SOA Exams & Modules
[mathjax] LEARNING OBJECTIVES Able to construct decision trees for both regression and classification. Understand the basic motivation behind decision trees. Construct regression and classification trees. Use bagging and random forests to improve accuracy. Use boosting to improve accuracy. Select appropriate hyperparameters for decision trees and related techniques. EXAM NOTE As pointed out in Subsection 3.1.1, there are only two …
[mathjax] Case Study 3: GLMs for Count and Aggregate Loss Variables Learning Objectives Select appropriate distributions and link functions for count and severity variables. Identify appropriate offsets and weights for count and severity variables. Implement GLMs for count and severity variables in R. Assess the quality of a Poisson GLM using the Pearson goodness-of-fit statistic. Combine the GLMs for count …
[mathjax] Case Study 2: GLMs for Binary Target Variables Learning Objectives Compared to GLMs for numeric target variables, GLM-based classifiers enjoy some subtly unique features, which will be revealed in the course of this case study. At the completion of this section, you should be able to: Combine factor levels to reduce the dimension of the data. Select appropriate link …
[mathjax] Case Study 1: GLMs for Continuous Target Variables Learning Objectives Select appropriate distributions and link functions for a positive, continuous target variable with a right skew. Fit a GLM using the glm() function in R and specify the options of this function appropriately. Make predictions for GLMs using the predict() function and compare the predictive performance of different GLMs. …
[mathjax] EXAM PA LEARNING OBJECTIVES Learning Objectives The Candidate will be able to describe and select a Generalized Linear Model (GLM) for a given data set and regression or classification problem. Learning Outcomes The Candidate will be able to: Understand the specifications of the GLM and the model assumptions. Create new features appropriate for GLMs. Interpret model coefficients, interaction terms, …
[mathjax] Regularization What is regularization? Reduce model complexity: Reduces the magnitude of the coefficient estimates via the use of a penalty term and serves to prevent overfitting. An alternative to using stepwise selection for identifying useful features. How does regularization work? Variables with limited predictive power will receive a coefficient estimate that is small, if not exactly zero, and therefore …
Accounting Principles
Product Classification Why need product classification? Not all products manufactured by insurance companies are insurance contracts Insurance contracts are those that contain significant insurance risk How products are classified? For valuation purposes, insurance contracts can be further classified into: Ordinary Life – Participating Ordinary Life – Non-Participating Personal Accident Unit-linked (Contracts with an explicit account balance) Universal life (Contracts with …
Introduction IFRS 17 Insurance Contracts establishes principles for the recognition, measurement, presentation and disclosure of insurance contracts issued. It also requires similar principles to be applied to reinsurance contracts held and investment contracts with discretionary participation features issued. The objective is to ensure that entities provide relevant information in a way that faithfully represents those contracts. This information gives a …
Coding & Programming
Definition Types Definition type Description Formula A formula expressed in Prophet’s programming language. Constant A constant value. Global The value is read from the global file at run time. Parameter The value is read from a parameter file at run time. Model point The value for each model point is read from the model point file at run time. Generic …