Development of Key Risk Indicators for Risk Management Within Insurance

University essay from KTH/Matematisk statistik

Author: Olivia Boija; Louise Lindström; [2021]

Keywords: applied mathematics; Key Risk Indicator; KRI; regression modeling; sensitivity analysis; Monte Carlo simulation; expected loss; risk; tillämpad matematik; regressions modellering; känslighetsanalys; Monte Carlo simulering; förväntade förluster; risk;

Abstract: In this thesis a regression analysis of ten independent data sets is analysed in order toestimate losses and Key Risk Indicators (KRI). Each data set contains a list of objects,impacts that each object contains and revenue stream values (RSV) to each impact.The project investigates the data and simulate yearly losses as response variables in theregression modelling. The three regressors that influence the yearly losses are numberof objects, sum of revenue streams and expected aggregated losses. Given the responsevariable from each data set a percentage scale of KRI’s is determined indicating howlarge losses each set possess.

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