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Machine learning in individual claims reserving
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Machine learning in individual claims reserving (2018)Machine learning in individual claims res…Micro-level stochastic loss reserving for general insurance (2013)Micro-level stochastic loss r…Distribution-free Calculation of the Standard Error of Chain Ladder Reserve Estimates (1993)Distribution-free Calculation…Recent Challenges in Actuarial Science (2021)Recent Challenges in Actuaria…Mind the Gap - Safely Incorporating Deep Learning Models into the Actuarial Toolkit (2021)Mind the Gap - Safely Incorpo…Machine Learning in P&C Insurance: A Review for Pricing and Reserving (2020)Machine Learning in P&C Insur…Individual Loss Reserving Using a Gradient Boosting-Based Approach (2019)Individual Loss Reserving Usi…DeepTriangle: A Deep Learning Approach to Loss Reserving (2019)DeepTriangle: A Deep Learning…A machine learning approach for individual claims reserving in insurance (2019)A machine learning approach f…Neural networks applied to chain–ladder reserving (2018)Neural networks applied to ch…An Individual Claims History Simulation Machine (2018)An Individual Claims History …AI in Actuarial Science (2018)AI in Actuarial ScienceData Analytics for Non-Life Insurance Pricing (2017)Data Analytics for Non-Life I…Stochastic Claims Reserving in General Insurance (2002)Stochastic claims reserving methods in insurance (2008)Modern Actuarial Risk Theory: Using R (2010)INDIVIDUAL LOSS RESERVING WITH THE MULTIVARIATE SKEW NORMAL FRAMEWORK (2013)Modelling censored losses using splicing: A global fit strategy with mixed Erlang and extreme value distributions (2017)System Modeling and Optimization (1982)Non-Life Insurance: Mathematics & Statistics (2013)A NEURAL NETWORK BOOSTED DOUBLE OVERDISPERSED POISSON CLAIMS RESERVING MODEL (2019)A neural network extension of the Lee–Carter model to multiple populations (2019)The sixth generation of knowledge management – the headway of artificial intelligence (2021)Modeling and Forecasting U. S. Mortality (1992)A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)To Explain or to Predict? (2010)Neural Network Embedding of the Over-Dispersed Poisson Reserving Model (2018)NEIGHBOURING PREDICTION FOR MORTALITY (2021)Prediction of Outstanding Liabilities in Non-Life Insurance (1993)Prediction of Outstanding Liabilities II. Model Variations and Extensions (1999)Gradient boosting trees for auto insurance loss cost modeling and prediction (2011)Predictive Modeling Applications in Actuarial Science (2014)Personalization as a promise: Can Big Data change the practice of insurance? (2020)Transformation of insurance technologies in the context of a pandemic (2021)17. A Value for n-Person Games (1953)Strictly Proper Scoring Rules, Prediction, and Estimation (2007)Claims Frequency Modeling Using Telematics Car Driving Data (2018)Bias regularization in neural network models for general insurance pricing (2019)Time Series Analysis: Forecasting and Control (1977)Modelling Extremal Events (1997)Value At Risk: The New Benchmark for Managing Financial Risk (2000)Analytic and bootstrap estimates of prediction errors in claims reserving (1999)Machine Learning for Financial Risk Management: A Survey (2020)
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References

  • 1993Distribution-free Calculation of the Standard Error of Chain Ladder Reserve Estimates551 cited
  • 2013Micro-level stochastic loss reserving for general insurance148 cited

Cited by

  • 2017Data Analytics for Non-Life Insurance Pricing79 cited
  • 2018Neural networks applied to chain–ladder reserving62 cited
  • 2019DeepTriangle: A Deep Learning Approach to Loss Reserving60 cited
  • 2019A machine learning approach for individual claims reserving in insurance60 cited
  • 2018An Individual Claims History Simulation Machine58 cited
  • 2020Machine Learning in P&C Insurance: A Review for Pricing and Reserving52 cited
  • 2021Recent Challenges in Actuarial Science41 cited
  • 2018AI in Actuarial Science36 cited
  • 2019Individual Loss Reserving Using a Gradient Boosting-Based Approach36 cited
  • 2021Mind the Gap - Safely Incorporating Deep Learning Models into the Actuarial Toolkit23 cited

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