Pre-Retirement Guidance journeys equip our customers with intuitive tools enabling consumers to visualise their retirement goals and encourage more informed financial choices backed by a robust analytical framework.
The automation of core pension planning analytics is an excellent way to control costs and retain a tight grip on regulatory responsibilities while delivering a truly market-leading service: be it HNWI-oriented analytical support for advisors or completely automated digital journeys.
Whether you envision a regulated financial advice or guidance set-up, OutRank can be adapted to help your customers make more informed, goal-specific decisions about their pensions.
The OutRank API can deliver:
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.
Welcome to our brand-new series describing the elements of digital financial experiences you can build using OutRank API!
Fredrik Daveus, CEO at Kidbrooke®, explores how to build trust in digital wealth management for the Swiss WealthTech Landscape Report 2021 by The Wealth Mosaic.
The financial guidance and advice services, which constitute the life insurer’s core business, were among the first to go through the transformation. Joakim Pettersson, the digital strategy and innovation lead at Skandia, believes that digitalisation is “the only way to scale financial advisory services”.
Evida began its path as a family office managing a wide range of assets for wealthy families. Initially, the Swedish financial advisor outsourced the management of equity and fixed income positions to other parties. However, the combination of their interest for factor-based investments and dissatisfaction with wealth management services provided by the largest banks in Sweden, Switzerland and Luxembourg convinced Evida to build their own digital advisory service.
In this article series, we present a machine learning-based approach to solving a common problem in financial modelling where one is faced with the task of estimating the value of a function which requires a significant amount of computation to evaluate. More specifically, a function that corresponds to a so-called nested simulation aimed at, for example, estimating a capital requirement for a financial institution or the risk associated with a structured product for a retail investor.
In the third and the final part of our “Portfolio Construction” article series, the findings of the previous sections are applied to a broader and more realistic set of assets to evaluate the performance of the proposed methods against more conventional techniques.
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.
The second part of the “Portfolio Construction”-series explores whether introducing parameter uncertainty to the model would improve the out-of-sample performance of the optimal portfolio. Additionally, the article proposes and tests two adjustments to regular utility optimisation.
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.
In the first part of the ”Asset and Liability Management using LSMC” article series, we outline an ALM framework based on a replicating portfolio approach along with a suitable financial objective. This ALM framework, albeit simplified, is constructed to provide a straightforward replication of the complex interactions between assets and liabilities. Moreover, a brief introduction to the LSMC method used to generate all underlying risk factors is presented.
OutRank powers financial decision-making based on the simulations of your clients' personal finances on a balance sheet level. This enables you to achieve a 360 degree view of your clients' financial situation, empowering a truly personalized approach to financial decision-making and goal-based planning at scale.Learn more
The OutRank financial simulaton engine, enables pension providers to build high-quality digital and hybrid customer journeys for accumulators and decumulators alike. With OutRank, you can empower clients to navigate retirement planning by making your financial experiences more engaging, visual and intuitive.Learn more
Kidbrooke’s Economic Scenario Generator is a fully-featured scenario generator capable of replicating all important stylized facts of the widest range of financial assets. Cloud-native and API-first it is a flexible, modular and high-performance solution to any economic scenario needs.Learn more