Course Details

AI in Insurance holds profound importance in revolutionising industry practices, elevating operational efficiencies, and refining customer interactions. By leveraging AI, insurers gain unprecedented capabilities to predict and manage customer behaviours such as churn. This predictive power enables proactive retention strategies, ensuring customer satisfaction and loyalty.

AI in Insurance: Predicting Customer Churn training course designed to equip insurance professionals with essential AI tools and strategies. This course focuses on leveraging AI to predict and mitigate customer churn, a critical factor in enhancing business sustainability and customer retention.

Participants will gain insights into the application of AI technologies such as machine learning and deep learning specifically tailored for insurance operations. Practical sessions include data handling, exploratory data analysis (EDA), and building predictive models to effectively manage churn risk. By the course's end, participants will possess practical skills to implement AI-driven solutions that drive operational efficiency and improve customer satisfaction in insurance contexts.

By the end of the AI in Insurance training course, participants should be able to:

Understand the significance of customer churn in the insurance industry and the role of AI in predicting churn.
Grasp the fundamentals of AI and machine learning related to customer churn.
Handle and analyze customer data using Python and perform exploratory data analysis (EDA).
Utilize predictive analytics to identify risk factors associated with churn.
Implement machine learning models to forecast customer churn.
Develop targeted strategies to enhance customer retention based on AI insights.
Evaluate and adjust models in response to evolving customer data.

This course is ideal for individuals involved in various roles within the insurance industry, including:

Insurance company executives and decision-makers
Customer relationship managers
Data scientists and analysts working in insurance
Marketing professionals in insurance companies
Policy and strategic planners in insurance

Course Outline

5 days course

Day 1

Day 2

Day 3

Day 4

Day 5

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.
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AI in Insurance: Predicting Customer Churn

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