Machine Learning for Insurance: Predict Claim & Assess Risk

Welcome to Machine Learning for Insurance: Predict Claim & Assess Risk course. This is a comprehensive project based course where you will learn how to build insurance risk assessment models, predict insurance claim amounts, and detect insurance claim fraud using models like XGBoost, LightGBM, Random Forest, Logistics Regression, SVM, and KNN. This course is a perfect combination between machine learning and risk assessment, making it an ideal opportunity to level up your data science skills while improving your technical knowledge in insurance business. In the introduction session, you will learn about machine learning applications in insurance and also its technical limitations. Then, in the next section you will learn how insurance risk assessment models work. This section will cover data collection, data preprocessing, feature selection, splitting data into training and testing sets, model selection, model training, assessing risk, and model evaluation. Afterward, you will download insurance datasets from Kaggle, it is a platform that provides many high quality datasets from various industries. Once everything is ready, we will start the project, firstly we will clean the dataset by removing missing values and duplicates, once the data is clean and ready to use, we will start exploratory data analysis, in the first section, we are going to analyze the relationship between age, gender, and insurance claim amount, which will enable us to identify demographic patterns in claim behavior and better understand how different age groups and gender identities influence the likelihood and size of insurance claims. Following that, we are going to find the correlation between body mass index and blood pressure with insurance claim amount, which will allow us to quantify how health indicators relate to the amount claimed, providing valuable insights into health related risk factors. Afterward, we are going to investigate the correlation between smoking status and insurance claim amount, which will help us to evaluate how lifestyle choices such as smoking contribute to higher insurance claim amounts and increased risk profiles.

Then after that, we are going to conduct feature importance analysis using a Random Forest model, which will allow us to identify and rank the most influential features affecting insurance claim amounts, enabling more focused and efficient model development. Next, we are going to predict insurance claim amounts using XGBoost and LightGBM regressors, which will enable us to leverage the power of machine learning to make accurate predictions and capture complex interactions between input features and claim amounts. Following that, we are going to build an insurance risk assessment model using Logistic Regression and Random Forest classifiers, which will enable us to classify individuals based on risk levels, allowing insurance companies to improve underwriting strategies and make informed decisions. Then, we are also going to detect insurance claim fraud using Support Vector Machines and K Nearest Neighbors, which will enable us to identify unusual claim patterns, flag suspicious activity, and reduce financial losses due to fraudulent claims. Lastly, at the end of the course, we are going to test our machine learning models using synthetic data generated by ChatGPT, which will allow us to validate model robustness in diverse scenarios by formatting synthetic datasets into CSV files and uploading them to a Gradio user interface.

Before getting into the course, we need to ask this question to ourselves, why should we integrate machine learning to insurance? Well, here is my answer, machine learning enables insurance companies to make faster, more accurate decisions, reducing costs and improving operational efficiency. By predicting risks and detecting potential fraud more effectively, insurance businesses can enhance profitability and maintain competitive advantage in a rapidly evolving market.

Below are things that you can expect to learn from this course:

• Learn about machine learning applications in insurance and its technical limitations

• Learn how insurance risk assessment models work. This section covers data collection, data preprocessing, feature selection, splitting data into training and testing sets, model selection, model training, assessing risk, and model evaluation

• Learn how to clean dataset by removing missing values and duplicates

• Learn how to analyze relationship between age, gender, and insurance claim amount

• Learn how to find correlation between body mass index and blood pressure with insurance claim amount

• Learn how to find correlation between smoking status and insurance claim amount

• Learn how to conduct feature importance analysis using Random Forest Regressor

• Learn how to predict insurance claim amount using XGBoost

• Learn how to predict insurance claim amount using LightGBM

• Learn how to build insurance risk assessment model using Logistic Regression

• Learn how to build insurance risk assessment model using Random Forest Classifier

• Learn how to detect insurance claim fraud using Support Vector Machine

• Learn how to detect insurance claim fraud using K Nearest Neighbor

• Learn how to test machine learning model using synthetic data

• Learn how to handle class imbalance using Synthetic Minority Oversampling Technique

Hi all, my name is Chris Raharja. I graduated from University of Washington with BS in Mathematics. I used to work as a technology consultant in one of Big 4 firms and now I have been running several different business models such as print on demand, affiliate marketing, drop shipping, ads traffic arbitrage. I have been always passionate about teaching since my first time as a volunteer math tutor in high school. My goal on Udemy is to share my knowledge and build a wonderful community to study many different things together.