OutRank API enables pensions providers to build the next-generation pension journeys both for accumulators and decumulators. Depending on your business model, it empowers your advisors or customers to navigate through the intricacies of retirement planning by delivering seamless financial decision-making tools. Our APIs are designed by experienced domain experts, ensuring the quality of the underlying models.
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:
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.
Kidbrooke’s Economic Scenario Generator is an API that enables a spectrum of firms to model possible future states of the global economy and capital markets to drive a wide range of portfolio and risk management decisions.
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Kidbrooke’s Balance Sheet Simulator is a critical element of OutRank API responsible for constructing future cash flow trajectories on the balance sheet level.
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Kidbrooke’s financial decision support toolkit is a collection of APIs that support investment goal creation, risk profiling and investment product ranking.
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