A $3B FinTech replaced spreadsheet-based cash flow forecasting with AI-driven predictive models, eliminating 12 annual liquidity crises and $12M in emergency borrowing costs.
This company had achieved unicorn status on the strength of its technology, but its finance infrastructure was fundamentally spreadsheet-driven. The treasury team of 3 analysts spent 40 hours every week manually pulling data from SAP S/4HANA, Workday Financial Management, and seven banking APIs — stitching it together in Excel to produce a weekly cash flow forecast.
The problem: by the time the forecast was published every Monday morning, the underlying data was already 5 days stale. A payment run that hit on Friday afternoon wouldn't appear in the forecast until the following Monday. This meant the treasury team was flying blind on short-term cash position.
The consequence was 12 liquidity events in 2023 — moments where cash on hand dropped below the minimum operating threshold, requiring emergency draws on the company's revolving credit facility. Each emergency draw carried a 4.5% interest rate and triggered covenant notifications to investors. The CFO described it as "structurally embarrassing for a company that calls itself a FinTech."
Flowtaris AI built a real-time data ingestion layer connecting all 23 data sources into a unified treasury data model. SAP S/4HANA pushed GL entries in near-real-time via event streaming. Workday Financial Management connected via API for payroll and benefits cashflows. Seven banking connections established via SFTP and API for balance and transaction feeds.
Four specialized prediction models were trained on historical data: AR model (collections timing based on customer payment patterns), AP model (payment run optimization and vendor payment timing), Payroll model (bi-weekly and monthly payroll, benefits, taxes), and Tax model (estimated payments and VAT/GST flows). Each model was validated against 6 months of held-out historical data before deployment.
For 30 days, the AI system ran in parallel with the existing manual spreadsheet process. Every Monday, the analysts produced their traditional forecast, and the AI produced its forecast. Results were compared against actual cash positions. The AI achieved 91% accuracy vs the manual team's 58% across all 4 weeks of shadow mode.
Full transition to AI-driven forecasting. Manual weekly process replaced by daily automated forecast updates pushed directly to the CFO dashboard. Automated liquidity alerts configured — any projected cash position below threshold triggers immediate Slack notification to CFO, Treasurer, and CFO's EA with specific recommended action.
23 live data sources feeding into a unified treasury model. SAP event streaming, Workday API, 7 banking connections — all with < 15-minute latency and automated data quality validation.
4 domain-specific models (AR, AP, Payroll, Tax) using ensemble methods combining gradient boosting with time-series neural networks. Models retrain weekly on new actuals.
Proactive monitoring of projected cash positions against configurable thresholds. Immediate multi-channel alerts (Slack, email, SMS) to CFO and treasury team when intervention is needed.
Automated monitoring of all 23 data feeds with anomaly detection. Missed feeds trigger immediate alerts before stale data reaches forecast models — zero silent failures.
Eight months after go-live, the results were externally validated as part of the company's annual treasury management review. The most significant outcome was the elimination of all liquidity events — zero emergency credit facility draws since deployment.
The $12M figure represents: $8.2M in avoided emergency borrowing costs (principal not drawn), $2.1M in reduced treasury buffer requirements (capital freed for operations), $1.1M in reduced covenant notification fees and banking relationship costs, and $600K in FP&A productivity gains from real-time data availability.
"For the first time in our company's history, I have 90%+ cash visibility 30 days out. I haven't touched the revolving credit facility in 8 months. We went from 12 liquidity emergencies a year to zero. That's not just an operational improvement — that's a fundamentally different way to run treasury."
