Abstract
Transport risks in supply chains have increasingly lead to significant capital losses. Insurance claims against such losses have grown accordingly, while simultaneous advances in technology lead to continuously larger volumes of data recorded. Traditional risk evaluation methods in insurance struggle to account for rising supply chain complexity which is reflected by growing amount and dimensionality of supply chain data. Therefore decision-makers in the transport insurance industry need new ways of appropriate knowledge representation to support transport insurance providers with daily tasks such as premium tariffing. This paper presents a method based on multidimensional scaling (MDS) for the identification of groups of similar claims as a first step towards the improvement of supply chain risk evaluation and forecasting. We show the application potential of transforming and visualising transport damage claims data as the basis for developing decision support systems (DSS) to support transport insurance providers in tasks such as premium tariffing as well as transport and supply chain managers in risk mitigation and prevention activities.
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References
Balaji S, Srivatsa SK (2012) Decision Tree induction based classification for mining Life Insurance Data bases. Int J Comput Sci Inf Technol Secur (IJCSITS) 2:699–703
Breiman L (2001) Statistical modeling: the two cultures. Stat Sci 16:199–231
Broeksema B, Telea AC, Baudel T (2013) Visual analysis of multi-dimensional categorical data sets. Comput Graph Forum 32:158–169
Borg I, Groenen PJ (2005) Modern multidimensional scaling: theory and applications. Springer Science & Business Media
Boriah S, Chandola, V, Kumar V (2008) Similarity measures for categorical data: a comparative evaluation. red 30:3
Cerchiara RR, Edwards M, Gambini A (2008) Generalized linear models in life insurance: decrements and risk factor analysis under Solvency II. In: 18th international AFIR colloquium
Devale AB, Kulkarni RV (2012) Applications of data mining techniques in life insurance. Int J Data Min Knowl Manage Process (IJDKP) 2:31–40
Guelman L (2012) Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Syst Appl 39:3659–3667
Guo L (2003) Applying data mining techniques in property/casualty insurance. In: CAS 2003 winter forum, data management, quality, and technology call papers and ratemaking discussion papers, CAS
Hornik K, Feinerer I, Kober M (2015) skmeans: spherical k-Means clustering. R package version 0.2-7. https://CRAN.R-project.org/package=skmeans. Accessed 30 Oct 2015
Hong TP, Lin WY, Lee WY (2003) Adapting crossover and mutation rates in genetic algorithms. J Inf Sci Eng 19:889–903
IUMI—International Union of Marine Insurance (2014) Global marine insurance report
Joia P, Petronetto F, Nonato LG (2015) Uncovering representative groups in multidimensional projections. Comput Graph Forum 34:281–290
Keim D (2002) Information visualization and visual data mining. IEEE Trans Vis Comput Graph 8:1–8
Krishna K, Murty MN (1999) Genetic K-means algorithm. IEEE Trans Syst Man Cybern Part B: Cybernet 29:433–439
Maas P, Milanova V (2014) Zwischen Verheissung und Bedrohung – Big Data in der Versicherungswirtschaft. Die Volkswirtschaft 5:23–25
Roux I (2008) Application of cluster analysis and multidimensional scaling on medical schemes data. Doctoral dissertation, Stellenbosch University
Schaerer M, Wanner H, Grinyer CC (IBSG) (2011) Transforming the insurance industry to increase customer relevance. https://www.cisco.com/web/about/ac79/docs/fs/Insurance_Customer_Relevance_0301_IBSG.pdf. Accessed 30 Oct 2015
Schäfer I, Kaduszkiewicz H, Wagner HO, Schön G, Scherer M, van den Bussche H (2014) Reducing complexity: a visualisation of multimorbidity by combining disease clusters and triads. BMC Public Health 14:1285–1299
Skorna AC (2013) Empfehlungen für die Ausgestaltung eines Präventionskonzepts in der Transportversicherung. Doctoral Thesis, University St. Gallen
Skorna AC, Fleisch E (2012) Loss prevention in transportation to ensure product quality: insights from the cargo insurance sector. In: Advances in production management systems. Value networks: innovation, technologies, and management. Springer, Berlin, Heidelberg
Skorna AC (2013b) Empfehlungen für die Ausgestaltung eines Präventionskonzepts in der Transportversicherung. Verlag Versicherungswirtschaft GmbH, Karlsruhe
Thakur SS, Sing JK (2013) Mining customer’s data for vehicle insurance prediction system using k-means clustering-an application. Int J Comput Appl Eng Sci 3:148–153
Vilko JP, Hallikas JM (2012) Risk assessment in multimodal supply chains. Int J Prod Econ 140:586–595
Wickelmaier F (2003) An introduction to MDS. Sound Quality Research Unit, Aalborg University, Denmark
Yeo AC, Smith KA, Robert JW, Brooks M (2001) Clustering technique for risk classification and prediction of claim costs in the automobile insurance industry. Intell Syst Account Finan Manage 10:39–50
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Vican, V., Blindu, C., Fofonov, A., Ucinska, M., Bendul, J., Linsen, L. (2017). Application Potential of Multidimensional Scaling for the Design of DSS in Transport Insurance. In: Freitag, M., Kotzab, H., Pannek, J. (eds) Dynamics in Logistics. Lecture Notes in Logistics. Springer, Cham. https://doi.org/10.1007/978-3-319-45117-6_10
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DOI: https://doi.org/10.1007/978-3-319-45117-6_10
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