ELRF Training Videos

ELRF™ Training videos

Videos marked with an (*) contain discussion of new content in the latest ELRF™ release.


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The training videos should be used for hands on training. We suggest you run the videos on a separate computer using a data projector, and train as a group.

The only way you will learn all the new concepts and be able to exploit all the immense benefits is by using the system. Experiential learning is imperative.

It is important that you study the videos in sequential order as set out below.

Table of Contents

1.0 Introduction

1. Introduction to ELRF™

1.1 Introduction to ELRF™ database structures


The database functionality and navigation is studied. Data, models and links to reports all reside in one relational database. A database can either be remote (on a server that is shareable) or local. Communication between two databases has the same intuitive feel as using Windows Explorer for communicating between two sub-folders. The objects in the database are Triangle Groups (TG) and Composite Triangle Groups (CTG). TGs (and CTGs) also contain objects, namely, triangles, exposures, premiums, data sets, models and links to reports.


This video demonstrates:

  • database manipulation
  • database structure
  • triangle group structure
  • using variables and values to filter triangle groups
  • system navigation
  • creation of new databases and communication between two databases
  • new triangle types


1.2 Overview of ELRF™ modeling frameworks


In this video, a brief overview of the modeling frameworks included in ICRFS™ is presented.


These frameworks include:

  • Link Ratio Techniques (LRT) including Bornhuetter-Ferguson
  • Extended Link Ratio Family (ELRF) as discussed in the paper "Best Estimate for Reserves"
  • Probabilistic Trend Family (PTF) modeling framework
  • Multiple Probabilistic Trend Family (MPTF) modeling framework

There is a paradigm shift between link ratio techniques (LRT) and the probabilistic modeling frameworks PTF and MPTF. The ELRF modeling framework provides the bridge between the two frameworks.


An identified model in the PTF modeling framework gives a succinct description of the volatility in the data. The description of the volatility is represented by four graphs, which tell a story about the data.


Benefits of the ICRFS™ software package include:

  • A user configured, easily navigated database
  • The database is a repository for the data, models, and forecast scenarios
  • Uneven sampling periods: for example, Accident year reserves versus quarterly evaluations.
  • Models are saved in the triangle groups
  • Monitoring and updating every review period is a seamless operation
  • Diagnostics for existing link ratio methods
  • Pricing both retrospective and prospective reinsurance structures
  • Pricing for different limits for different years
  • Future accident (underwriting) period segmentation pricing
  • Understandable probabilistic models summarised by four interrelated pictures.
  • Correlations (all three types: process, parameter, and reserve) and trends are measured from the data.
  • Economic Capital: risk charges for combined reserve and underwriting risks. (Note there is usually additional diversification credit obtained for the combined risk charge on reserves and underwriting).
  • modeling wizard
  • Reinsurance evaluation

modeling multiple triangles simultaneously in the MPTF module has additional applications and benefits including risk diversification analysis, capital allocation analysis, credibility modeling, and many other applications as seen in subsequent chapters.



1.3 Uncertainty and Variability


Variability and uncertainty are two distinct concepts and cannot be used interchangeably. Variability is an observed phenomenon that is to be measured and where appropriate explained. Uncertainty, on the other hand, refers to knowledge about variability.


This is easiest to explain by way of example. We demonstrate by comparing two games of chance where the parameters of the games are known. In this case, we have no uncertainty in our knowledge about either game. We 'know' the mean, standard deviation, and indeed the probabilities of all the outcomes.


Parameter uncertainty leads to uncertainty in the variability of the process- our knowledge about the variability is uncertain. The inherent variability (process variance) cannot be reduced.



1.4 Manual creation of Triangle Groups


In this video, manual creation of a triangle group is illustrated. Although triangle groups are usually created via an importing macro, it can be useful to create triangle groups and triangle manually for small projects.


Creating triangle types and triangles are also demonstrated along with transferring data and models between similarly sized triangle groups.


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