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In finance, the LaPlace transform is used to calculate the distribution of stochastic present value. There are several practical impediments to the use of the LaPlace transform in actuarial science: we lack a physical interpretation of the transform, it requires a change in perspective to a frame of reference that we seldom use, and it involves complex arithmetic. This paper will define terms and provide interpretations and visualizations of the transform, the alternate frame of reference as well as the meaning of complex values in an actuarial setting. The dual purposes of this paper are to give actuaries a formula for computing the stochastic present value distribution and to attach a meaning to the LaPlace transform in an actuarial context.
This paper aims to demonstrate how deep learning (a subset of machine learning) can be used to forecast the ultimate losses of a sample group of Property and Casualty insurance companies. The paper initially explores the concept of loss development - how losses incurred by an insurance company mature across time. These losses then reach a final amount, known as the ultimate loss. The paper also looks at some already existing methods of forecasting the ultimate loss. The paper then introduces a novel method of forecasting losses, one which involves the use of deep learning neural networks. This new method uses Long Short-Term Memory (LSTM) - an advanced form of a deep learning architecture which specializes in finding patterns in temporal data. The findings of this method are then compared to a currently existing Python package which can also be used to predict ultimate losses. The paper also goes to critique some shortcomings of the model that is presented.
This paper presents an approach for combining (or unifying) triangle-based reserving methods. The approach I present expresses the combination of multiple triangle-based methods as a multivariate linear model. I intend this approach to provide a more flexible model with a statistical basis for underlying actuarial assumptions and the selection of the accident year point estimate after consideration of multiple methods.
Featuring Call Papers on COVID-19 and the P&C Insurance Industry and Four Independent Research Papers
Featuring Three Independent Research Papers
Featuring the winner of the 2021 CAS Reinsurance Call Paper Program
Featuring Five Reserves Call Papers and Four Independent Research Papers
Featuring Four Essays on Communications to Senior Management and Four Independent Research Papers
Featuring CAS Research Working Parties Reports
Featuring one CAS Research Working Party Report
Featuring the Non-Technical Reserving Call Papers
Featuring the CAS Research Working Party Report
Featuring a Report of the CAS Automated Vehicles Task Force and One Independent Research Paper
Featuring Ratemaking Call Papers, Climate Change Call Papers and three Independent Research Papers
Featuring the report of the CAS Working Party on Sustainable ERM (SERM)
Featuring two CAS-Sponsored Research Reports, Ratemaking Call Papers and Independent Research
Featuring Two Reinsurance Call Papers
Featuring one Independent Research Paper
Featuring Independent Research
Featuring the CAS Data & Technology Working Party Report and Independent Research
Featuring the CAS RBC Dependencies and Calibration Working Party Report
Featuring the CAS Reserves Call Papers, Innovation Essays and Independent Research
Featuring the CAS RBC Dependencies and Calibration Subcommittee Report
Featuring the Reinsurance Call Papers and Independent Research
