Predictive analytics for SMEs
ItalevionAI analyzes your company's liquidity with predictive models trained on historical data, identifying allocation strategies verified through backtesting before they are proposed.
The problem
Many Italian SMEs maintain liquidity reserves higher than necessary, not due to strategic choice but due to the absence of tools capable of evaluating alternatives rigorously.
Investing corporate capital requires an analysis of risk, time horizon and market scenarios that an internal finance office can rarely conduct on an ongoing basis. The most frequent result is inaction, even when there is measurable room for improvement. Dormant Capital is not a management error: it is the implicit cost of a decision never made.
How it works
The system does not generate isolated forecasts. Each recommendation goes through a modeling phase and a verification phase on historical data, documented and consultable by the customer.
The algorithms analyze market time series, corporate cash flows and macroeconomic indicators to generate probabilistic liquidity allocation scenarios. Each forecast is accompanied by a confidence interval, not an isolated figure.
Analysis of over 15 years of market data for each model
Before being proposed, each strategy is tested on past market periods, including contraction phases, to verify its resilience in unfavorable conditions. The outcome of the backtesting is documented and made available to the customer together with the recommendation.
Test across multiple market cycles, not a single good periodMethodology
Each candidate strategy is applied retroactively to distinct historical time windows, including downturns and high volatility phases. The goal is not to demonstrate constant performance, but to measure how the strategy would have performed in real and different conditions.
Illustrative representation of the structure of a multiple time window test. The real values depend on the risk profile and the capital considered, and are shared during the individual analysis.
Diversification by time horizonLiquidity is segmented by required availability, separating short-term reserves from capital that can be allocated over longer horizons.
Stress test on unfavorable scenariosEach strategy is subjected to simulated adverse market conditions, not just average or optimistic scenarios.
Defined exposure limitsMaximum allocation limits are established for each individual strategy, to contain the impact of unexpected events.
Periodic review of modelsPredictive models are recalibrated when market conditions deviate significantly from initial assumptions.
Concrete applications
Companies with collections concentrated in certain months of the year maintain high reserves to cover periods of lower activity, often higher than real needs.
Expected outcome: allocation of the excess quota on horizons compatible with the seasonal cycle.
The fund intended for unexpected events often remains in accounts with no return, due to the absence of a clear criterion on how much can be invested without compromising its availability.
Expected outcome: tiered liquidity structure, with part of the fund remunerated without loss of immediate access.
Profits set aside awaiting decisions on reinvestment or distribution often remain without a defined intermediate destination.
Expected outcome: temporary allocation consistent with the company's decision horizon, reversible if priorities change.
Frequently asked questions
How we evaluate the stability of forecasts over time.
How the company's financial data is processed.
Each model is evaluated by comparing the predictions generated to data actually observed in post-test periods, not just the data used to build it. Discrepancies are documented and used to recalibrate the model.
No. Backtesting checks how a strategy would have performed under known historical conditions. It does not constitute a guarantee of future results, but reduces the risk of relying on untested strategies in adverse scenarios.
The recommendations generated by the models are reviewed by analysts before being presented to the client, with particular attention to exposure limits and consistency with the declared liquidity profile.
The data is processed according to the principles of the General Data Protection Regulation (GDPR) and stored on infrastructures with limited and tracked access. Technical detail is available on request.
The data used for analysis is not shared with third parties for commercial purposes. Any technical suppliers act as data controllers, within the limits set by the contract.
Yes. The customer can request access, rectification or deletion of their data at any time, according to the methods indicated in the privacy policy.
Compliance note: Data processing follows the GDPR principles applicable to businesses operating in Italy. Specific details of the contract and security measures are provided in the individual analysis phase.
Our team examines the current liquidity structure and returns a preliminary indication on allocation margins, without obligation. The historical data on which the methodology is based is available upon request.