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Forecast Monitoring & Variance Analysis

Status: Shipped

Domain: Variance Analysis Linear Projects: None tracked

What It Does

Palm enables treasury teams to compare forecasts against actuals and understand why variances occurred. This is the critical feedback loop that turns forecasting from a guessing game into a learning system.

The platform shows where forecasts were accurate and where they missed, with the ability to drill down from summary variances to the specific transactions that drove them. This transparency enables informed decisions about forecast configuration and creates accountability across the organization.

Variance analysis is essential for building trust in forecasts - without understanding why misses happened, teams can't improve and stakeholders remain skeptical.

Capabilities

Capability Status Notes
Forecast vs actual comparison Shipped Side-by-side view of predicted vs actual
Category-level variance Shipped See variance by payment type
Entity-level variance Shipped See variance by legal entity
Drill-down to transactions Shipped Navigate from variance to underlying transactions
Variance trending Shipped Track variance over time
Actuals vs direct forecast Shipped Compare actuals to Palm's direct forecast
Actuals vs FP&A forecast Not Shipped Compare actuals to FP&A budget forecast

Jobs Fulfilled

From variance-analysis/jobs.md

1. Investigate variances between forecast and actuals

Desired Outcomes Addressed: - [x] Minimize the time to identify root causes of forecast variances - [x] Reduce manual effort to drill down from summary to transaction level - [x] Increase the ability to make informed model configuration decisions - [x] Increase the speed of identifying variances between forecast sources

How Palm Addresses This: - Direct drill-down from variance to transactions - Category-level breakdown shows which payment types are causing misses - Variance comparison across forecast sources - Historical variance tracking for trend analysis

2. Confirm treasury-initiated payments went through daily

Desired Outcomes Addressed: - [x] Minimize the time to identify delayed or failed payments - [ ] Reduce the risk of missing critical payment failures (partial - requires user to check) - [x] Increase confidence that funded accounts have correct balances

How Palm Addresses This: - Expected vs actual comparison highlights missing movements - Transaction visibility confirms payments - Variance flags unexpected differences

Pain Points Addressed

Pain Point Addressed? Notes
No formal variance analysis process exists Yes Built-in variance views
No market solution for variance analysis Yes Palm provides this capability
Data validation consumes time before analysis Partial Data quality still requires attention
Customer inflows variance is a "black box" Partial Can see what happened, not always why
Understanding the "why" requires manual investigation Partial Drill-down helps, but external factors still need research

What's NOT Included (Yet)

  • FP&A vs actuals variance analysis (compare actuals to budget forecasts)
  • Automatic variance attribution (assumption changes vs actuals variance)
  • Permanent vs timing difference tracking
  • Variance alerts/notifications
  • Variance explanations/annotations
  • Holiday calendar integration for explaining zero activity days
  • AP data integration for complete variance picture

How It Works (Technical)

TODO: Fill in via codebase analysis

Component Technology Notes
Variance calculation
Drill-down navigation
Historical storage
API endpoints

Key files/services: - TBD

Last updated: 2026-02-17