FOLIO applies a predictive, time-series model to your cash position, continuously re-estimating risk and withdrawal likelihood. The result is an allocation that works for the business without the usual trade-off: no lock-up periods, and withdrawals settle instantly even while capital is deployed.
Most German Mittelstand businesses keep a substantial share of working capital in low-yield accounts, on the assumption that liquidity and return cannot coexist. That assumption held when cash management relied on static rules: fixed notice periods, manual forecasting, quarterly reviews. Under those conditions, an idle balance was simply the price paid for safety.
Those rules were built for a world without continuous, per-account forecasting. They were never designed to distinguish a balance that is genuinely needed next week from one that will not be touched for months.
Inflation reduces the real value of that safety margin for every month it sits unused. A predictive model, re-estimating short-term liquidity needs on an ongoing basis, can separate the portion of a balance that is genuinely idle from the portion that must stay accessible.
The difference is not a matter of taking on more risk. It is a matter of allocating capital with better information, and keeping the accessible portion truly accessible rather than reserved out of habit.
Illustrative comparison of reserved versus actively allocated capital under a static approach and a continuously forecast one. Actual proportions depend on each account's transaction pattern.
Exposure across counterparties and instruments is recalculated continuously, not at the end of a reporting cycle. A position is flagged the moment a risk parameter moves outside its expected range.
Time-series models trained on historical disbursement and receipt patterns estimate when capital will genuinely be needed, separating seasonal cycles from one-off anomalies.
Allocation recommendations are generated from the risk and cash-flow outputs, balancing yield potential against the instant-access requirement set for each account.
The three pillars operate on a shared data pipeline. A change in projected cash need is reflected in the risk assessment and the allocation recommendation within the same processing cycle, rather than propagating through separate systems on different schedules.
The model ingests transaction history and current account signals to maintain a rolling estimate of near-term withdrawal likelihood.
A reserve, sized to that forecast, is kept in immediately accessible form at all times. The remainder becomes eligible for allocation.
A withdrawal request is settled from the reserve first. No notice period, no lock-up window, and no penalty for early access.
This sequence is what allows FOLIO to offer no lock-up periods as a standing feature rather than an exception. The reserve is recalculated before the need arises, not after a withdrawal request is submitted, which is the structural difference between predictive liquidity management and a fixed-term placement.
A mid-sized manufacturer carries higher cash reserves between September and January to cover seasonal input purchasing. Historically, the full reserve sat in a standard business account for the entire period, regardless of when it was actually drawn down.
The forecasting layer identified that only a portion of the reserve was required at any single point in the cycle. The remainder became eligible for allocation while remaining withdrawable without notice. The working-capital cushion stayed the same size; the uncommitted portion of it stopped sitting idle.
A business-services firm held short-term client retainers in a single operating account, with limited visibility into counterparty or concentration risk. The real-time risk assessment segmented the balance by expected holding period and flagged a concentration exposure the finance team had not previously quantified.
Short-term placement was restructured to match the measured risk profile, reducing exposure to any single counterparty, with no change to withdrawal availability on any part of the balance.
Recommendations are produced by a layered architecture. A time-series forecasting module estimates liquidity needs; a risk-scoring module evaluates counterparty and instrument exposure; and an optimisation layer reconciles both outputs against the instant-access constraint set for each account.
The framework is retrained on an ongoing basis as new transaction data becomes available, rather than on a fixed annual schedule, so its liquidity estimates reflect current behaviour rather than a historical average.
Account and transaction data used for forecasting is processed under EU data protection standards and is not shared with third parties for purposes unrelated to the analysis it was collected for. Model training runs on anonymised, aggregated patterns; an individual account's data is not used to inform recommendations made to other clients.
Begin with an analysis of your current liquidity structure. Funds remain accessible throughout the process, with no lock-up period at any stage, from the initial assessment to ongoing allocation.
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