Analytics Value Training for Insurance

Kód kurzu: AVTINS

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Analytics Value Training for Insurance will help you to understand your digital assets and their value better. This program is designed to transform your organization into a data-driven organization and obtain business value with analytics in the insurance industry. More specifically, you learn how to apply analytics to create business value, how predictive modeling and visualizing can help you find hidden nuggets, and how to impact your organization by using the right communication and message. Our eight-month learning program enables you to learn and reflect on critical analytics skills for Data Analytics professionals in the insurance industry. Rather than exclusively feeding you technical knowledge that can quickly become obsolete, we will cover the critical skills to succeed with analytics and create a permanent behavioral change. The program includes 10 days of mandatory training to translate new knowledge into real business and analytics issues and cases. Optional days for more in-depth tool training and lab sessions during the period are available, both for the individual and in the plenum. 

certifikovaní lektori

uznávané certifikácie

Široká ponuka technických
a soft skills kurzov

Skvelý zákaznicky

Prispôsobenie kurzov
presne na mieru

Termíny kurzov

Počiatočný dátum: Na vyžiadanie

Forma: Na vyžiadanie

Dĺžka kurzu: 70 hodín

Jazyk: en

Cena bez DPH: 6 000 EUR


Forma Dĺžka
Jazyk Cena bez DPH
Na vyžiadanie Na vyžiadanie 70 hodín en 6 000 EUR Registrovať
G Garantovaný kurz

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Cieľová skupina

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Actuaries, data analysts, quantitative analysts, and data scientists; product owners, department managers, and member of management who want to bring analytics value into their organization; and IT professionals

Štruktúra kurzu

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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.

Predpokladané znalosti

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You should have a high engagement for learning more about the drivers for analytics success. You need to have the willingness to change your mindset, and you should be looking forward to having an impact on the further development of your department. There are no formal technology prerequisites for this program.

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