The market needed something better.
The global cash-management software market is worth ~$13 billion and growing at ~15% a year — yet the largest players serve fewer than 10,000 clients each, implementations run into seven figures and 12–18 months, and mid-market teams are priced out entirely. The gap between what finance leaders need and what exists has never been wider.
Most cash forecasts are wrong — at significant cost.
Not our claims — the industry's own numbers.
Typical accuracy of manual 13-week cash forecasts.
AFP 2025Share of organisations that hit 90% forecast accuracy at 30 days.
HighRadius 2024Of companies miss their free-cash-flow target by more than 10%.
EY-Parthenon 2024Of treasurers cite poor data quality as their biggest obstacle.
PwC 2025Figures from published industry research; sources named per figure. We don't guarantee a minimum accuracy — every dataset is different, which is exactly why the pilot proves the value on your data before you commit.
Existing systems were built for a pre-AI world.
They're being retrofitted — and it shows.
Siloed by design
Traditional TMS and EPM stacks were built as separate tools. Cash, forecasting and risk live in different platforms — requiring manual reconciliation between them and making cross-module insight impossible.
Painful to implement
Enterprise TMS implementations routinely run 12–18 months and seven figures. By the time you're live, the business has changed. Mid-market teams are priced out entirely.
AI as an afterthought
Incumbents are racing to bolt AI features onto legacy architectures. The result is intelligence that doesn't connect across modules — a chatbot in the corner of a system that still can't see the whole picture.
Fragmented, unharmonised data
Decades of bank portals, ERP exports and spreadsheet archaeology left treasury data scattered across incompatible formats. Every analysis starts with reconciliation instead of insight — and the AI bolted on top inherits the mess.
Started from a different place.
AI at the core, out to every edge
finsait was designed AI-first, not plastered on top. One intelligence layer runs from the core out to every edge of the loop — position, forecast, risk, decision. Models learn across your data, surface anomalies, and improve continuously. It's not a feature; it's the architecture. And it never makes a decision for you — it exists to make you faster, sharper and harder to argue with when you make yours.
One loop, not modules
Your position already knows your forecast; your forecast already knows your risk. There's nothing to reconcile, because there was never more than one source of truth.
Trust, by construction
Every number is traceable to its source, and every view can be reconstructed as it stood on any date. Decisions are saved with their full context attached. Built for the scrutiny of auditors, boards and regulators — not retrofitted for it.
Live in weeks, hands-on
No seven-figure implementation programme. We bring your data in together, models start learning immediately, and the loop is live from day one — with the team that built the platform doing the onboarding. And the timing works in your favour: open-banking access, ISO 20022 harmonisation and modern integration platforms finally make one clean source of truth practical. We build on that from day one — instead of retrofitting around twenty years of fragmented formats.
Built to hold your most sensitive numbers.
Treasury data is the crown jewels. The foundations are not negotiable.
Tenant isolation
Every client's data is isolated at the database layer with row-level security — enforced by the platform, not by convention.
EU hosting, GDPR native
Hosted in the EU on Google Cloud. GDPR compliance designed in from the first line — see our Privacy & Data Policy.
Enterprise authentication
Auth0-backed sign-in with invite-only access during the pilot phase. Encryption in transit and at rest.
Your data stays yours
Client data is never shared. Future benchmarking uses anonymised, aggregated ratios only — always opt-in.
Cash is cash in every industry. The band below is the full global sector taxonomy — not a shortlist.
Built by people who turn models into decisions.
Marcus Martinsson
M.Sc. Theoretical Physics. Over 20 years in strategy consulting, leading data-science and financial-intelligence programmes in executive and senior roles at Accenture, NTT and PwC — focused on translating quantitative models into direct P&L impact for finance and treasury teams. One of his finance-analytics innovations earned an acknowledgement at the Adam Smith Awards, corporate treasury's most prestigious industry recognition.
Judge it on your own numbers.
Test the engines in your browser, visit our playground to walk the real platform, or join the pilot and see the loop run on your data.