Imagine you are picking a portfolio for your savings. You know your risk profile is “low,” so naturally, you expect to be matched with a low-risk investment. But what if the recommendation you get is something riskier—like a high-risk portfolio? That is exactly what one of our customers experienced, and understandably, they asked: “why?”

The short answer is: "because the model is working as it should.”

Before we dive into the more technical details, it is worth pausing to acknowledge a common debate in digital financial advice: should the model be simple or complex? At Kidbrooke, we believe that simplicity has its place – when it is informed by the underlying complexity of real-life financial situations. Here, we will walk you through why complexity, when done right, leads to clearer, more confident decisions—and how our model accommodates both ends of the spectrum.

At the heart of our advice engine is a sophisticated economic scenario generator—powered by Monte Carlo simulations. This allows us to simulate thousands of potential future economic paths and provide digital financial advice that is not only tailored, but also realistic. We recalibrate the model monthly to reflect the latest market outlook and keep the advice aligned with current conditions.

What is really driving the advice?

Our model is designed to go beyond surface-level inputs and deliver holistic financial advice. In addition to the customer’s risk profile, it takes a wide range of other inputs into account.

For example:

• Savings capacity — how much the customer can contribute regularly
• Buffer capital — emergency funds that provide financial stability
• Other savings — assets elsewhere that may already involve some risk

These are just a few of the parameters our model considers capturing the full financial context of the customer. All of these factors shape what kind of risk actually makes sense for each individual. For example, someone with a lot of buffer capital might be able to take more risk in their new investments even if they do not feel very risk-tolerant. On the other hand, a customer with large amounts of other capital might already have enough risk exposure elsewhere. The model captures that, too.

Transparency builds trust

When a customer noticed a change in the advice over time, we walked them through how buffer savings and external capital influence the recommendation. The shift made sense in context. That is the power of a transparent, explainable model: it gives both advisers and end-customers confidence in the digital financial advice—and just as importantly, it gives advisers the ability to adjust and finetune the inputs based on customer preferences or policy decisions. Because without a good model, you do not just lose accuracy—you lose insight. And while the model can automatically generate updated advice, quarterly for example, to help end-customers feel continuously supported and informed, it does not replace human interaction.

It enables hybrid advice: digital where it adds efficiency, human where it adds trust. Advisers are always available to walk through the recommendation and help interpret it in a personal context, giving the customer the opportunity to have that trusted conversation that puts their mind at ease. In a world where digital financial advice tools are increasingly prevalent, this human interaction remains irreplaceable. The goal is not to replace advisers, but to equip them with better tools and insights, enabling them to offer more meaningful support to clients.

Why not keep it simple? 
Because simple models can not answer complex questions. They might always recommend the same portfolio for a “low risk” person, regardless of their broader financial picture. That is not good advice; it is one-size-fits-all. That said, we recognise that simplicity can be the right choice in certain contexts. Some of our customers prefer a more straightforward model, either because of internal strategy or because it better suits their end-customers’ needs.

Flexibility meets clarity

Our model is built to be flexible: it can be fine-tuned to handle complexity, but it can also be adapted to offer simplified digital financial advice if that is the desired approach. Whether you are delivering advice to a mass-affluent audience or providing support for more complex, high-net-worth portfolios, the model adjusts accordingly.

We understand that different firms, and different end-customers, have varying needs. Some require robust financial projections with dynamic scenario simulations, while others may prefer a more simple approach with fewer inputs and assumptions. Our model supports both. It empowers financial institutions to adjust key parameters, reflect their brand’s risk philosophy, and deliver digital financial advice that is both explainable and scalable.

Yes, it is complex; but with that complexity comes clarity and control. When built properly, a sophisticated model does not obscure the advice; it reveals the ‘why’ behind it, helping firms meet regulatory expectations and customers make smarter, more confident decisions.

Read the original article here.