ICRFS-Plus™ Demonstration videos and powerpoint slide show
ICRFS-Plus™ is a tour de force of interactive software design and computational speed.
An ICRFS-Plus™ corporate database, which is not difficult to create, enables complete executive oversight. This means that that you will be able to find, with just a few mouse clicks, models and reports for any segment of your business in any country, the actuary modelling that segment of the business, capital allocation by LOB and calendar year, reserve risk charge and underwriting risk charge for the aggregate of LOBs, whether outward reinsurance is effective in respect of reducing retained risk, and more. Creation of an ICRFS-Plus™ database from triangles stored elsewhere or unit record transactional data is also seamless and effortless using COM scripts.
One great benefit of ICRFS-Plus™ is that you can manage and measure all your long tail liability risks with a single composite model. Only one model for each company!
Click here to view an powerpoint presentation overview (about 14Mb) of ICRFS-Plus™
A single composite model measures the reserve, underwriting and combined risks for each LOB and the aggregate.
One double click loads the model and reveals pictorially the volatility structure of each long tail LOB in your company and their inter-relationships (correlation structures). All the critical financial information such as risk capital allocation by LOB and calendar year, and Tail Value-at-Risk for different time horizons can be computed in a matter of seconds. A company-wide report can be created effortlessly with a single report template.
In respect of Solvency II Capital Requirements (SCR), Market Value Margins (Risk Margins) and Technical Provisions (Fair Value of Liabilities), for the aggregate of multiple LOBs, video chapter 5 provides Insureware's solution to the one year risk horizon.
View the videos below to experience the numerous unique benefits and applications afforded by a unique paradigm shift.
Some of the (real) case studies modelled in the videos are also discussed briefly in the ICRFS-Plus™ brochure.
These videos are arranged in logical order so it is important that you view them that way.
If for any reason you are unable to view the training or demonstration videos, please contact our support staff at firstname.lastname@example.org 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.
- 3.1 Introduction to MPTF, two Lines of Business: LOB1 & LOB3 and two types of correlations
- 3.2 Clusters: LOB A - J and SDFx40
- 3.3 Layers: 1M, 2M, 1Mxs1M and 0-25, 25-50,...
- 3.4 3.4 Gross versus Net of Reinsurance - same trend structure with high process (volatility) correlation
- 3.5 Credibility Modelling: CompA, Maa951 and Company A, Company B
3. The Multiple Probabilistic Trend Family (MPTF) Modelling Framework
The MPTF module of ICRFS-Plus™ is used to design a composite model for multiple lines of business, multiple segments and multiple layers. It has many benefits including assessment of level of diversification, design of optimal outward reinsurance and credibility modelling.
3.1 Introduction to MPTF, two Lines of Business: LOB1 & LOB3 and two types of correlations
In this video, the MPTF modelling framework is introduced. This modelling framework has diverse applications including: modelling multiple lines of business, capital allocation by Line of Business based on a covariance formula, pricing layers including excess layers, credibility modelling, and producing a company wide report.
Correlation, linearity, linear regression, weighted least squares and normality are intimately related concepts. Two lines of business LOB 1, LOB 3 are first studied in the PTF modelling Framework. The optimal PTF models are run in the MPTF modelling framework and optimised. Two types of correlations, namely, process correlations and parameter estimates correlations are estimated from the data and are an integral part of the optimal model. These correlations induce correlations between any two pair of cells between the two lines of business and hence correlations between all aggregate forecast distributions between the two lines of business. The correlation for the total reserve distributions between the two lines is very high! Level of diversification is assessed and is compared. Capital allocation by LOB is also compared.
3.2 Clusters: LOB A - J and SDFx40
Two examples are presented to show the use of clusters. In particular, LOB A B C D E F G H I J (10 lines of business) and SDFx40 (40 lines of business). Analysis in MPTF is limited only by the computer hardware and processing time - the larger the number of lines or segments, the more space and time is needed for processing.
Methods of creating clusters are discussed including manual creation of clusters - in this case via modification of existing clusters. The new optimisation method of optimising only within clusters is also utilised.
3.3 Layers: 1M, 2M, 1Mxs1M and 0-25, 25-50,...
For the three layers limited $1M, $1Mxs$1M, limited $2M a composite model is designed in MPTF. The optimal composite model contains very high process correlations and parameter estimates are also highly correlated. The model provides consistent estimates of reserve distributions for the layer limited $2M and the aggregate of the two layers limited $1M and $1Mxs$1M. Perhaps this is not surprising as the layers are additive. It is also found that outward reinsurance where each individual loss limited is to $1M ($1,000,000) is not more capital efficient than outward reinsurance where each individual loss is limited $2M ($2,000,000).
For a layered composite data set 0-25K, 25K-50K,…, 150K-200K, it is also found that the coefficient of variation of the net reserves limited to $xK does not depend on x!
Highest process correlations are between neighbouring layers. The development period peak shifts to the right as you move to higher layers. Calendar year trends are statistically the same for neighbouring layers and any change in trends occur in the same periods for all layers.
3.4 Gross versus Net of Reinsurance - same trend structure with high process (volatility) correlation
For another study involving gross data versus data net of reinsurance, the retrospective outward reinsurance program (that has been in place for many years) is far from optimal from the point of view of the cedant. Gross data and net of reinsurance data have both high process correlation and parameter estimates correlations.
3.5 Credibility Modelling: CompA, Maa951 and Company A, Company B
CompA (modeled in Video 2.1) contains paid losses for a company writing relatively low exposures. The paid losses are extremely volatile, yet calendar year trend is stable. As a result of high process variability the estimate of the calendar year trend is not significant, equivalently, it is not credible. The triangle group Maa951 contains industry data for the same line of business. It has very little process variability and much higher (and unstable) calendar year trends than Compa. The Compa component of the composite (credibility) model has a significant (credible) calendar year trend. Some of the development period trends are also credibility adjusted, but not all of them. Which trends are credibility adjusted depends on the information in each triangle and their inter-relationships! The industry and the company experiences are very different, both in terms of trends and process variability.
In a study involving two companies, Company B is a triangle that has very few accident years. Company A is the same line of business but has larger dimensions. The "information" in Company A is used to design a credibility model for Company B. Calendar year and development year trends are credibility adjusted. Process variability that is intrinsic to the company is never adjusted.
For additional information on ICRFS-Plus™ features - click here.
Solvency II Capital requirements for each LOB and the aggregate of all LOBs are only met by ICRFS-Plus™ in a sound statistical framework.