1. Executive Summary
A leading fintech company specializing in forecasting across stocks, forex, crypto, and precious metals elevated its predictive accuracy by integrating an AI-driven ensemble method developed by Intersoft. This approach dynamically prioritizes models based on historical performance, resulting in significantly more reliable price insights. The enhanced forecasting framework supports global trading platforms and corporate finance teams with high-confidence, multi-asset predictions.
2. Client Context
The client operates at the intersection of finance and advanced machine learning, delivering high-frequency forecasts across multiple asset classes. Their clients include:
- Global trading platforms seeking precision for automated trading strategies
- Internal finance departments of multinational firms requiring real-time asset insights for treasury and risk management
Each model produces 7 forecasted price points per asset per interval, providing granular visibility into short- and mid-term price movements.
3. The Challenge
Despite their sophisticated use of models—including Liquid Neural Networks (LNNs), gradient boosting, and recurrent architectures—forecast volatility remained an issue. Challenges included:
- Inconsistent model performance across asset types and market conditions
- Limited visibility into which models to trust at any given moment
- Difficulty in creating a scalable method to leverage multiple models simultaneously
The client needed a systematic, data-driven mechanism to assess model confidence and improve overall reliability.
4. Our Approach
Intersoft proposed a modular forecasting architecture, centered on Ensemble Confidence Orchestration:
- Model Scoring Layer: Developed a historical performance tracker to score models by accuracy per asset and time horizon
- Confidence Aggregator: Designed an ensemble mechanism that dynamically assigns weights to models based on real-time and historical confidence signals
- Integration API: Seamlessly wrapped this logic around the client’s existing forecasting pipeline for minimal operational disruption
5. The Solution
At the heart of the solution is an ensemble meta-model that governs forecast selection:
- For each asset and interval, the ensemble selects from multiple models
- Confidence is computed using historical error rates (MAE, RMSE) contextualized by market volatility
- Forecasts are then ranked and optionally combined, prioritizing the model with the most reliable track record for similar conditions
This method was applied to all asset classes with particular success in crypto and forex markets—where volatility often undermines single-model reliability.
6. Outcomes / Impact
- Up to 1.8% Increase in Forecast Accuracy (compared to prior single-model best)
- Stability Across Volatile Assets: Especially impactful in crypto, where model ranking dynamically adjusted to changing regimes
- Actionable Confidence Scores: Enabled clients to differentiate between high-certainty and low-certainty forecasts
- Improved Client Trust & Retention: Trading platform clients reported fewer false signals and better automated strategy outcomes
7. Lessons Learned / Critical Success Factors
- Model Diversity Is an Asset: Rather than choosing a “best” model, orchestrating them yields stronger, context-aware forecasts.
- Confidence Is Currency: Providing clarity into “why this forecast, now” was key for client adoption.
- Non-Disruptive Innovation Works: Wrapping the ensemble logic around existing outputs ensured quick time-to-value.
8. Why Intersoft?
Intersoft’s strength in AI architecture and deep domain experience in financial modeling enabled a transformation that respected existing IP while amplifying its value. Our track record in creating ensemble-driven intelligence systems made us the ideal strategic partner.
9. Next Steps / Looking Ahead
Intersoft and the client are now exploring:
- Real-Time Model Retraining based on intraday forecast errors
- Client-Facing Dashboards showing ensemble dynamics and rationale
- Expanding Ensemble Logic to volume prediction and sentiment-based models
10. Contact & Call to Action
Curious how ensemble AI can redefine forecasting precision for your trading or financial intelligence needs? Connect with Intersoft at intersoft.nl for a tailored consultation.
