Risks · 2018 · 60 citations · 13 references
BusinessInsuranceActuarial ScienceMachine LearningStochastic ModelingActuarial AnalyticsClaims AnalyticsClaims ModelingSimulation MethodsSimulation ModelingSimulation-based InferenceConvolutional Neural NetworksMachine Learning For InsurancePredictive Claims ModelingSimulation-based Claims ModelingStochastic Claims ReservingStochastic Scenario AnalysisStochastic SimulationStochastic Simulation Algorithms
The aim of this project is to develop a stochastic simulation machine that generates individual claims histories of non-life insurance claims. This simulation machine is based on neural networks to incorporate individual claims feature information. We provide a fully calibrated stochastic scenario generator that is based on real non-life insurance data. This stochastic simulation machine allows everyone to simulate their own synthetic insurance portfolio of individual claims histories and back-test thier preferred claims reserving method.
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Learning representations by back-propagating errors
David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams · Nature · 1986 · 30.9K citations
Multilayer feedforward networks are universal approximators
Kurt Hornik, Maxwell B. Stinchcombe, Halbert White · Neural Networks · 1989 · 21.2K citations
Multilayer feedforward networks are universal approximators
HornikK., StinchcombeM., WhiteH. · Neural Networks · 1989 · 9.4K citations