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
FUNCTION INSTR(BYVAL TEXT_DATA AS TEXT, BYVAL TEXT_FIND AS TEXT)AS INTEGER ; Match Sub-String and Return its Position I INTEGER J INTEGER FOR I FROM 1 TO LEN(TEXT_DATA) – LEN(TEXT_FIND) + 1 FOR J FROM 1 TO LEN(TEXT_FIND) IF SUBSTR(TEXT_FIND,J,1) <> SUBSTR(TEXT_DATA,I + J – …
Function Categories The Prophet programming language provides a wide range of functions which are grouped into the following categories: Array functions Conversion functions Dynamic functions Error / Warning functions Financial functions Logical functions Mathematical functions Product functions Read functions Run Position functions Run Progress functions Run Setting functions Statistical functions Text functions Trigonometric functions Array Functions Function Description ARRAY_INITIALISE Resizes …
Extended formulas 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 situations where extended formulas can be useful include: The calculation of commutation …
Overview The Prophet programming language is the programming language used to create and maintain formula definitions for variables in each product and library. A formula in Prophet can be specified using either: The standard Prophet language features or, The Prophet Extended Formula features. A formula created using the standard Prophet language generates values for one variable only. An extended formula …
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