Why model accuracy is key to digital financial advice

June 6, 2025

Imagine choosing a savings portfolio based on your “low risk” appetite, only to receive a recommendation for a higher-risk investment. That’s exactly what happened to one of Kidbrooke’s users, prompting the question: “Why?” The surprising answer—“because the model is working as it should”—opens a broader discussion about the role of intelligent modelling in digital financial advice.

Kidbrooke, which offers unified analytics for investment and wealth, recently explored why good models matter in digital financial advice.

At the core of this debate is a long-standing question: should financial advice models be simple or complex? According to Kidbrooke, simplicity has value, but only when it is supported by a robust understanding of real-life financial variables. The company advocates for complexity that drives clarity—using advanced technology to simulate outcomes, personalise advice, and ultimately build trust with end users.

Kidbrooke’s approach is grounded in a powerful economic scenario generator, which uses Monte Carlo simulations to project thousands of possible future scenarios. This enables the delivery of tailored, real-time advice based on the current market outlook. The model is updated monthly to remain aligned with macroeconomic trends and client needs.

Unlike simpler models that rely solely on basic inputs like a customer’s stated risk level, Kidbrooke’s advice engine considers multiple dimensions. These include savings capacity, emergency funds (buffer capital), and existing assets held elsewhere. This comprehensive view helps determine how much risk is appropriate in the context of the customer’s full financial picture. For instance, someone with a strong financial safety net might afford to take more investment risk—even if their risk tolerance seems low on the surface.

Transparency is a key feature of the system. Kidbrooke supports advisors with clear explanations of how recommendations are shaped, helping them build confidence with clients and refine advice as needed. As one example, when a customer noticed a shift in advice, the advisor was able to walk through how their buffer capital and other holdings had influenced the new recommendation.

Importantly, the technology is designed to support hybrid advice models. While the system can generate updated recommendations on a regular basis, such as quarterly, the human touch remains vital. Advisors are equipped to use the model to explain decisions and tailor conversations, ensuring clients feel heard and supported.

Kidbrooke also recognises that not every customer or firm needs the full depth of a complex model. For those seeking a more straightforward approach, the system can be configured to provide simplified guidance. This flexibility ensures that the tool can adapt to different business strategies and client segments—from mass-affluent customers to high-net-worth individuals.

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