Verane Négocaison, dashboard for cash flow analysis and allocation recommendations

Turn your excess cash into measurable decisions

Verane Négocaison continuously analyzes your cash flows and suggests allocation paths adapted to your risk profile, with publicly viewable results.

Performance log verified by the community of users
The observation

Idle cash has a cost, rarely visible on a bank statement

Many managers leave their excess cash in low-paying accounts, due to lack of time to compare options or to monitor day-to-day market developments. Excel tables and one-off analyzes provide a dated snapshot, not a continuous vision.

  • Excess cash tied up in low-paying funds by default due to lack of regular monitoring.
  • Time-consuming manual analyses, dependent on a single person and difficult to audit.
  • Allocation decisions made without real-time visibility on the level of risk actually taken.
How it works

A continuous analysis cycle, without technical jargon

Verane Négocaison is based on three automatically repeated steps, so that the recommendations reflect the real situation of your cash flow and not a fixed estimate.

Step 1

Data collection

Your cash flows, banking histories and maturities are aggregated securely, on a regular basis, without manual intervention on your part.

Step 2

Predictive modeling

The models identify cash flow trends, seasonality of your business and plausible market scenarios to anticipate your near-term liquidity needs.

Step 3

Optimization recommendation

An allocation is offered between several short-term supports, each with an explicit level of risk, to be validated or adjusted according to your constraints.

Our difference

An open performance log, not an abstract promise

Each recommendation followed generates an entry in a searchable log, cross-referenced with feedback from users who applied it. It is this intersection that constitutes community verification.

Date Strategy applied Observed performance Deviation vs. forecast Verification Status
Example Short-term allocation, conservative profile To consult online To consult online Verified by 1+ user
Example Mixed allocation, balanced profile To consult online To consult online Verified by 1+ user

Observed net return

Monitoring entry by entry, distinct from the theoretical performance announced at the time of the recommendation.

Gap between prediction and reality

Published for each strategy, in order to measure the reliability of the models over time.

Community Verification

Each entry can be confirmed or contested by users who have applied the same recommendation.

The table above illustrates the log format. The actual values ​​are published after application of the recommendations and verification by the users concerned; they vary depending on the risk profile and market conditions.

Concrete benefits

What a data-driven allocation changes

Three desired effects, regardless of the size of your cash flow: fewer human errors, an ability to absorb growth, and an updated reading of the risk taken.

Verane Négocaison, risk analysis applied to corporate treasury

Reduction of risk linked to human decision-making

An allocation decision taken in a hurry, on the basis of an intuition or an isolated figure, remains a common source of error. The models apply the same criteria to each analysis, which limits deviations linked to fatigue or approximation.

A method that holds the charge when cash flow increases

A pivot table becomes difficult to maintain as soon as the amounts or number of accounts increase. The automated processing maintains the same rigorous analysis, whether your surplus is modest or significant.

An updated vision, not a fixed quarterly report

Liquidity needs evolve as collections and maturities progress. The recommendations are recalculated each time the flows are updated, rather than during a one-off accounting update.

Frequently asked questions

What we get asked most often

Allocation decisions affect both trust in the algorithm and the security of the transmitted data. Here are the points most frequently raised by executives and financial managers.

How does the AI ​​determine its recommendations?

The models use your cash flow history and market scenarios to estimate a short-term liquidity need. Each recommendation indicates the associated level of risk; the final decision always remains yours.

Can I ignore or change a recommendation?

Yes. Recommendations are reasoned proposals, not automatic orders for execution. You can adjust the amounts, rule out an option or request an analysis on a more restricted scope.

How are the performances published in the journal verified?

Each log entry is associated with a recommendation actually applied by one or more users, who confirm or report a deviation from the result obtained. This confrontation between prediction and reality constitutes community verification.

Are my banking data transmitted to third parties?

Treasury data is used exclusively for analysis and generation of recommendations for your account. They are not resold or used for advertising purposes.

What happens if a model is wrong?

A discrepancy between forecast and actual result is possible, as with any financial analysis method. This is precisely what the performance log makes visible, gap after gap, rather than hiding it.

On data security

Data exchanges are encrypted and access to your space is restricted to people you authorize. Treasury information is hosted within the European Union in accordance with the requirements for financial data.

Every euro left without arbitrage has a measurable opportunity cost

Review Verane Négocaison's performance log before deciding whether the approach fits how you want to manage your excess cash.

Request access View the full FAQ