ICFS-Plus: Actuarial Software for the Property and Causality Insurance Industry

Videos marked with an (*) contain discussion of new content in ICRFS-Plus™ 12.


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™ 12 and modelling modules

    2. Modelling using the Link Ratio Techniques and Extended Link Ratio Family modelling framework

      3. Introduction to the Probabilistic Trend Family modelling framework

        4. Modelling real data (CTP) in the PTF modelling framework

          5. TG CS5: heteroscedasticity and varying parameters

            6. TG ABC: modelling wizard, simulations, and release of capital as profit

              7. Importing of data from other applications and COM Automation

                8. Further PTF Modelling Examples

                  9. Layers and the PALD Module

                    10. Introduction to MPTF

                      11. Clusters and MPTF Concepts

                        12. Capital Management of long tail liabilities

                          13. Solvency II one year risk horizon: SCR, Best Estimate of Liabilities (BEL), Technical Provisions (TP), and Market Value (Risk) Margins (MVM) for the aggregate of long-tail LOBs

                            14. Other applications of the MPTF modelling framework

                              15. The Bootstrap: how it shows the Mack method doesn't work

                                16. Updates from 10.6 to 11

                                16. Updates from 10.6 to 11

                                16.1 Database updates


                                In particular:

                                • The currency system variable is introduced.
                                • Descriptions for each database object within a triangle group is shown.
                                • Control over composite order is covered briefly.



                                16.2 PTF updates.


                                In particular:

                                • Future and reserving accident periods are now controlled in a single dropdown.
                                • Subsets of future calendar years can be selected.
                                • Multiple future calendar periods can be conditioned on for the purpose of calculating conditional statistics.



                                16.3 MPTF updates.


                                In particular:

                                • Datasets are displayed in list view. Previous tabbed view is available via the display preferences.
                                • Forecast and forecast combinations dialog boxes are combined.
                                • Control over currency is available when multiple currencies are present.
                                • PALD and Solvency II(*) can now be run on any aggregate in the forecast.


                                (*) Some additional restrictions apply to solvency II - for instance, at least one positive factor must exist in the selected aggregate.



                                16.4 ELRF bootstrap update


                                In particular:

                                • The bootstrap technique can be applied to any average link ratio (only) model in ELRF.
                                • Output is available similar to PALD including VaRs, T-VaRs, and distributions by accident year, calendar year (if not incurred data), and total.
                                • The bootstrap sample can be centered around the model mean or the sample mean.