Analytics Value Training for Insurance

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Session 1

• How to create value.
• Stakeholder analysis.
• What is the use case and value story?
• Introduction to analytics method.
• How to use the Analytics Use Case Canvas.
• How to be agile when working with analytics.
• Introduction to insurance methods.
• Read data from different DB (data lake and data mart).
• Data exploration and diagnostic.
• Data partitioning and honest assessment procedures (Bootstrap and k-fold cross-validation).
• Data quality and data transformation.
• Profit loss matrix.

Session 2

• Stakeholder analysis and interviewing techniques.
• Prepare, explore, and visualize data.
• Predictive analytics.
• Formulate your own analytics use case.
• Data-analytics personas.
• Feature extraction and variable selection methods.
• Claims, churn, and fraud model prediction.
• Triaging claims.
• Pricing and risk selection (pricing improvement).
• Customer segmentation.

Session 3

• Open source and cloud.
• How to implement and take action on analytics.
• Evaluating and monitoring analytics.
• Best practice for deployment analytics.
• Decision process and need analysis.
• Identify themes in claims and transcriptions: text analysis (collect documents, corpus parsing, topic modeling, word embedding, and sentiment analysis).
• Model deployment.

Session 4

• Advanced data preparation and exploration.
• Advanced predictive modeling.
• From informer to influencer.
• Transition model.
• Change model.
• Policy recommendation engine.
• Personalized marketing.
• Customer lifetime value.
• Real-time monitoring of pricing models.

Session 5

• Presentation technique.
• Lifelong learning.
• Machine learning and computer vision.
• Natural language analytics and optimization.
• What are neural networks and deep learning?
• Ethics, bias, and explainability in AI.
• AI next generation.
• Computer vision in insurance.