SOA Exams & Modules
Summary of Questions Question Answer What are the modeling improvements? Modeling Improvements: Adding an interaction term, factorizing a variable, using a tree-based model to take care non-linear relationship. Describe / Explain … (how X is used). Definition of X Explain one way how X is used Discuss … Definition / Effects Evaluate the influence to the subject Examples: discuss the …
Principal Components and Cluster Analyses LEARNING OBJECTIVES The candidate will be able to apply cluster and principal components analysis to enhance supervised learning. The Candidate will be able to: Understand and apply K-means clustering. Understand and apply hierarchical clustering. Understand and apply principal component analysis. Chapter Overview As you can tell from its name, Exam PA is mostly concerned …
[mathjax] Extended Case Study: Classification Trees LEARNING OBJECTIVES The focus of this section is on constructing, evaluating, and interpreting base and ensemble trees. At the completion of this case study, you should be able to: Understand how decision trees form tree splits based on categorical predictors. Understand how decision trees deal with numeric predictors having a non-linear relationship with the …
[mathjax] Mini-Case Study: A Toy Decision Tree LEARNING OBJECTIVES In this section, we construct a toy decision tree on a small-scale dataset taken from a sample question of the Modern Actuarial Statistics II Exam of the Casualty Actuarial Society and displayed in Table 5.1. The small number of observations makes it possible for us to perform calculations by hand and …
[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 …
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
Purpose Extended formulas enhance and extend the capabilities of the Prophet programming language. They enable more complex calculations to be carried out than standard Prophet formulas. They are also able to retain the values that they have calculated from one model point to the next and from one loop to the next in a dynamic or stochastic run. Examples of …
Q_A_EXP IF ZERO_MORT = 1 AND AGE_AT_ENTRY < ZERO_TOL_AGE THEN 0 ELSE IF WL_POLICY = 1 AND t
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 …