Model

Knowledge Base Articles

HAYAH Insurance recently partnered with Kidbrooke® to build engaging, self-service investment journeys with OutRank®, the financial simulation engine driving HAYAH’s new goals-based financial planning experiences. HAYAH Insurance, established in 2008 and headquartered in Abu Dhabi, is the UAE’s newest and most exciting insurance company, specialising in life and medical insurance and savings products. Here HAYAH Insurance talk about the exciting partnership they have embarked upon with Kidbrooke.

Skandia, the Swedish life insurance company, has ramped up its initiatives in using technology to improve the overall experience of its customers. The goal is simple – developing a digital space to offer touchpoints relevant and meaningful enough to drive engagement across all of Skandia’s channels.

Today’s case study examines a real-life experience of a Swedish family who struggled to receive adequate help from the local wealth management service providers.

Skandia strives to build communication channels in a digital space that would match the physical experiences in engagement levels and even improve the service quality in a way that has not been achievable before.

The modern wealth management industry still relies on the 50-year-old approaches to portfolio management, widely popularized by Markowitz's Modern Portfolio Theory (1952). Despite heavy criticism within the academic circles, the alternative methods remain undeservingly overlooked in practice. In the context of the modern leap for hyper-customization, we look into one of the alternatives to Modern Portfolio Theory in greater detail - the Utility-based approach.

There is a number of challenges associated with portfolio construction based on historical data. This three-part article series explores some of the most common issues attributed to the model-based portfolio optimization: the sensitivity to changes in data, large variations in portfolio weights and the bad out-of-sample performance.

As machine learning methods grow in use and popularity, we explore yet another dimension of wealth management that our experts consider fit for applying such frameworks. In this article, we deploy hierarchical clustering to find more consistent ways of predicting the relative future performance of funds.

In the third and concluding article in the ALM using LMSC series, we focus on analyzing the optimal asset allocations in the context of changing asset classes as well as finding the optimal allocation by maximizing the risk-adjusted net asset value. The estimates based on the LSMC method are then compared to the estimates obtained from the full nested Monte Carlo method.