workshop-loss-reserv-fraud

KO | EN

gitlite — search

#python #java #python3 #arduino #golang #machine-learning #rust #html #flask #javascript #seismology #nodejs

workshop-loss-reserv-fraud

★ 10 Open GitHub ↗

Course material for a workshop on loss modelling, reserving and insurance fraud analytics

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

## Workshop Analytics for Loss Modelling, Reserving and Insurance Fraud

by Katrien Antonio and Jonas Crevecoeur

Course materials for the online *Loss Modelling, Reserving and Insurance
Fraud Analytics* course in June 2021.

📆 June 3, 10 & 17 2021 ⏰ From 3pm to 6pm 📌 online via MS Teams

Course materials will be posted in the days before the workshop.

## Overview

This three-half-day workshop introduces the *essential concepts of
building insurance loss models with R*.

On the **first half-day**, you will gain insights in the foundations of
handling **loss data**, including useful *data wrangling and
visualization steps*. You will cover a variety of *discrete and
continuous loss distributions*, and techniques to build more flexible
distributions from standard distributions (by mixing and splicing). You
will learn how to fit these models to actual data and inspect their
goodness-of-fit. Then, you will use the fitted model to estimate risk
measures.

The **second half-day** then puts focus on **reserving** analytics.
Starting from a granular data set with the development of individual
claims, you will learn how to get useful insights from the data.
Step-by-step you will move from the granular data to aggregated data in
a run-off triangle. You will fit the famous chain ladder method and
examine its validity on the given data. Alternative modelling strategies
based on recent research will be discussed.

The **third half-day** then covers challenges in building **fraud**
detection models from tabular and networked data. You will acquire
insights in the foundations of these analytic methods, learn how to
set-up the model building process, and focus on building a good
understanding of the resulting model output and predictions.

Leaving this workshop, you should have a firm grasp of the working
principles of a variety of loss models for frequency and severity data
and be able to explore their use in practical settings. Moreover, you
should have acquired the fundamental insights to explore some other
methods on your own.

## Schedule and Course Material

The detailed schedule is subject to small changes.

| Session | Duration      | Description                                                        | Lecture material                                                                                                                                                                                                                                                                               |
| :-----: | ------------- | ------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|  Day 0  | your own pace | Prework                                                            | sheets prework in [html](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/prework/prework_day_0.html) and in [pdf](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/prework/prework_day_0.pdf)                                                             |
|  Day 1  | 3 - 3.15      | Prologue                                                           | sheets in [html](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day1/loss_modelling_reserving_analytics_day_1.html#prologue) and in [pdf](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day1/day_1.pdf)                                               |
|         | 3.15 - 3.50   | Data sets                                                          | sheets in [html](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day1/loss_modelling_reserving_analytics_day_1.html#data-sets)                                                                                                                                              |
|         | 4 - 4.50      | Frequency models                                                   | sheets in [html](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day1/loss_modelling_reserving_analytics_day_1.html#frequency)                                                                                                                                              |
|         | 5 - 6         | Severity models                                                    | sheets in [html](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day1/loss_modelling_reserving_analytics_day_1.html#severity)                                                                                                                                               |
|  Day 2  | 3 - 3.20      | Motivation and strategies                                          | sheets in [html](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day2/loss_modelling_reserving_analytics_day_2.html#motivation) and [pdf](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day2/loss_modelling_reserving_analytics_day_2.pdf)             |
|         | 3.20 - 3.50   | Reserving data structures (part 1): daily and yearly data          | sheets in [html](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day2/loss_modelling_reserving_analytics_day_2.html#horizontal_aggregation) and [pdf](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day2/loss_modelling_reserving_analytics_day_2.pdf) |
|         | 4 - 4.15      | Reserving data structures (part 2): individual and aggregated data | sheets in [html](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day2/loss_modelling_reserving_analytics_day_2.html#vertical_aggregation) and [pdf](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day2/loss_modelling_reserving_analytics_day_2.pdf)   |
|         | 4.15 - 4.50   | Claims reserving with triangles                                    | sheets in [html](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day2/loss_modelling_reserving_analytics_day_2.html#chainladder) and [pdf](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day2/loss_modelling_reserving_analytics_day_2.pdf)            |
|         | 5 - 5.45      | When the chain ladder method fails                                 | sheets in [html](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day2/loss_modelling_reserving_analytics_day_2.html#chainladderfail) and [pdf](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day2/loss_modelling_reserving_analytics_day_2.pdf)        |
|         | 5.45 - 6      | Research outlook                                                   | sheets in [html](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day2/loss_modelling_reserving_analytics_day_2.html#research_outlook) and [pdf](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day2/loss_modelling_reserving_analytics_day_2.pdf)       |
|  Day 3  | 3 - 3.30      | Motivation and fraud detection cycle                               | sheets in [pdf](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day3/day_3_fraud_analytics_print.pdf)                                                                                                                                                                       |
|         | 3.30 - 3.50   | Network and tabular data                                           |                                                                                                                                                                                                                                                                                                |
|         | 4 - 4.30      | Ranking algorithm                                                  |                                                                                                                                                                                                                                                                                                |
|         | 4.30 - 4.50   | Featurization                                                      |                                                                                                                                                                                                                                                                                                |
|         | 5 - 5.30      | Supervised learning with imbalanced data                           |                                                                                                                                                                                                                                                                                                |
|         | 5.30 - 5.50   | Evaluation metrics                                                 |                                                                                                                                                                                                                                                                                                |
|         | 5.50 - 6      | Wrap-up                                                            |                                                                                                                                                                                                                                                                                                |

##### Half-day 1: Loss modelling analytics

Topics include:

- data sets used in the course: MTPL and SecuraRe losses
  - data handling and visualization tools with {ggplot} and {dplyr}
  - building frequency models: Poisson, Negative Binomial, ZI and
    Hurdle, maximum likelihood estimation and goodness-of-fit
  - building severity models: simple to complex parametric
    distributions, splicing to construct a global fit, mixing, estimate
    a risk measure.

Download lecture sheets in
[html](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day1/loss_modelling_reserving_analytics_day_1.html)
and
[pdf](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day1/day_1.pdf).

##### Half-day 2: Claims reserving analytics

Topics include:

- data sets used in the course: individual claims, daily data
  - data handling and visualization tools with {ggplot} and {dplyr}
  - from daily to yearly data, from individual claims to triangles
  - fitting well known claims reserving methods
  - when the simple methods fail: what else is there?

Download lecture sheets in
[html](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day2/loss_modelling_reserving_analytics_day_2.html)
and
[pdf](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day2/loss_modelling_reserving_analytics_day_2.pdf).

##### Half-day 3: Insurance fraud analytics

Topics include:

- insurance fraud detection strategies and numbers
  - network and tabular data
  - ranking algorithms, e.g. PageRank, BiRank
  - network featurization steps
  - supervised learning with imbalanced data, sampling methods like
    SMOTE
  - evaluation metrics, eg AUROC, AUPR, top decile lift
  - wrap up.

Download lecture sheets
[pdf](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/day3/day_3_fraud_analytics_print.pdf).

## Prework

The workshop requires a basic understanding of R. We prepared some
prework sheets that you can take a look at before the workshop
([html](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/prework/prework_day_0.html)
or
[pdf](https://katrienantonio.github.io/workshop-loss-reserv-fraud/sheets/prework/prework_day_0.pdf)).

Being familiar with statistical or machine learning methods is *not*
required. The workshop gradually builds up these concepts, with an
emphasis on hands-on demonstrations and exercises.

## Software Requirements

You have **two options** to join the coding exercises covered during the
workshop. Either you join the RStudio cloud workspace dedicated to the
workshop, and then you’ll run R in the cloud, from your browser. Or you
use your local installation of R and RStudio.

We kindly ask participants to **join the RStudio Cloud as default**\!

### RStudio Cloud - default\!

You will join our workspace on R Studio Cloud. This enables a **very
accessible set-up** for working with R in the cloud for the less
experienced user\!

<https://rstudio.cloud/spaces/147546/join?access_code=YgLiHaQpPvZdcbh943QMO%2FqtF%2FKTct855m5tYtmH>

Here are the steps you should take (before the workshop):

- visit the above link
  - log in by creating an account for RStudio Cloud or by using your
    Google or GitHub login credentials
  - join the space
  - at the top of your screen you see ‘Projects’, click ‘Projects’
  - with the ‘copy’ button (on the right) you can make your own version
    of the ‘day 1 on loss modelling’ project; in this copy you can work
    on the exercises, add comments etc.
  - you should now be able to visit the project and see the ‘scripts’
    and ‘data’ folders on the right. Open and run the
    ‘installation-instructions.R’ script from the scripts folder, to
    see if everything works fine.

We will have everything set up for you in the correct way. You only have
to login\!

### Local installation - optional (make sure you have access to the RStudio Cloud as sketched above)\!

Alternatively, you can bring a laptop with a recent version of R and
RStudio installed. Make sure you can connect your laptop to the internet
(or download the course material one day before the start of the
workshop). You will need:

- R (at least 3.5.2 <https://cloud.r-project.org/bin/windows/base/>)
 - RStudio
 (<https://www.rstudio.com/products/rstudio/download/#download>)

In the `prework` folder you will find a step-by-step guide to installing
R and RStudio (though a bit outdated).

Please run the following script in your R session to install the
required packages

``` r
packages <- c("tidyverse", "here", "gridExtra", "caret", "rsample", "recipes", "grid", "rstudioapi", "MASS", "actuar", "statmod", "ReIns", "pscl", "zoo", "igraph", "expm", "pROC", "ChainLadder", "lubridate")
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)

if(sum(!(packages %in% installed.packages()[, "Package"]))) {
  stop(paste('The following required packages are not installed:\n', 
             paste(packages[which(!(packages %in% installed.packages()[, "Package"]))], collapse = ', ')));
} else {
  message("Everything is set up correctly. You are ready to go.")
}
```

## Instructors

[Katrien Antonio](https://katrienantonio.github.io/) is professor in
insurance data science at KU Leuven and associate professor at
University of Amsterdam. She teaches courses on data science for
insurance, life and non-life insurance mathematics and loss models.
Research-wise Katrien puts focus on pricing, reserving and fraud
analytics, as well as mortality dynamics.

*Jonas Crevecoeur* is a post-doctoral researcher in biostatistics at KU
Leuven. He recently obtained his PhD within the insurance research group
at KU Leuven and holds the degrees of MSc in Mathematics, MSc in
Insurance Studies and MSc in Financial and Actuarial Engineering (KU
Leuven). Before starting the PhD program he worked as an intern with QBE
Re (Belgium office) where he studied multiline products and copulas.
Jonas was a PhD fellow of the Research Foundation - Flanders (FWO, PhD
fellowship fundamental research).

Happy learning\!

-----

workshop-loss-reserv-fraud

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README