Predictive capital optimisation
Fidelis Invona analyses market signals in real time to help freelancers put idle capital to work between contracts, without sacrificing liquidity or taking on undisclosed risk.
Decision pipeline (simplified)
The idle cash problem
Freelance and consulting income rarely arrives on a fixed schedule. Between contracts, surplus funds are commonly left in low-yield holding accounts, where they lose real value to inflation while earning little to nothing in return.
Fidelis Invona addresses this through what we term Capital Efficiency: the practice of matching short-term liquidity needs against a continuously updated risk model, so that unused funds are allocated to appropriately low-volatility opportunities rather than left dormant.
How the engine works
Stage 01
The model aggregates global market signals, including rate movements, sector volatility indices and liquidity conditions, refreshed continuously rather than on a fixed batch cycle.
Stage 02
Monte Carlo simulations run against the ingested data to forecast a distribution of likely volatility outcomes, rather than a single point estimate, before any recommendation is generated.
Stage 03
Recommendations are checked against community-verified logic thresholds before execution, and every action taken is written to the public performance log described below.
Community-verified results
Illustrative representation only. Live figures are published on the record-level log available to registered users.
Every recommendation generated by the model, along with its outcome, is written to an immutable, timestamped ledger. This supports Back-tested Reliability review: users and independent observers can check historical accuracy against actual market conditions, rather than relying on curated summaries.
Applied to freelance work
Scenario 01
When a contract ends and the next has not yet started, surplus funds can sit for weeks or months. The model allocates this surplus according to a defined risk band, adjusting exposure automatically as your stated timeline to the next expected payout shortens.
Scenario 02
Funds set aside for HMRC obligations still need to remain accessible on demand. The model ring-fences a liquidity floor for this purpose and only allocates the remainder toward low-volatility, capital-preserving positions.
Scenario 03
Tech and creative freelancers are often exposed to sector-specific demand shocks. The predictive model monitors leading indicators for these sectors and reduces correlated exposure ahead of forecast downturns, rather than after they materialise.
Technical questions
Position sizing is adjusted dynamically based on the forecast volatility band produced by the Monte Carlo simulation stage. When forecast volatility rises beyond your configured tolerance, exposure is reduced automatically, with the objective of limiting drawdown rather than eliminating it entirely. No model can guarantee against loss.
Account-level financial data is used solely to calibrate your liquidity floor and risk tolerance settings. It is not published to the community log. Only anonymised, aggregate decision outcomes are written to the public performance record described in the transparency section.
Yes. You retain Full Liquidity Control over funds outside any ring-fenced reserve you have defined, such as a tax reserve. Withdrawal timing depends on the settlement terms of the specific instrument the model has allocated to, which are disclosed before allocation occurs.
Join a community of 2,000+ UK freelancers leveraging verified predictive analytics to manage capital between contracts.