Discretionary management driven by AI
Lune Placenza analyzes your cash flow, learns your risk tolerance and continuously adjusts an allocation adapted to the irregularity of freelance income.
The algorithm is not based on a fixed risk questionnaire. It observes the actual evolution of your cash flow and gradually refines an estimate of the volatility that you can withstand, without intervention on your part.
Each collection, each invoice paid late and each off-peak period enriches an individual risk profile, recalculated at regular intervals rather than from a one-off declaration.
The distribution between available liquidity and investment supports evolves according to your observed cash flow, with safety margins defined in advance for periods without missions.
Each allocation modification is time-stamped and accompanied by a searchable justification: the triggering factor, the extent of the change and its estimated impact.
Flow collection
Profile learning
Allocation Adjustment
Data Intelligence
The Lune Placenza predictive engine observes the rate of your collections over several months to distinguish a temporary drop from a structural change in your activity. This distinction conditions the extent of the proposed adjustments.
When a cash flow dip is detected even before it affects your available balance, the liquid portion of your allocation is reinforced gradually, rather than through a brutal arbitrage which would penalize long-term performance.
Recalculations are carried out continuously, based on the data you authorize to connect, without requiring manual re-entry of your income or expenses.
The proposed discretionary management is based on three distinct stages, each documented and consultable, so that the origin of an arbitration remains understandable.
Incoming and outgoing flows are aggregated and compared to your activity history to establish a map of your available cash flow and its seasonal variations.
A risk tolerance scenario is calculated from this mapping, then compared to conservative market assumptions to determine a target allocation.
The target allocation is applied in stages, with trigger thresholds defined to avoid arbitrages that are too frequent or disproportionate to the observed variation.
Three situations frequently encountered by independent consultants and private investors illustrate how allocation adapts to their context.
When a contract ends without an immediate successor, the invested portion is reduced in a measured manner to preserve a liquidity reserve covering the period of uncertainty, without interrupting the long-term strategy.
Tax deadlines known in advance (deposits, regularizations) are integrated into the cash flow calendar, so that the necessary amounts are isolated before their due date.
A one-off influx of revenue, for example at the close of a major assignment, triggers a review of the allocation between asset classes rather than an automatic allocation at the same ratio as previously.
The following questions come up most often when evaluating an automated management tool. Responses aim for precision rather than generic reassurance.
Exchanges with connected banking establishments take place via encrypted connections, and internal access is limited by role. No bank identification data is stored unencrypted on our servers.
The information used to train and refine statistical models is dissociated from your identity before any aggregate processing. Individual results remain linked to your account only.
The liquid part of the allocation is sized to remain available in the short term. The investment vehicles offered are selected taking into account their redemption period, so that this period is always consistent with your cash flow profile.
Investing in financial instruments involves a risk of capital loss. Past performance is no guarantee of future performance. Lune Placenza provides decision support tools and does not constitute personalized investment advice within the meaning of the regulations applicable in France.