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

                                14. Other applications of the MPTF modelling framework

                                14.1 Credibility Modelling Compa using industry Maa951

                                Compa has high process variability.

                                In this case the final development and calendar trends are both zero statistically, after optimization. The future calendar period trend assumption is especially crucial in forecasting. We show how to credibility adjust these two trends which were rejected as statistically significant using Compa data (only). The credibility adjustments are based on industry data Maa951.

                                In the absence of collateral data it is prudent to use the insignificant calendar year trend for forecasting in the presence of high process variability.

                                A more systematic approach to this problem is to use credibility modeling - if collateral data are available.

                                In this case CompA represents Auto BI data from a single company representing about 3% of the market. The TG Maa951 represents the total industry and obviously exhibits much lower process variability.

                                The industry data have high unstable calendar year trends and a final negative development year trend, whereas Compa has insignificant (zero) calendar year trend and an insignificant (zero) final development period trend. You may recall that Compa, even though possessing high process variability, is stable in respect of trends. Removal of the last nine calendar periods gave very good predictions of the distributions of the 161 observations left out, and reserve distributions beyond the last calendar period are the same as using all the data!

                                When we run the two lines together as a composite in MPTF we find a correlation of around 0.25 between the datasets. The credibility model for CompA is then formed by the MPTF model which takes the correlations into account and is formed by freshly evaluating all the parameters on this basis.

                                After some exploration we find that the credibility model for CompA contains a positive calendar (inflation) trend, but does not support a negative final development trend. The forecast from this model is more conservative than the one based on following through on the insignificant trend.

                                14.2 MPTF Net of Reinsurance versus Gross. Is outward reinsurance optimal?

                                One application of ICRFS-Plus is the evaluation of outward reinsurance programs and optimal retention. In this video we study an example of Gross data versus Net of reinsurance data. Process correlation between the two segments is very high as expected, and the trend structure is almost identical.

                                This case study is viewable here (13 minutes).

                                14.3 Credibility modelling small arrays (Company X and Company Y)

                                In some circumstances we have a small array containing data with high volatility, but we also have a larger array of data for the same LOB which we believe represents a good approximation to the larger context of our original dataset. We want to use credibility modelling to extend our model and hence our forecasting ability beyond the limitations of the data. We explain how to place the data in a larger array filled out with zeros so that MPTF modelling is possible. This is carried out for illustrative purposes with real data called Company X and Company Y.