Aurore Fondange trains machine learning models on your historical data and investment behavior. The AI learns your risk tolerance and automatically adjusts the diversification of your portfolio, without manual intervention at each trade.
The engine continuously ingests your data feeds and recalculates the probability of each scenario with each new market event. The more the volume of data grows, the more the model calibration becomes finer.
Unlike manual analysis, the system does not depend on the availability of an analyst. It processes market data, macroeconomic indicators and your decision history in parallel, then returns a recommendation prioritized by risk level.
The goal is not to replace your strategic judgment, but to reduce the amount of human error linked to decision fatigue and confirmation bias.
Markets, history, risk profile.
Weighting of probable scenarios.
Arbitration proposed, validated by you.
The system never operates in a black box. Each step produces a searchable trace, and you retain control over the final triggering of the execution.
Aggregation of market data, internal flows of your activity and your decision history, in a structured format usable by the model.
The algorithm compares your past decisions to simulated scenarios and gradually adjusts the risk tolerance parameters specific to your profile.
The recommendation adopted is presented with its reasoning. You validate, adjust or reject, and this decision enriches the following calibration cycle.
The analytical structure remains the same; only the risk parameters and arbitrage objectives differ depending on whether it is a corporate strategy or a personal portfolio.
Management teams use the system to arbitrate between several budget allocation scenarios. The model shows the probable impact of each option over a defined horizon, before any commitment of resources.
Budget scenario sensitivity map
For an individual profile, the system learns the acceptable loss thresholds and offers diversification that respects these limits, rather than a standardized allocation that is identical for all profiles.
Distribution of a portfolio calibrated to the risk profile
Aurore Fondange designs decision support tools based on machine learning, intended for professionals who must decide between several scenarios within short deadlines. The bias is architectural: each recommendation is based on a verifiable calculation trace, not on a black box.
The team favors a technical but accessible approach: the models remain explainable, and the results are presented with the reasoning behind them, so that the final decision remains in your hands.
The details below are intended to support technical and compliance teams who evaluate the platform before adoption.
Data is encrypted at rest and in transit. They are only used to calibrate your own risk profile and are never shared with other accounts without your explicit consent.
No. The system produces recommendations accompanied by their reasoning. Execution remains subject to your validation, unless you explicitly activate an automatic execution mode based on thresholds that you define yourself.
The initial connection of data feeds and the first risk profiling are usually completed within a few days. The accuracy of the recommendations then gradually improves as the model accumulates calibration cycles.
Connect your first data feeds and let the model build your initial risk profile. You keep control over each validation.