### Course Description

This course will touch upon predictive analytics and modeling in life insurance – where it is used, the applications of predictive analytics and modeling in business. It also explains how to build a predictive model – data management, the types of predictive models, mortality models and other insurance applications. At the end, we will explain the results, ethics and legal limitations.

#### What am I going to get from this course?

- A deeper understanding of modeling and predictive analytics as well as the math behind it.
- Based on data and goals you want achieve you will be able to use the right method to achieve the goals.
- Get a good feeling about modeling, its challenges and how to overcome them.
- Become well versed in solving life insurance predictive modeling applications using either small or big data.

### Curriculum

#### Module 1: Predictive Analytics & Modeling in Life Insurance

About life insurance, what it is, it's history, applications, why it is useful and more. What are the benefits for insurance companies and customers.

Lecture 2
Brief History & Risk Factors

Brief history of predictive analytics in insurance and which risk factors are used by companies today to calculate the premiums. Example of online insurance and how they calculate it.

#### Module 2: Application of Predictive Analytics & Modeling in Business

Lecture 3
New Opportunities Using Big Data

What are the new opportunities which arived with advancements in this scientific field recently. This is especially connected with the development of big data - we can use data in many more ways and here we discuss which new fields have opened out with those new developments.

Lecture 4
Underwriting & Big Data

Underwritting has been the heart of the insurance business. Here we discuss its future and how this is changing with predictive modeling.

Lecture 5
Marketing & Big Data

Marketing is a big area where predictive analytics is already useful. Here we link marketing and marketing in life insurance as a special usecase for predictive analytics and modeling. We briefly describe the usecases.

Lecture 6
Other Applications

Insurtech startups, possibilities of utilizing big data for insurance.

How data is organized in storing and developing the data - as well as utilizing it.

#### Module 3: Building a Predictive Model

Lecture 8
What is predictive analytics

Lecture 9
Statistics vs. Predictive Analytics

Differences and similarities between the two sciences in how they work and what they do.

Lecture 10
General Guidance

The algorithms usually used in modeling and general guidance what to do once given a dataset. How the process works, what to focus on, etc.

Lecture 11
Data Operations

What are the data operations that modeler does once receiving the data.

Lecture 12
Structured & Unstructured Data

Types of data: structured and unstructured and how to recognize them - also how does certain type of data affect the modeling process.

Lecture 13
Static & Streamed Data

Types of data: static and streamed and how to recognize them - also how does certain type of data affect the modeling process.

Lecture 14
The Types of Variables

Types of variables we work with and the challenges that come with different kinds of variables.

Once we import the data, we must make sure that we can do models out of it; finding erroneus records, checking for different data issues and dealing with them.

Lecture 16
Reducing the Dimensionality

Fixing the dimensionality of the data; reducing the amount of variables and good practices.

Lecture 17
Outliers, Extreme Cases

All about dealing with outliers and extreme cases in data.

Lecture 18
Data Smoothing

Smoothing the data means creating a function that attemts to capture important patterns in the data.

Process of consturcting a curve or mathematical function that has the best fit to a series of data points.

Lecture 20
Explanatory Analysis

This is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. We explore it in this lecture.

A useful approach when modeling is the creation of dummies. Here we learn when to do this and how.

Lecture 22
Data Transformation

Ways how we can tranform the data.

Lecture 23
Splitting Train/Test Set

We can split data in several ways - this is mostly done to assure proper testing of the model. Splitting is described here with its good practices - besides train and test set, we also talk about validation set and what criteria to use for splitting.

Lecture 24
Data Sources & Apache Hadoop

About data sources and also a brief introduction to Apache Hadoop.

Lecture 25
General Guidelines & Tools to Use

Lecture 26
Task Definition

#### Module 4: Types of Predictive Methods

About forecasting. What it is, how we can forecast. What are the goals of forecasting?

Lecture 29
Measures of Aggregate Errors

We want to know how good our prediction is. This is why we have measures of aggregate errors. We examine several most widely used measures and why is any measure useful i.e. when to use it.

What is seasonality, few ways to tacle it, how to detect it.

Lecture 31
Seasonality Basics

Measuring seasonality, some examples of tackling seasonality and about time series modeling.

Autoregressive integrated moving average method for time series data understanding and predicting (forecasting). There are several ARIMA models and the way how they work is described here.

Lecture 33
ARCH & Other Models

Autoregressive conditional heteroskedasticity model and some other time series handling and forecasting models.

Lecture 34
Linear Regression

Linear regression - the basic forecasting model. What it is and the way how it is used.

Lecture 35
Simple Linear Regression Using a Single Predictor

Maths behind simple liner regression using a single predictor - how we estimate it, assesing errors, confidence intervals of predictors, etc.

Lecture 36
Stepwise Regression

Stepwise linear regression - what that is, why it's useful and exact steps of how it's done.

Lecture 37
General linear model

What it is where it's useful, how it is done.

Lecture 38
Multiple linear regression

Multivariate linear regression.

Lecture 39
Maximum Likelihood Estimation

Maximum likelihood estimation is a very popular method in many models, especially in insurance. Here we go through the basic idea and how it is done in practice. This is especially useful in generalized linear models.

As an example of probabilistic model we present logit model.

As another example of probabilistic model we present the probit model. Commands in Python and R for logit and probit are also presented here.

Lecture 42
Decision Trees Introduction

An introduction to decision trees and how they work. The whole idea behind them is discussed here.

Lecture 43
Random Forest Examples in Script, information gain, stopping criteria

Here we will have some examples in scripts of how random forest algorithm works. We also discuss some more advanced topics in random forest and decision trees - that is how we learn from data, information gain for tree models, gini impurity and stopping criteria - when to stop growing a tree/forest.

Lecture 44
Decision Tree Types, advantages and disadvantages of decision trees

Decision Tree Types, advantages and disadvantages of decision trees and about other possible algorithms in modeling.

Lecture 45
Exercise1 Instructions

In this exercise you will need scripting language to implement some of the models learned in this module.

Lecture 46
Exercise1 Solutions

Solutions given in R and Python. Together with narration.

In this exercise you will need scripting language to implement some of the models learned in this module.

Lecture 48
Exercise 2 solutions

Solutions given in R and Python. Together with narration.

#### Module 5: Mortality Models & Other Insurance Applications

Lecture 49
Mortality & Life Expectancy Models

About mortality and life expectancy modeling. We will also go through standard datasets that we can get - with some examples.

Lecture 50
Lee-Carter Model

Implementation and maths behind Lee-Carter model.

Lecture 51
The Poisson Log-Bilinear Model

Implementation and maths behind Poisson log-bilinear model.

Lecture 52
Claims, Blake, Dow M7 Model

Implementation and maths behind M7 model. This is one of the models that has been proposed more recently and is gaining traction.

Lecture 53
Advantages & Disadvantages

Advantages & Disadvantages of different models.

Lecture 54
Term Insurance Policy

Lecture 55
What is Retention

Lecture 56
Customer Lifetime Value

#### Module 6: Results, Ethics, & Legal Limitations

Lecture 57
Monitoring Results - Summary