Deep neural networks (DNNs) have been applied in class incremental learning, which aims to solve common real-world problems of learning new classes continually. One drawback standard DNNs is that they are prone catastrophic forgetting. Knowledge distillation (KD) a commonly used technique alleviate this problem. In paper, we demonstrate it can indeed help the model output more discriminative results within old classes. However, cannot problem tends classify objects into classes, causing...

Variable annuities are insurance products that contain complex guarantees. To manage the financial risks associated with these guarantees, companies rely heavily on Monte Carlo simulation. However, using simulation to calculate fair market values of guarantees for a large portfolio variable is extremely time consuming. In this article, we propose class GB2 distributions model capture positive skewness typically observed empirically. Numerical results used demonstrate and evaluate performance...

In the field of biomechanics, delicate structural layout living organisms often brings many inspirations for engineering design. As in case aero-engine piping layout, current single- and multi-tube layouts are ineffective need to be optimised. Inspired by efficient material transfer space utilisation mechanisms biological systems, we propose an automatic pipe method based on co-evolutionary algorithm improved A* algorithm. Taking inspiration from how networks adapt optimize their...

The financial risk associated with the guarantees embedded in variable annuities cannot be addressed adequately by traditional actuarial techniques. Dynamical hedging is used practice to mitigate arising from annuities. However, a major challenge of dynamical calculate dollar Deltas portfolio within short time interval so that rebalancing can done on timely basis. In this article, we propose two-level metamodeling approach efficiently estimating partial under multiasset framework....

Variable annuity is arguably the most complex individual retirement planning product in financial market. Its intricacy stems from a variety of features including investment options, guaranteed benefits, withdrawal etc. In many ways, variable annuities can be viewed as traditional life and products at next level sophistication with added options. Despite significant amount publications by practitioners academics on subject matter, there have been few research papers that systematically...

Data clustering has been discussed extensively, but almost all known conventional algorithms tend to break down in high dimensional spaces because of the inherent sparsity data points. Existing subspace for handling high-dimensional focus on numerical dimensions. In this paper, we designed an iterative algorithm called SUBCAD categorical sets, based minimization objective function clustering. We deduced some cluster memberships changing rules using function. also determine associated with...

Abstract Metamodeling techniques have recently been proposed to address the computational issues related valuation of large portfolios variable annuity contracts. However, it is extremely diffcult, if not impossible, for researchers obtain real datasets frominsurance companies in order test their metamodeling on such and publish results academic journals. To facilitate development dissemination research effcient portfolios, this paper creates a synthetic portfolio contracts based properties...

Abstract Variable annuities contain complex guarantees, whose fair market value cannot be calculated in closed form. To the insurance companies rely heavily on Monte Carlo simulation, which is extremely computationally demanding for large portfolios of variable annuity policies. Metamodeling approaches have been proposed to address these computational issues. An important step metamodeling experimental design that selects a small number representative policies building metamodels. In this...

Data clustering refers to the process of dividing a set objects into homogeneous groups or clusters such that in each cluster are more similar other than those clusters. As one most popular tools for exploratory data analysis, has been applied many scientific areas. In this article, we give review basics clustering, as distance measures and validity, different types algorithms. We also demonstrate applications insurance by using two scalable algorithms, truncated fuzzy c-means (TFCM)...

A variable annuity is a tax-deferred retirement vehicle created to address concerns that many people have about outliving their assets. In the past decade, rapid growth of annuities has posed great challenges insurance companies especially when it comes valuing complex guarantees embedded in these products.

A variable annuity is a popular life insurance product that comes with financial guarantees. Using Monte Carlo simulation to value large portfolio extremely time-consuming. Metamodeling approaches have been proposed in the literature speed up valuation process. In metamodeling, metamodel first fitted small number of contracts and then used predict values all other contracts. However, metamodels investigated are sophisticated predictive models. this paper, we investigate use linear regression...

Variable annuities are long-term investment vehicles that have grown rapidly in popularity recently. One major feature of variable is they contain guarantees. The guarantees embedded complex and the values cannot be obtained from closed-form formulas. Insurance companies rely heavily on Monte Carlo simulation to calculate fair market Valuation risk management a large portfolio big challenge insurance because model very time consuming. In this paper, we propose use metamodeling approach speed...