A model purpose-built for corporate cash timing, not general finance prediction.
Generic time-series models fail on treasury data because cash flow timing is highly episodic — payroll, quarterly taxes, large AP clusters. Cashvyne's engine is trained on treasury-specific patterns.
AI Engine
What makes it different from a generic time-series model
Event-Anchored Sequences
Payroll, tax deposits, large vendor payments are episodic events — not smooth trends. Cashvyne's model anchors on named event types and learns their timing variance per entity.
Customer Payment Behavior
AR collection timing varies by customer. The model learns that Customer A pays Net 30 invoices on Day 28 while Customer B averages Day 35 — and builds those biases into forecasts.
Daily Recalibration
Each morning, Cashvyne compares yesterday's forecast to actuals. Persistent variance in a category (e.g. AR collections) triggers automatic model weight adjustment for that entity.
Entity-Level Isolation
Each legal entity has its own model instance — trained on that entity's history. Subsidiary A's erratic payment pattern does not contaminate Subsidiary B's forecast accuracy.
Explainable Variance
When forecasts deviate from actuals, Cashvyne attributes the variance to named causes — not a black-box delta. Treasurers can review exactly why Week 3 came in higher than projected.
No Cross-Customer Training
Your data is never used to train models for other Cashvyne customers. Each customer's model runs in isolation against that customer's own historical data.
~94% accuracy at the 30-day entity-level horizon
Measured across customers using Cashvyne since early 2026. Average absolute deviation between forecast and actuals, at the weekly entity-level bucket.
Put the AI engine to work on your treasury data.
Setup and initial training takes less than 48 hours with your AR/AP and bank history.
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