Applications of Distributed and Parallel Computing in the Solvency II Framework: The DISAR System

Castellani, Gilberto; Passalacqua, L.

doi:10.1007/978-3-642-21878-1_51

Cited by 9 publications

(10 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the present work, the values of the empirical probability distribution of assets and liabilities have been determined using

D I S A R ®

(Dynamic Investment Strategy with Accounting Rules), a commercial computational system that tackles market‐consistent valuation of complex cash flows using numerical techniques in a stochastic framework, 2,19 designed to be used in the Italian insurance sector 23 . DISAR is composed by a database management system and of a set of computing engines.…”

Section: Valuation In the Solvency II Frameworkmentioning

confidence: 99%

“…In order to have a good estimation of the SCR , both the number of risk‐neutral and real‐world simulations have to be very large, thus resulting in a “compute‐intensive” process 2,18,19 . Nevertheless, nested Monte Carlo simulation is the standard approach for insurance undertakings 3,4,17 even if it can present prohibitive computational challenges.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning techniques in nested stochastic simulations for life insurance

Castellani

Fiore

Marino

et al. 2021

Appl Stoch Models Bus & Ind

Self Cite

View full text Add to dashboard Cite

The insurance regulatory regime introduced in the European Union by the “Solvency II” Directive 2009/138, that has become applicable on 1 January 2016, is aimed to safeguard policyholders and beneficiaries by requiring insurance undertakings to hold own funds able to cover losses, in excess to the expected ones, at the 99.5% confidence level, over a 1‐year period. In order to assess risks and evaluate the regulatory Solvency Capital Requirement, undertakings should compute the probability distribution of the Net Asset Value over a 1‐year period, with a financially inspired market consistent approach. In life insurance, given the peculiarities of the contracts, the valuation of the Net Asset Value distribution requires a nested Monte Carlo simulation, which is extremely time‐consuming. Machine learning techniques are considered a promising candidate to reduce the computational burden of nested simulations. This work investigates the potential of well‐established methods, such as deep learning networks and support vector regressors, when applied to the valuation of the Solvency Capital Requirement of participating life insurance policies, by empirically assessing their effectiveness and by comparing their efficiency and accuracy, also w.r.t. the “traditional” least squares Monte Carlo technique. The work aims also to contribute to the global process of renewal of the European insurance industry, where Solvency II has made the board of directors fully responsible of the choice of evaluation techniques and algorithmic processes, under the periodic monitoring of national supervisory authorities.

show abstract

“…For the present work, the values of the empirical probability distribution of assets and liabilities have been determined using

D I S A R ®

Section: Valuation In the Solvency II Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning techniques in nested stochastic simulations for life insurance

Castellani

Fiore

Marino

et al. 2021

Appl Stoch Models Bus & Ind

Self Cite

View full text Add to dashboard Cite

show abstract

“…A recent proposal [9] has addressed the computational problems deriving from Solvency II compliance in the context of Italian life insurance, introducing DISAR R , a commercial system originally designed to work on a grid of conventional computers. DISAR tackles market-consistent valuation of the complex cash flows using numerical techniques in a stochastic framework, namely Monte Carlo simulation, on a fine-grained time grid.…”

Section: -127)mentioning

confidence: 99%

“…The Dynamic Investment Strategy with Accounting Rules (DISAR) System [9], [8] is designed for the evaluation and control of minimum-guaranteed profit-sharing life policies indexed to the returns of dedicated funds (segregated funds). It is based on market-consistent evaluation criteria under uncertainty in a general asset-liability management (ALM) framework.…”

Section: The Disar R Systemmentioning

confidence: 99%

Machine Learning-Based Elastic Cloud Resource Provisioning in the Solvency II Framework

Rizza

Casarano²,

Castellani³

et al. 2016

2016 IEEE 36th International Conference on Distributed Computing Systems Workshops (ICDCSW)

Self Cite

View full text Add to dashboard Cite

The Solvency II Directive (Directive 2009/138/EC) is a European Directive issued in November 2009 and effective from January 2016, which has been enacted by the European Union to regulate the insurance and reinsurance sector through the discipline of risk management. Solvency II requires European insurance companies to conduct consistent evaluation and continuous monitoring of risks-a process which is computationally complex and extremely resource-intensive. To this end, companies are required to equip themselves with adequate IT infrastructures, facing a significant outlay. In this paper we present the design and the development of a Machine Learning-based approach to transparently deploy on a cloud environment the most resource-intensive portion of the Solvency II-related computation. Our proposal targets DISAR R , a Solvency II-oriented system initially designed to work on a grid of conventional computers. We show how our solution allows to reduce the overall expenses associated with the computation, without hampering the privacy of the companies' data (making it suitable for conventional public cloud environments), and allowing to meet the strict temporal requirements required by the Directive. Additionally, the system is organized as a selfoptimizing loop, which allows to use information gathered from actual (useful) computations, thus requiring a shorter training phase. We present an experimental study conducted on Amazon EC2 to assess the validity and the efficiency of our proposal.

show abstract

“…It stands to reason that the computational performance of the valuation process plays a relevant role; this motivates the need to develop both accurate and efficient numerical code and to use High Performance Computing (HPC) methodologies and resources. The literature on the application of HPC in the development of "internal model" is very poor; a relevant contribution is given in [5] where is introduced DISAR (Dynamic Investment Strategy with Accounting Rules), a Solvency II compliant system designed to work on a grid of conventional computers.…”

Section: Introductionmentioning

confidence: 99%