@misc{Fojtík_Jan_Alternative_2017,
 author={Fojtík, Jan and Procházka, Jiří and Zimmermann, Pavel and Macková, Simona and Švehláková, Markéta},
 identifier={DOI: 10.15611/amse.2017.20.11},
 year={2017},
 rights={Wszystkie prawa zastrzeżone (Copyright)},
 publisher={Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu},
 description={20-th AMSE. Applications of Mathematics and Statistics in Economics. International Scientific Conference: Szklarska Poręba, 30 August- 3 September 2017. Conference Proceedings Full Text Papers, s. 145-154},
 language={eng},
 abstract={Estimation of future liabilities is one of the essential actuarial tasks. With the huge client portfolios nowadays, not only the accuracy of liability estimates is of great importance but also the time within which the results are calculated. Especially in the case of estimates of life insurance liability, the computing time can be very high, because the estimation is based on a projection of future cash flow of each contract separately. Therefore, methods to reduce computation time while as do not significantly decrease accuracy are welcomed by many actuaries. Cluster analysis can be applied for this purpose. Basic idea is to split contracts into clusters and represent all contracts within the cluster by a specific contract, so called model point. Projection is only calculated for these model points and weights are assigned to reflect the number of contracts of the cluster. The main contribution of this paper consists in the analysis of clustering variables in the case of approximate life insurance liability model},
 title={Alternative Approach for Fast Estimation of Life Insurance Liabilities},
 type={materiały konferencyjne},
 keywords={life insurance, estimation of BEL, cluster analysis},
}