Mack-Net model: Blending Mack's model with Recurrent Neural Networks.

In general insurance companies, a correct estimation of liabilities plays a key role due to its impact on management and investing decisions. Since the Financial Crisis of 2007–2008 and the strengthening of regulation, the focus is not only on the total reserve but also on its variability, which is an indicator of the risk assumed by the company. Thus, measures that relate profitability with risk are crucial in order to understand the financial position of insurance firms. Taking advantage of the increasing computational power, this paper introduces a stochastic reserving model whose aim is to improve the performance of the traditional Mack's reserving model by applying an ensemble of Recurrent Neural Networks. The results demonstrate that blending traditional reserving models with deep and machine learning techniques leads to a more accurate assessment of general insurance liabilities. • The proposed Mack-Net model leads to a more accurate reserve prediction. • Mack-Net model generates more appropriate risk measures than Mack's model. • Mack-Net model does not increase the variance to generate an appropriate VaR. • Neural Networks estimate more accurate parameters than usual reserving models. [ABSTRACT FROM AUTHOR]