ICRFS™ Demonstration videos and powerpoint slide show

ICRFS™ is a tour de force of interactive software design and computational speed.

An ICRFS™ corporate database 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 modeling 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!

Rather than store data in spreadsheets, all actuarial related data are stored in ICRFS™ databases – including any models, forecast scenarios, and any other data related to actuarial requirements. The data can either be stored in Insureware’s proprietary format within the database (imported), or linked directly to claims data warehouses where structured tables in the ICRFS™ Open Data Architecture (IODA) format have been created allowing ICRFS™ to query the data warehouses directly and create loss development arrays on the fly.

ICRFS™ also includes metrics and calculations essential for IFRS 17 - including the calendar year liability stream, forecast scenario tracking, and distinguishing between earned premium and unearned premium.

Insureware's solution to the one year risk horizon, relevant for Solvency II Capital Requirements (SCR), Market Value Margins (Risk Margins) and Technical Provisions (Fair Value of Liabilities), for the aggregate of multiple LOBs, is covered in video chapter 5. Solvency II Capital requirements for each LOB and the aggregate of all LOBs are only met by ICRFS™ in a sound statistical framework.

View the videos below to experience the numerous unique benefits and applications afforded by the paradigm shift from link ratios to measuring trends. Some of the (real) case studies modelled in the videos are also discussed briefly in the ICRFS™ brochures. These videos are arranged in logical order so it is important that you view them that way.

For additional information on new functionality in ICRFS™ - click here.


1. Introduction to ICRFS™

1.1 General Introduction

1.1.1 Relational database and COM technology

The database functionality is demonstrated. Data, models and reports all reside in one relational database that can be configured in any way by the users. Communication between two databases has the same intuitive feel as using Windows Explorer for communicating between two sub-folders. Importation of triangles, from other applications including unit record transactional data, into an ICRFS™ relational database is effortless using the COM technology.

1.1.2 modeling, paradigm shift and benefits

ICRFS™ is the key to a new innovative paradigm in for measuring and managing long (and short) tail liability risks. The various modules (modeling frameworks) LRT, ELRF, PTF and MPTF of the system are introduced. The critical difference between variability and uncertainty is illustrated with two gambling examples. The numerous benefits and applications afforded by the unique paradigm shift are also described.

1.2 The LRT and ELRF modeling frameworks

1.2.1 The Link Ratio Techniques (LRT) modeling framework

The Link Ratio Techniques (LRT) module is described using the incurred (Mack) array. It can be applied to any incremental or cumulative array. There are numerous choices in selecting and smoothing link ratios (age to age development factors) to any ultimate period. Selected link ratios (methods) can be saved and run at any time to obtain forecasts including Bornheutter Ferguson. The context sensitive help (topic help) is also shown. Demonstration video 5.2 illustrates the flexibility of report templates including post-processing of ICRFS™ generated tables.

1.2.2 The Extended Link Ratio Family (ELRF) modeling framework

Weighted average link ratio methods can be regarded as average trends (slopes through the origin), and accordingly are formulated as regression estimators in the ELRF module. The default starting method in the ELRF modeling framework is the so-called Mack Method which is the regression equivalent formulation of volume-weighted-average link ratios. These regression methods (through the origin) are extended to include intercepts, and constant trends across the accident years in the incrementals for each development period. A number of diagnostic tests are provided for identifying the best model in this family. Forecast standard errors are also given.

Three mutually exclusive conditions satisfied by data are introduced. For all but one development period the Mack incurred data satisfy condition 1. Even where the development period satisfies condition 2, link ratios do not have any predictive power (for this incurred array). This study is also discussed in the paper "Best Estimates for Reserves".

Also see demonstration videos 9.1-9.5 to see how the bootstrap can be used to test whether the Mack (and related) methods work for a dataset.

1.3 The Probabilistic Trend Family (PTF) modeling framework

The Probabilistic Trend Family (modeling framework) is introduced in this session. Axiomatic trend properties of trends satisfied by every triangle are explained using a simple example. Subsequent to adding process variability to the trend structure, the data are modeled in order to explain a number of statistical concepts. A model is represented by four pictures (graphs); each picture has a simple interpretation. Any three out of the four pictures are meaningless (without the fourth). This study is also discussed in the paper "Best Estimates for Reserves".

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