. Successful application of active learning requires an effective metric in order to gauge the informativeness of data. Current …Given the computational challenges associated with valuing large variable annuity (VA) portfolios, a variety of data … mining frameworks, including metamodeling and active learning, have been proposed in recent years. Active learning, a …

interest in FX modeling and prediction. Recently, machine learning (ML) and deep learning (DL) techniques have shown promising … propose a novel forecasting framework, the MVO-BiGRU model, which integrates variational mode decomposition (VMD), data … augmentation, Optuna-optimized hyperparameters, and bidirectional GRU algorithms for monthly FX rate forecasting. The data …

handle these claims. Machine learning (ML) is one of the methodsthat solves this problem. As car insurers aim to improve … study considers how automotive insurance providers incorporate machinery learning intheir company, and explores how ML … models can apply to insurance big data. We utilize various MLmethods, such as logistic regression, XGBoost, random forest …

remains unclear how pension de-risking activities affect firms' performance, partially due to the lack of de-risking data. In … identify different "de-risking" strategies that US-based companies have used. A combination of text mining, machine learning …, and natural language processing methods is applied to the textual data for automated identification and classification of …

The purpose of this paper is to survey recent developments in granular models and machine learning models for loss … models and machine learning models. Their relative merits are discussed, as are the factors governing the choice between them …

. (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 …

data across lines of business, and show that they improve on the predictive accuracy of existing stochastic methods. The …

This paper proposes a generalized deep learning approach for predicting claims developments for non-life insurance …-ladder predicted outstanding claims are used as part of the multi-task learning to remove the dependence on case estimates. Grid … outperforms both traditional chain-ladder methodology, the automated machine learning approaches (AutoML), and the original …

questions by applying several supervised machine learning methods. This algorithm may help financial institutions such as banks …