fraud Prevention

Chapter 3: Case Studies and Projects

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Lesson 1: Case Study: Anomaly Detection in Real BFSI Scenarios

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Lesson 2: Hands-On Project: Implementing Behavioral Biometrics

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Summary of Chapter 3 Case Studies and Projects

Module 6: AI in Fraud Detection: Case Studies and Regulatory Compliance

Chapter 1: Real-World Applications and Case Studies

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Lesson 1: Overview of AI-Based Fraud Detection Case Studies

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Lesson 2: Insights from Leading BFSI Organizations

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Summary of Chapter 1 Real-World Applications and Case Studies

Chapter 2: Legal and Ethical Considerations

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Lesson 1: Ethical Concerns in AI Fraud Detection

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Lesson 2: Mitigating Legal Risks

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Summary of Chapter 2 Legal and Ethical Considerations

Chapter 3: Regulatory Compliance

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Lesson 1: Understanding BFSI Regulatory Requirements

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Lesson 2: Designing Compliant AI Fraud Detection Systems

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Summary of Chapter 3 Regulatory Compliance

Module 7: Developing an AI Fraud Detection Strategy

Chapter 1: Strategic Considerations for AI in Fraud Prevention

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Lesson 1: Assessing AI Readiness in Organizations

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Lesson 2: Defining Goals and Success Metrics

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Summary of Chapter 1 Strategic Considerations for AI in Fraud Prevention

Chapter 2: Emerging Technologies in AI Fraud Detection

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Lesson 1: Cutting-Edge Tools and Techniques

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Lesson 2: Future Trends in AI for BFSI Fraud Prevention

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Summary of Chapter 2 Emerging Technologies in AI Fraud Detection

Chapter 3: Design and Implementation of AI Strategies

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Lesson 1: Framework for Strategy Design

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Lesson 2: Case Study: Successful AI Fraud Detection Strategy

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Summary of Chapter 3 Design and Implementation of AI Strategies