Master Insurance Policy Forecasting: From Data Integration to Predictive Modeling
What you will learn
Predicting Insurance Policy Case Size: Develop skills to forecast the amount customers are likely to pay for insurance.
Banking & Insurance Data Integration: Work within a bancassurance model alongside roles like Data Architect, Data Scientist, Data Engineer, and more.
ETL & Data Warehousing: Extract, transform, and load data into data warehouses and marts using Apache NiFi, MySQL, and more.
Machine Learning Application: Build and deploy models (XGboost and ANNs) to forecast insurance policy case sizes
Why take this course?
Unlock the power of predictive analytics in the insurance and banking industries with our comprehensive course on Customer Spending Forecasting: Insurance Policy Case Size Prediction. This course equips you with the tools and techniques to predict the policy case size—the expected amount a customer may pay for an insurance policy—based on demographic and financial data. With this skill set, you’ll be able to drive revenue growth, enhance customer targeting, and personalize offers in a competitive insurance landscape.
The course begins by setting the business context within the bancassurance model, where banks and insurance companies collaborate to provide tailored insurance offerings. You’ll learn to navigate the business problem and work within various roles, such as Data Architect, Data Analyst, Data Scientist, and Data Engineer, to deliver a cohesive solution. Gain hands-on experience with essential customer information, from demographic details to financial insights, that form the backbone of the model.
Our course walks you through the entire data integration pipeline, from accessing and ingesting data from diverse sources (like core banking, and card management systems) to centralizing it in a Data Warehouse (DWH) and Data Mart environment. You’ll dive into end-to-end data flow, covering ETL (Extract, Transform, Load) processes and real-time streaming with technologies like Apache NiFi and Kafka.
As we proceed, you’ll build and deploy machine learning models using Python, Jupyter Notebook, XGBoost, and Artificial Neural Networks (ANN). These models are trained on financial indicators like credit card limits, CASA balances, and spending behavior to predict insurance policy case sizes accurately. With BI tools such as Power BI and Tableau, you’ll also learn to visualize and report insights effectively.
This course is perfect for data enthusiasts, aspiring data scientists, and banking professionals looking to upskill in the rapidly growing field of predictive analytics. Join us to harness the power of data in transforming insurance and banking strategies for the future!