Kidbrooke's Research Collection

Knowledge Base

Our knowledge base consists of our collection of original technical research articles produced by our experienced staff within the R&D stage of product development.


Building a Cutting Edge Digital Life Insurer via OutRank® - the Financial Simulation Engine

May | 2023

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 case study III: Using OutRank to Enhance their Investment Customer Journeys

May | 2023

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.

Personal Accident Insurance: Would My Savings Suffice?

June | 2022

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 Case Study II: Building Channel-Agnostic Wealth Experiences

February | 2022

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.

OutRank User Guide: The Walkthrough of the Digital Investments Use Case

January | 2022

Welcome to our brand-new series describing the elements of digital financial experiences you can build using OutRank API!

Steps to heaven - How to take your customers on a journey to the land of digital trust

December | 2021

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.

Skandia Case Study: Pioneering Seamless Digital Wealth

July | 2021

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 Case Study: How to build an innovative financial advisor in under seven months?

June | 2021

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.

Part II - Artificial Neural Networks as a Substitute to LSMC and Nested Simulations

January | 2021

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.

Part I - Introduction to Artificial Neural Networks

November | 2020

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.

Part III - Portfolio Construction - The Real World Analysis

December | 2019

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.

Beyond Modern Portfolio Theory: Expected Utility Optimisation

November | 2019

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.

Part II - Portfolio Construction - Sampling & Optimisation

September | 2019

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.

Part I - Portfolio Construction - Parameter & Model Uncertainty

August | 2019

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.

Hierarchical Clustering: Prediction of Systematic Underperformance

June | 2019

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.

Part I: An Introduction to Self-Normalizing Neural Networks

March | 2019

Machine learning applications have become more prominent in the financial industry in recent years. Our new article series is exploring the benefits and challenges of using self-normalising neural networks (SNNs) for calculating liquidity risk. The first piece of the series introduces the main concepts used in the investigative case study for the Swedish bond market.

Part III: Asset and Liability Management Using LSMC - Allocation Optimisation

February | 2019

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.

Part II: Asset and Liability Management Using LSMC - Accuracy and Performance

December | 2018

The second part of the series exploring the use of Least Squares Monte Carlo in Asset and Liability Management is focused on evaluation of accuracy and performance of this method in comparison to full nested Monte Carlo simulation benchmarks.

Part I: Asset and Liability Management Using LSMC - Introduction to the Framework

September | 2018

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.

Introduction to Credit Index Modelling

September | 2017

This article will discuss why it is important to model credit indices and detail a number of different approaches to this problem.

Overcoming the Insufficiency of Historical Data; The Rolling Window Method

August | 2017

In this article, we evaluate the rolling window procedure to alleviate the problem of inadequate data by increasing the number of observations extracted from a limited set of data.

    Implications of a joint modelling framework in dependence modelling

    February | 2016

    In this part we evaluate the framework by performing simulations and discuss the implications of utilizing a dependence model like this.

    A Joint Framework for Dependence Modelling

    October | 2015

    In this article we seek to develop a model allowing for dependence between equity and credit risk.

      An Introduction to Dependence Modeling

      October | 2015

      Part I of III describing a framework for analysing dependency between equity and credit risk.

        Modelling Operational Risk

        September | 2015

        In this article we conduct a case study of the operational risk capital requirement, with the ambition of comparing it with the Solvency II Standard Formula.

          Effects of Least Squares Monte Carlo Simulation

          September | 2015

          In this article we investigate the performance of the LSMC approach on a stylised financial product.

          An Introduction to Operational Risk

          August | 2015

          We will in this article give an introduction to operational risk, and explain the subject as it is defined in Basel II.

            Regression Functions in Least-Squares Monte Carlo Simulations

            August | 2015

            In this article we will introduce an efficient way of estimating and calibrating regression functions in a LSMC environment.

            An Introduction to Least-Squares Monte Carlo Simulation

            June | 2015

            In this part we introduce a recognised technique for sophisticated risk modelling, Least-Squares Monte Carlo.