ICRFS-Plus™ Training videos for new users
If for any reason you are unable to view the training or demonstration videos, please contact our support staff at support@insureware.com and we will arrange to send you a copy of the videos on CD-Rom. You will be able to run the videos from the CD.
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. Introduction to ICRFS-Plus™ 10.2 and modelling modules
2. Modelling using the Link Ratio Techniques and Extended Link Ratio Family modules
3. Introduction to the Probabilistic Trend Family modelling framework
4. Modelling real data (CTP) in the PTF modelling framework
8. Further PTF Modelling Examples
10. Uneven Sampling periods, updating, and various other topics
9. Layers and the PALD Module
In this video we are studying layers and the predictive aggregate loss distribution (PALD) module.
The Triangle Groups: All 1M, All 1Mxs1M and All 2M are three layers. The structure of the models is very similar in all three layers. Note that the calendar trend in the intermediate layer: 1Mxs1M is not significant.
An identified PTF model predicts log normal distributions for each cell and their correlations. There is no analytical distribution of the sum of log normals. In order to determine the distribution of an aggregate of log normals we need to simulate from each log normal including their correlations. The PALD module provides the facility to conduct simulations in order to obtain distributions of aggregates for accident periods, calendar years and the total reserve. This output can then be used to compute percentiles and VaR tables.
The Reinsurance module is also covered. This module allows the evaluation of varying attachment points with the resultant expected payouts for the Insurer or Reinsurer (compared to no cover).
The output tables from these PALD and Reinsurance modules are explained.
The PALD results are run for the reserve distribution as well as for future pricing years.
It is important to include parameter uncertainty in the forecasting scenarios. Parameters with the same mean but different standard deviations are not the same forecast scenario.
The video demonstrating the PALD and Re modules is 35 minutes long.
