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
File Management CopyFolder Function: CopyFolder (Source As String, Target As String) As Integer Copies contents of a source folder including subdirectories to the target folder. If target folder exists then it will be deleted before copy operation is performed. Returns True(-1) if successful or False(0) if failed. Example: DeleteFolder Function: DeleteFolder (FolderName As String) As Integer Deletes a directory with …
Types of Tables Tables are used at many levels in AXIS for many different: uses, structures, and shapes Table Hierarchy Characteristics Cell Tables Used only by cells, or by sub-objects linked to cells Support policy level calculations at the cell level Projection Tables Used by higher level objects, such as sub-funds, funds and offices Column-specific shape Rows are financial years …
Background There are times you are interested in summing up the results across various dimensions with specified conditions or indicators to exclude some results from the ARRAY_SUM function. There is an incident that I need to separate medical health UCOI cash flows from total medical UCOI cash flows, because there are different loss ratios for these two types of UCOI …
What is calculation looping? Calculation looping is a process whereby parts of the calculation are repeated several times. In many ways it is similar to the rebasing facility. However, calculation looping differs from rebasing in the following ways: With calculation looping the parts that are repeated are repeated for the whole of the period of calculation. With rebasing only the …
Fundamental Concepts Notes Goal-seeking cannot be used in conjunction with looped modules. If you wish to goal seek at a product or fund level, you should use the REPEAT_LEVEL function. Variables used in goal-seeking Variable Description SEED_VAL Seed value for first iteration. PREV_VAL Value for current iteration. NEXT_VAL Value for next iteration. IF_CONVERGED If iteration has converged. How …
Dates CODE_SEGMENT Dates ; Valuation Date IF LEN(STRVAL(MONTH(EXTRACT_DATE))) = 1 THEN VAL_DATE = STRVAL(YEAR(EXTRACT_DATE)) + “0” + STRVAL(MONTH(EXTRACT_DATE)) ELSE VAL_DATE = STRVAL(YEAR(EXTRACT_DATE)) + STRVAL(MONTH(EXTRACT_DATE)) ENDIF ; Last-Year End LYE_DATE = STRVAL(YEAR(EXTRACT_DATE) – 1) + “12” ; Beginning of Current Year BOY_DATE = STRVAL(YEAR(EXTRACT_DATE)) + “01” ; Last-Month LM_M = STRVAL(NUMVAL(SUBSTR(VAL_DATE, 5, 2)) – 1) IF LEN(LM_M) = 1 THEN LM_M …