It is often a good idea to start simple when embarking on a challenging implementation project. Financial institutions usually strive to build simple, intuitive solutions that their customers would easily understand. However, choosing simplicity in your digitalization strategy could lead to both good and bad outcomes. For example, suppose a customer journey delivers value in just a few simple steps, without excessive questionnaires or unnecessary distracting elements. In that case, it is an example of a "good simple." At the same time, simplistic assumptions about future assets' returns may underestimate the investment risk or fail to consider the effect of fees and taxes correctly. Thus, oversimplifying analytical elements of the financial journeys would be an example of "bad simple."
When designing OutRank, our financial simulation engine, we chose a more realistic scenario-based approach over the simplistic models based on Modern Portfolio Theory. Today we discuss one of the core functional units of OutRank in more detail, outlining the implications of its features on the resulting customer journeys.
Economic Scenario Generator (ESG) within OutRank models possible future states of the global economy and capital markets to inform a wide range of portfolio and risk management decisions. In the context of a digital financial journey, the ESG introduces a forward-looking and probability-driven perspective to the elements affected by financial risk while considering the institution's house views. Additionally, due to a granular approach to underlying risk factors, it is possible to accurately model the future market development of an institution's product universe. In essence, the quality of the decision support that financial firms provide to their clients depends on the accuracy of their analysis. Therefore, it is essential to examine the features that differentiate this technology at the core.
Financial institutions can differ dramatically in their business models, even if they operate in the same industry. For instance, some institutions would provide financial advice by matching their fund universe to the customer needs, while others would propose a selection of individual stocks. Furthermore, large financial institutions value the ability to impose their house views on how the global markets would develop over time, while the smaller players could prefer to adopt a historical market consensus from an external source. Therefore, our ESG can adopt the financial institutions' house views or provide your firm with our Global Outlooks, informed by a regularly updated consensus of several renowned financial institutions worldwide. OutRank allows for different levels of granularity when it comes to modelling the customers' product universe and easily adjusts to the institutions' preferred risk factor universe. Achieving relative flexibility regarding the level of modelling granularity and implementing house views has always been a priority for us.
Some forward-looking algorithms require regular adjustments to function correctly. In the financial industry context, it is important to ensure that the model informing the customers' decision-making is as realistic and accurate as possible. There are two distinct processes responsible for updating and analyzing the performance of economic scenario generators – model calibration and model validation. Model calibration updates the prediction machine with the latest historical data. It is an automated process to a large extent. The second process, model validation, includes monitoring the performance of the model, reviewing the ongoing appropriateness of its specification, and testing its results against experience. It contains more qualitative elements than the model calibration. At Kidbrooke, we have gone the extra mile to ensure that the calibration and validation of our models correspond to the industry's best practices. More specifically, we automated a substantial part of these processes, which allow us to keep these tasks as lean and efficient as possible. We also summarize our analyses in an annual validation report enabling our clients to form their own opinions regarding the reliability of the model powering their offerings.
Leveraging data-driven technology is undoubtedly one of the most critical overarching trends in an increasingly more digitalized economy. However, it is crucial to retain control over intelligent algorithms that work with your data. This makes transparency and interpretability critical to the long-term success of a digital offering. To help our customers excel with their digital businesses, we ensure that they can access and visualize the outputs of the economic scenario generator. For instance, they can analyse how imposing different climate change scenarios could affect the expected market development. Moreover, we have introduced actual (as opposed to interpolated) monthly time steps for our scenarios, which allows a more detailed analysis as compared to some legacy providers' ESGs.
Compromising on core technology defining your financial offering is a "bad simple". Therefore, we suggest that you choose not complex but superior technology to drive your digital solutions. That could be not only a reliable differentiating factor to your brand but also a great strategic choice for enabling further opportunities to innovate. Given the right technology, it is possible to give the underlying decision-making tool the soul, the spirit, and the story to match the brand values of your financial institution. And most importantly, suitable models such as those implemented in OutRank are better suited to learn, develop and adapt to your vision as times change.