Methodology Evolution
How our scenario modeling approach has grown and adapted through years of real-world application in Australia's dynamic financial landscape
Development Milestones
Initial Framework Development
Started with basic Monte Carlo simulations for Australian superannuation planning. The approach was straightforward—we focused on retirement scenarios for individuals approaching their 60s. Back then, interest rates were predictable, and market volatility seemed manageable.
Multi-Variable Integration
The pandemic taught us that single-variable models weren't enough. We expanded to include employment scenarios, health costs, and housing market fluctuations. This version could handle up to 15 different variables simultaneously—a significant jump from our original 4-variable system.
Behavioral Economics Layer
Real people don't make perfectly rational financial decisions. We learned this from watching thousands of scenarios play out differently than predicted. Our methodology now accounts for psychological factors, spending patterns that change with life events, and the reality that people adjust their plans mid-stream.
Continuous Refinement Process
Our methodology doesn't sit still. Every quarter, we review outcomes from real scenarios and adjust our algorithms. Sometimes this means tweaking probability distributions. Other times, it requires completely rethinking how we model certain events.
For example, in mid-2024, we noticed our housing cost projections were consistently off for first-time buyers in Sydney and Melbourne. The issue wasn't our math—it was that we hadn't properly weighted the psychological impact of market timing fears on buyer behavior.