Kastren Fundast analyses your cash position with predictive models and recommends how much surplus to deploy, and when. Every recommendation is written into a public performance log, so you can review the reasoning before you rely on it.
Recommendations are reviewed by an independent community of finance professionals before they are published, not after.
Most small businesses keep more cash in low-interest accounts than they need for day-to-day operations. This is rarely a choice made on purpose. Building a proper case for deploying surplus cash usually means pulling together cash flow forecasts, receivables timing, and market conditions, then interpreting them without a finance team on hand.
Kastren Fundast was built to shorten that process. Our models read your cash flow history, upcoming commitments, and relevant market signals, then translate them into a plain-English recommendation with a stated level of confidence. You still make the decision. We simply remove the manual analysis that usually stands in the way of it.
Illustrative example only. Actual proportions depend on your cash flow data and risk tolerance.
Rather than separate tools, Kastren Fundast runs predictive modelling, risk monitoring, and reporting from one continuously updated data set, so recommendations stay consistent as your figures change.
Our models study your historical cash flow, seasonal demand patterns, and current account balances alongside publicly available indicators such as Bank of England rate movements. The output is a forecast range for surplus cash over the coming weeks and months, not a single fixed number.
Once cash is deployed, the same models keep monitoring it. If market conditions shift, or your own cash flow forecast changes, you receive a flagged review rather than a silent report buried in a monthly summary. Thresholds are set with you, so alerts reflect your own risk appetite.
Every recommendation and outcome is compiled into a board-ready summary written in plain English, with the underlying figures available for anyone who wants to check the working. Reports are generated automatically, so nobody on your team spends an afternoon formatting spreadsheets.
Transparency is easier to promise than to demonstrate. Here is the process a recommendation goes through before it reaches you, and what happens to it afterwards.
You connect your accounting or banking data, and set the risk boundaries you're comfortable with.
The model produces a recommendation, which is checked against your stated constraints before release.
An independent panel of finance professionals reviews the model's reasoning and flags anything unclear.
The recommendation and its eventual outcome are recorded in the public performance log, in full.
| Date logged | Recommendation type | Outcome recorded | Review status |
|---|---|---|---|
| Q1 entry | Short-term deployment | Within forecast range | Reviewed |
| Q1 entry | Risk threshold adjustment | Flagged and revised | Reviewed |
The methodology behind the models is reassessed on a fixed schedule, and any material change is noted alongside the log so past recommendations remain understandable in the context they were made.
The same underlying models are applied to a handful of recurring situations UK small business owners bring to us.
For businesses holding more operating cash than current activity needs, we identify how much could reasonably be deployed into lower-risk short-term options without disrupting day-to-day liquidity.
Before committing cash to new premises, equipment, or hiring, our models test whether reserves can absorb the commitment under a range of trading scenarios, not just the most likely one.
During periods of interest rate or market movement, recommendations are automatically re-checked, so previously sound decisions are revisited rather than left to age unreviewed.
These are the questions we're asked most often by SME directors evaluating whether to submit their data.
Data is encrypted in transit and at rest, and stored on infrastructure located in the UK to align with your existing data residency obligations. Access is limited to the systems and reviewers directly involved in producing your recommendation. You can request deletion of your data at any time, in line with UK GDPR.
Most businesses connect their accounting software and provide an initial data set within the first week. The model then needs a short period to build a reliable forecast baseline before the first recommendation is issued, typically within two to three weeks of connection, depending on data quality.
Accuracy is measured by comparing each forecast range against the actual outcome once it occurs, and this comparison is what populates the public performance log. Ranges narrow as more of your own data is available, but no forecast is a guarantee, and every recommendation states its confidence level rather than presenting a single fixed figure.
A data consultation walks through your current cash position, what a model-based recommendation would look like for your figures, and how the performance log works, before you commit to anything.