SKV FR Lucet — data table and predictive model used for cash flow analysis

Data intelligence for freelancers

Anticipate cash flow dips between two missions using a predictive model

SKV FR Lucet analyzes your income flows and suggests ways to allocate your surplus capital, mission after mission. The algorithm acts as an analysis co-pilot: the final decision always remains yours.

Public performance log, updated weekly No promise of guaranteed returns Final decision always under your control

Illustrative overview — monthly distribution of surplus

Schematic example of visualization; the actual data of an account is specific to each mission profile.

Mission income is never linear

Between two contracts, the available cash flow varies greatly, and this irregularity complicates any saving or investment decision. Placing a surplus at the wrong time, or leaving it inactive for too long, has a real cost over time.

SKV FR Lucet builds a forecast model based on your past collections and your known deadlines. The system identifies periods where a portion of your capital can be allocated without weakening your operational cash flow, then proposes an adjustable distribution.

The objective is not to replace your professional judgment, but to secure it with quantified and updated benchmarks.

SKV FR Lucet — diagram of how the forecasting and allocation model works

Three analysis engines work continuously

Each module addresses a distinct dimension of your situation: flow monitoring, risk exposure and distribution of available capital. The results are recalculated with each new incoming data.

01

Real-time tracking

Your collections, pending invoices and recurring charges are consolidated in a single dashboard, updated with each movement to reflect your actual cash flow.

02

Risk assessment model

The system assesses the volatility of your income over several months and adjusts the recommended level of prudence before any allocation proposal.

03

Automated Allocation Engine

Once the risk has been calibrated, the engine suggests a distribution between security reserve and optimizable capital, which you can validate, modify or refuse.

A public performance log, viewable by everyone

Trust cannot be decreed: it is verified. Each allocation decision executed on the platform is recorded in a log accessible to users, with its date, its risk context and its observed result.

Example of a tracked series — internal reference index

Illustration of the tracking format; the values ​​displayed publicly on the actual log vary between user cohorts.

Each log entry can be cross-checked by the user community, which reports any discrepancies between the initial recommendation and the observed execution. The calculation methods applied remain identical for all accounts, without preferential treatment.

The log does not constitute a guarantee of future results: it documents what has been observed, under past market conditions.

Newspaper updated every Monday

Three steps before the first recommendation

The integration remains progressive: no data is used before your explicit validation at each stage.

01

Data integration

You connect your activity records or enter them manually. The system establishes an initial map of your revenue cycles.

02

Model calibration

The model adjusts its risk parameters to your actual history and cash flow horizon, before any numerical suggestion.

03

Supervised execution

Each proposed allocation is submitted to you for validation. You retain the option to adjust or decline any recommendation.

Clarify how it works before committing

How is my data protected?

Transmitted data is encrypted during transfer and storage. Access to account information is limited to functions strictly necessary for calculating recommendations.

What happens to my financial history after an account is closed?

You can request deletion of your activity history. Data already anonymized in the public performance log is not linked to your identity.

Does SKV FR Lucet guarantee a level of performance?

No. The model provides recommendations based on past data and a calculated risk profile, but no future results are guaranteed. The public journal documents observed results, not promises.

Can AI decide for me?

No. Each allocation remains subject to your validation before execution. The role of the model is to inform your decision, not to replace it.

Keep control of your decisions, with additional numerical benchmarks

View the public performance log and evaluate the suitability of the model for yourself before implementing it on your account.

Contact the team

No contractual commitment is required to consult the log or test the calibration simulation.