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
5. Case Study CS5
5.1 CS5 and Heteroscedasticity
In this video, we introduce varying process variance (heteroscedascity) by development lag within the context of the PTF modelling framework. This is easiest to understand in terms of changes of percentage variability. Generally small payments vary more on a percentage scale than large payments.
We illustrate first using manual hetero analysis. In subsequent videos, we use automatic hetero adjustment.
The modelling of process variance is the fourth component of the PTF modelling analysis. Process variation is a common feature in all real data. This component of the model is just as critical as the changes in trends.
The video illustrating CS5 and Heteroscedascity (20 minutes).
5.2 CS5 and varying parameters
In this study, we examine varying parameters. This concept of varying parameters is close to the idea of exponential smoothing and credibility adjustment.
The model parameters window is discussed along with the relationship between this window and the model graphs (residual and model display).
Basically, for full parameters we give maximum weight to the new observations and zero weight to previous observations (they do not have any relevance to our new trend/level). With varying parameters, information from previous observations also is included when estimating the new 'level' thus we 'smooth' the changes between parameters.
The option reinstate all alpha parameters is also discussed.
The video illustrating the CS5 and varying parameters (20 minutes).
