F71RA - Machine Learning for Risk and Insurance 1

Course leader(s):

• George Tzougas (Edinburgh, September)

Syllabus

1.1 n/a

2. Gathering and preparing insurance data, identifying relevant risk factors, training and evaluating regression models to predict claim amounts or frequencies. Finally, the predicted values are used to calculate premium rates. (2.1 1. Data Collection: Gather data on non-life insurance policies, including claim frequency/size history and identify relevant risk factors for each insurance branch based on domain knowledge and exploratory data analysis EDA, 2.2 2. Exploratory Data Analysis EDA: Conduct EDA to understand the relationships between risk factors and claim amounts or frequencies. , 2.3 3. Regression Analysis: Use alternative regression models for claim counts and costs., 2.4 4. Ratemaking: Use trained regression models to predict expected claim amounts or frequencies for new policies., 2.5 Calculate premium rates based on these predictions., 2.6 5. Implementation and Monitoring:, 2.7 Implement the new rates in the insurance company's pricing system., 2.8 Continuously monitor and adjust the models and rates based on new data and changing market conditions.)

3. Demonstrate how to apply unsupervised learning techniques in insurance data analysis, particularly using clustering and dimensionality reduction methods to uncover patterns and groupings within the data. (3.1 1. Decision making and Loops in R, Functions in R and R packages, Importing data and exporting data from R, Factors and Subletting in R , Regression in R., 3.2 2. The unsupervised learning methods will aim at:, 3.3 a reducing the dimension of data,, 3.4 b clustering,, 3.5 c graphically illustrating high dimensional data., 3.6 3. The the main objectives of the three types of unsupervised, 3.7 learning methods are:, 3.8 a to minimize the reconstruction error1 of the original data,, 3.9 b to categorize data into clusters of similar cases,, 3.10 c to visualize high dimensional data by dimension reduction, 3.11 but preserve the local topology of the data as far as, 3.12 possible., 3.13 4. Use of a motivating data set for illustrating the alternative, 3.14 unsupervised learning methods., 3.15 5. Installation of Rstudio's Keras package: An R, 3.16 interface to the Python high-level neural networks package, 3.17 Keras.)

4. Apply Principal Component Analysis (PCA) for reducing variables in insurance datasets Analyze how PCA impacts model performance and data interpretability. Build and train neural network models using the Keras API. (4.1 1. PCA for dimension reduction - introduction, standardization of the design matrix, the correlation matrix, mathematical formulation of PCA: solution i: eigen decomposition ED & solution ii: singular value decomposition SVD., 4.2 2. Reconstruction error, PCA for dimension reduction - solutions i & ii: numerical, 4.3 example in R., 4.4 3. Autoencoders - mathematical formulation., 4.5 4. Autoencoders - PCA as an autoencoder, reconstruction of the original variables in the, 4.6 motivating data set using PCA in programming language R., 4.7 5. Feedforward neural networks - basic concepts., training algorithm., training, validation & testing., 4.8 6. Bottleneck Neural Networks: architecture, implementation for our motivating data set using the, 4.9 keras library of R, plot of the reconstruction error for the BNN in R and representation the data by the values of bottleneck neurons, 4.10 and BNN autoencoder plot in R.)

5. Apply supervised learning techniques using regression models to predict insurance claims. Training, evaluating, and interpreting regression models to understand relationships between risk factors and claim outcomes. (5.1 1. Multilayer perceptron neural network NN set up., 5.2 2. NN architecture , feature preparation and fitting in programming language R using real insurance data, , 5.3 3. Deviance loss calculation in programming language R., 5.4 4. Comparison of the Poisson regression and the multilayer perceptron neural network for predicting insurance claims.)

6. Apply K-Means and K-Medoids clustering to identify patterns in insurance datasets using R. implement the Expectation-Maximization (EM) algorithm for fitting mixture models to insurance claims data. apply Gaussian Mixture Models (GMM) for clustring. (6.1 1. K-means clustering – methodology and algorithm., 6.2 2. K-medoids clustering – methodology and algorithm., 6.3 3. K-means and K-medoids clustering implementation for our motivating data set, 6.4 in programming language R., 6.5 4. Gaussian mixture model – methodology, maximum likelihood estimation and Expectation-Maximization algorithm., 6.6 5. Comparison of the Expectation-Maximization Algorithm with K-means and K-medoids algorithm.)

Learning outcomes

By the end of the course, students should be able to do the following:

• Use statistical and machine learning methods for Insurance ratemaking.
• Demonstrate a good understanding of the different unsupervised learning methods in Insurance and utilise advanced software for carrying out unsupervised learning such as the Keras API in R & Python working together.
• Understand and conduct principal component analysis for dimension reduction in insurance and use autoencoders, such as bottleneck neural networks, for dimension reduction in insurance.
• Demonstrate an understanding of alternative supervised learning methods in insurance, such as regression models and Multilayer perceptron neural network models and apply such methods for predicting Insurance claims.
• Demonstrate an understanding of different clustering methods in insurance, show how to perform K-Means & K-Medoids clustering and use the Expectation-Maximization Algorithm for calibrating Gaussian \mixture models for clustering in insurance.

Further details

Curriculum explorer: Click here

SCQF Level: 11

Credits: 15