Digital transformation of financial businesses has been one of the hottest topics for strategic discussions within the industry for several years now. However, these conversations rarely touch upon how decision-making models of underlying technology contribute to the eventual customer experiences. Therefore, while optimising the resources required to digitalise a financial journey, it could be tempting to select a tool with a very simple model at heart. Today, we examine how analytical tools' underlying model quality and granularity can affect the user experience and even the navigation through an institution's strategic roadmap.
It is fair to claim that scalable decision-support is a core element of the digital value propositions in the financial sector. In theory, there is an infinite number of mechanisms suited for evaluating alternative financial choices, but there are two distinct types that we repeatedly come across in this space.
The first and the most common principle of financial decision-making is Modern Portfolio Theory, which earned a Nobel Prize in Economics for Harry Markowitz. Since then, it has become the first topic in any introductory finance curriculum around the world. However, Modern Portfolio Theory, in its most popular form, suffers from several drawbacks. First, this method is single period by design. This means that it estimates its parameters from historical data for a given time horizon. Therefore, it is impossible to include rebalancing or realistic tax deductions, where monthly account balances can differ each year. Second, the assumption that returns follow a normal distribution is unrealistic. It tends to underestimate large losses and thus give too optimistic predictions. Third, this approach is siloed in its nature because it is difficult to use it for modelling assets and debts together or for including more complex instruments such as mortgages in the analysis.
Another family of models able to digitalise financial decision-making is far more sophisticated and elegant. The scenario-based models adopt an approach that allows carrying the entire balance sheet of the end-customer into the future, cashflows included. This approach allows for more advanced probabilistic modelling of the underlying assets and debts on the end-customer balance sheets. The scenarios fueling this method can easily incorporate wealth managers' outlooks on how economies will develop into the future. Like in the first family of models, the optimal portfolio for a given customer is determined by maximising a utility function, defined by their unique financial situation and risk tolerance.
There are numerous distinctive advantages of the scenario-based approach over the standard, simplified cases of the modern portfolio theory.
First, scenario-based models are multiperiod, allowing financial brands to differentiate themselves by providing additional functionalities such as automatic rebalancing and realistic tax deduction, which would not be possible under the first approach.
Second, unconstrained by the assumptions of normally distributed returns and the quadratic nature of the utility function, the scenario-based models allow for more accurate stochastic modelling of institutions' product universe. This would have a similar effect on the resulting calculations of consumers' finances, as would an increase in your phone's camera resolution on the pictures you're taking.
Third, amplified by the cloud, the simulation engines using these models can evaluate thousands of realistic scenarios per second, contributing to a fast and seamless customer experience, which is critical in a digital context.
And fourth, the full balance sheet approach allows modelling of virtually any financial situation that end customers could face throughout their lives. This design supports numerous customer journeys only constrained by the fantasy of the financial institution. Essentially, this technology allows for building a genuine family office offering for the masses, destroying siloes in digital wealth once and for all.
When designing OutRank, our financial simulation engine, we chose the scenario-based approach. Specifically, we use a cutting-edge discrete time-series model with an ARMA-like structure and stochastic volatility calibrated to VIX historical data. This choice helped us achieve more realistic risk modelling capabilities, which stakeholders such as asset management or compliance teams tend to appreciate. The model quality is also instrumental for mapping your institution's products and incorporating house views. We also find that granularity that is easily tied to fundamental assumptions results in fewer compliance overheads.
Even though there is a dramatic difference at the very heart of these methods, many reputable institutions carry on spending millions on developing inferior experiences for their customers. We believe the reason is in the short-term strategic outlooks and a reactive digitalisation strategy pursued by many such institutions. The user experiences might look similar, but the first type of model heavily limits an opportunity to develop a holistic approach to consumer's finances. Therefore, these companies risk replacing a newly built, expensive customer journey with another one in the future.
The quality and attention to detail are not always visible or understood by the customers of the financial institutions. Although many banks and insurers made commitments to digital strategies, there is still a lack of discussion about which principles would comprise the decision-making processes and how good they are. We believe that choosing a superior technology to drive your digital solutions 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 prediction machine the soul, the spirit and the story of your financial institution. And most importantly, the suitable models are better suited to learn, develop and adapt to your vision as times change.
