While financial services scramble for digital transformation, Kidbrooke fears most have not assessed how the decision-making models of underlying technology might impact customer experiences.
In a new video, Kidbrooke explores how granularity affects the quality of digital financial experiences. The company examines how analytical tools’ underlying model quality and granularity impacts the user experience and navigation through an institution’s strategic roadmap.
The first principle of financial decision-making is Modern Portfolio Theory, which earned a Nobel Prize in Economics for Harry Markowitz, it said. However, Kidbrooke explained this method is flawed.
One reason this is that the method is single period by design. This means it estimates its parameters from historical data for a given time horizon, which makes it impossible to include rebalancing or realistic tax deductions where monthly account balances differ each year.
Another problem with the model is that it assumes returns follow a normal distribution, which it doesn’t. Finally, its approach is siloed because it is difficult to use it for modeling assets and debts together or including more complex instruments.
Kidbrooke explained that scenario-based models are far better. These adopt an approach that allows carrying the entire balance sheet of the end-customer into the future, cashflows included. This enables enhanced probabilistic modeling of underlying assets and debts in a balance sheet.
A boon of the scenario-based model is that they are multiperiod and allow financial brands to differentiate themselves through additional functionalities, including automatic rebalancing and tax deductions.
Secondly, they are unconstrained by assumptions of normally distributed returns and the quadratic nature of the utility function. They would give better modelling and improve calculations of consumers’ finances. Thirdly, it is amplified by the cloud and can evaluate thousands of realistic seconds each second.
Finally, by taking a full balance approach, it can model almost every financial situation an end customer could face during their lives.
The post said, “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 modeling 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.”
Watch the video here.
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