AI in Insurance: Predicting Customer Churn

Course Info

Date: Dec-23-2024

Length: 1 Week

City: Amsterdam

Fees: 4,600

Type: In Classroom

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Course Details

Course Outline

5 days course

Understanding Customer Churn



  • Introduction to Customer Churn: Understanding the Importance of customer retention in insurance.
  • AI Tools & Techniques: Overview of AI technologies used for predicting churn.
  • Role of AI in Identifying Customer Churn: How AI is used to enhance customer retention strategies.
  • Case Studies: Real-world examples of AI successfully preventing customer churn.


Data Handling & Analysis 


  • Python: Basics of Python programming focused on applications in fraud detection.
  • Data Collection for Churn Analysis: Best practices in collecting and handling customer data.
  • Exploratory Data Analysis (EDA): Using statistical tools to analyze churn data and identify patterns.
  • Data Challenges:  Understanding challenges in Data collection
  • Workshop: Hands-on practice with real insurance datasets to perform EDA.


Machine Learning Techniques for Churn prediction



  • Introduction to Machine Learning Models: Overview of standard supervised learning models used in churn prediction.
  • Feature Engineering for Churn Prediction: Techniques for selecting and engineering features from customer data that are most indicative of churn risk.
  • Hands-On Workshop: Building a predictive model using standard machine learning techniques to estimate churn risk.
  • Model Evaluation and Validation: Introducing methods like cross-validation and AUC-ROC curve to assess model accuracy and mitigate overfitting.
  • Q&A Session: An open discussion on the challenges and practical aspects of applying standard machine learning models in churn prediction.


Advanced Churn Prediction Using Deep Learning


  • Introduction to Deep learning: Introduction to Deep Learning and related frameworks.
  • Deep Learning for Churn Prediction: Exploration of how Deep learning models can be utilized for more complex churn prediction tasks.
  • Building Deep Learning Models: Building Neural Network for churn prediction.
  • Hands-On Exercise: Working on a Real-time Data Set to design Churn prediction models.
  • Case Study Discussion: Reviewing a real-world application where deep learning significantly improved churn prediction over traditional machine learning models.


Deployment, Monitoring & Maintenance


  • Deployment Strategies: Discussing best practices for deploying machine learning and deep learning models into production.
  • Maintaining Model Performance: Techniques for ongoing monitoring and maintenance of models in production.
  • Regulatory and Ethical Considerations: Ensuring that deployment of AI models complies with insurance industry regulations and ethical standards.
  • Final Project Presentations: Participants present their projects, highlighting their approach, the challenges faced, and the solutions implemented.
  • Course Recap and Feedback