Machine learning techniques for mortality modeling

Deprez, Philippe; Shevchenko, Pavel V.; Wüthrich, Mario V.

doi:10.1007/s13385-017-0152-4

Cited by 50 publications

(26 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…) and m mdl x the corresponding central death rate. Following Deprez et al (2017), but modeling the central death death rate instead of mortality rate (q x ), we initially set:…”

Section: The Modelmentioning

confidence: 99%

“…The abundance of proposed models shows that forecasting future mortality from historical trends is non-trivial. Following the idea proposed in Deprez et al (2017), we use machine learning algorithms, able to catch patterns that are not commonly identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit of standard stochastic mortality models. The machine learning estimator is then forecasted according to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard stochastic models.…”

mentioning

confidence: 99%

“…They are likely to be unwilling to use algorithms whose decisions cannot be rationally explained.However, we believe that machine learning techniques can be valuable as a complement to standard mortality models, rather than a substitute.In the literature related to mortality modeling, there are very few contributions on this topic. The work in Deprez et al (2017) showed that machine learning algorithms are useful to assess the goodness of fit of the mortality estimates provided by standard stochastic mortality models (they considered Lee-Carter and Renshaw-Haberman models). They applied a regression tree boosting machine to "analyze how the modeling should be improved based on feature components of an individual, such as its age or its birth cohort.…”

mentioning

confidence: 99%

“…The novelty of this paper is primarily in the mortality forecasting that takes advantage of machine learning, clearly capturing patterns that are not identifiable with a standard mortality model. Following Deprez et al (2017), we use tree-based machine learning techniques to calibrate a parameter (the machine learning estimator) to be applied to mortality rates fitted by the standard mortality model.We analyze three famous stochastic mortality models: the Lee-Carter model Lee and Carter (1992), which is still the most frequently implemented, the Renshaw-Haberman model Renshaw and Haberman (2006), which also considers the cohort effect, and the Plat model Plat (2009), which tries to combine the parameters of the Lee-Carter model with those of the Cairns-Blake-Dowd model with the cohort effect, named "M7" (Cairns et al (2009)). Three different kinds of supervised learning methods are considered for calibrating the machine learning estimator: decision tree, random forest, and gradient boosting, which are all tree-based.We show that the implementation of these machine learning techniques, based on features components such as age, sex, calendar year, and birth cohort, leads to a better fit of the historical data, with respect to the estimates given by the Lee-Carter, Renshaw-Haberman, and Plat models.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

See 4 more Smart Citations

Application of Machine Learning to Mortality Modeling and Forecasting

Levantesi

Pizzorusso²

2019

Risks

View full text Add to dashboard Cite

Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them forecasting future mortality rates by extrapolating one or more latent factors. The abundance of proposed models shows that forecasting future mortality from historical trends is non-trivial. Following the idea proposed in Deprez et al. (2017), we use machine learning algorithms, able to catch patterns that are not commonly identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit of standard stochastic mortality models. The machine learning estimator is then forecasted according to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard stochastic models. Out-of sample forecasts are provided to verify the model accuracy.it allows us to understand processes better, make predictions about the future based on historical data, and categorize sets of data automatically.We can distinguish between supervised and unsupervised learning methods. In the supervised learning methods, the goal is to establish the relations between a range of predictors (independent variables) and a determined target (dependent variable), whereas in the unsupervised learning methods, the algorithm sets patterns among a range of variables in order to group records that show similarities, without considering an output measure. While in the supervised method, the algorithm learns from the dataset the rules that are fed to the machine, in the unsupervised method, it has to identify the rules autonomously. Logistic and multiple regression, classification and regression trees, and naive Bayes are examples of supervised learning methods, while association rules and clustering are classified as unsupervised learning methods.Despite the increasing usage in different fields of research, applications of machine learning in demography are not so popular. The main reason lies in the findings often being seen as "black boxes" and considered difficult to interpret. Moreover, the algorithms are not theory driven (but quite data driven), while demographers are often interested in analyzing specific hypotheses. They are likely to be unwilling to use algorithms whose decisions cannot be rationally explained.However, we believe that machine learning techniques can be valuable as a complement to standard mortality models, rather than a substitute.In the literature related to mortality modeling, there are very few contributions on this topic. The work in Deprez et al. (2017) showed that machine learning algorithms are useful to assess the goodness of fit of the mortality estimates provided by standard stochastic mortality models (they considered Lee-Carter and Renshaw-Haberman models). They applied a regression tree boosting machine to "analyze how the modeling should be improved based on feature components of an individual, such as its age...

show abstract

“…) and m mdl x the corresponding central death rate. Following Deprez et al (2017), but modeling the central death death rate instead of mortality rate (q x ), we initially set:…”

Section: The Modelmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Application of Machine Learning to Mortality Modeling and Forecasting

Levantesi

Pizzorusso²

2019

Risks

View full text Add to dashboard Cite

show abstract

Computational Intelligence Based Hybrid Hyperparameter Tuned Prediction Techniques for COVID-19 Epidemiological Data

Dhamodharavadhani

Rathipriya

2021

Understanding COVID-19: The Role of Computational Intelligence

View full text Add to dashboard Cite

Forecasting Longevity for Financial Applications: A First Experiment with Deep Learning Methods

Bravo

2021

Communications in Computer and Information Science

View full text Add to dashboard Cite

Forecasting longevity is essential in multiple research and policy areas, including the pricing of life insurance contracts, the valuation of capital market solutions for longevity risk management, and pension policy. This paper empirically investigates the predictive performance of Recurrent Neural Networks (RNN) with Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures in jointly modeling and multivariate time series forecasting of age-specific mortality rates at all ages. We fine-tune the three hidden layers GRU and LSTM model's hyperparameters for time series forecasting and compare the model's forecasting accuracy with that produced by traditional Generalised Age-Period-Cohort (GAPC) stochastic mortality models. The empirical results suggest that the two RNN architectures generally outperform the GAPC models investigated in both the male and female populations, but the results are sensitive to the accuracy criteria. The empirical results also show that the RNN-GRU network slightly outperforms the RNN with an LSTM architecture and can produce mortality schedules that capture relatively well the dynamics of mortality rates across age and time. Further investigations considering other RNN architectures, calibration procedures, and sample datasets are necessary to confirm the superiority of RNN in forecasting longevity.

show abstract

Machine learning techniques for mortality modeling

Cited by 50 publications

References 12 publications

Application of Machine Learning to Mortality Modeling and Forecasting

Application of Machine Learning to Mortality Modeling and Forecasting

Computational Intelligence Based Hybrid Hyperparameter Tuned Prediction Techniques for COVID-19 Epidemiological Data

Forecasting Longevity for Financial Applications: A First Experiment with Deep Learning Methods

Contact Info

Product

Resources

About