historical_trend_analysis_skill

This skill analyzes multi-year financial data to detect trends, anomalies, and audit risks, enabling proactive tax planning using historical transactional data.
  • TypeScript

3

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

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Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill cleanexpo/ato --skill historical_trend_analysis

  • SKILL.md6.0 KB

Overview

This skill performs multi-year financial trend comparison, regression detection, and anomaly flagging to support tax planning and audit risk assessment. It ingests historical transaction and P&L data and produces year-over-year metrics, moving-average signals, and categorized risk flags. Outputs are designed to support ATO benchmarking checks, Similar Business Test evidence, and amendment prioritisation.

How this skill works

The skill compares 3–5 financial years using YoY percentage changes, rolling averages, and statistical anomaly detectors (Z-score, IQR). It classifies revenue and expense trends, identifies category-level deviations, and attaches recommended actions for each flagged item. Seasonal pattern analysis and configurable thresholds let you tune sensitivity for audit-risk and tax-opportunity detection.

When to use it

  • Comparing income and expense patterns across 3–5 financial years for trend detection
  • Identifying anomalous expense categories that deviate from historical norms
  • Assessing revenue decline or growth for loss carry‑forward and PAYG planning
  • Flagging sudden changes in expense ratios that may trigger ATO benchmarking scrutiny
  • Supporting Similar Business Test (SBT) evidence or Division 7A balance trend checks

Best practices

  • Use a minimum of three full financial years of clean data before interpreting trends
  • Adjust historical amounts for inflation (CPI) when comparing dollar values across years
  • Exclude one-off events (asset sales, insurance payouts) or normalise them before analysis
  • Map accounts consistently using Xero account codes to avoid category drift
  • Document business structural changes (mergers, acquisitions) and exclude affected periods

Example use cases

  • Generate a FY-by-FY revenue trend report that classifies stable, rapid, flat, volatile, or declining growth
  • Flag motor vehicle or contractor expense spikes for classification review and potential private-use adjustments
  • Produce SBT support evidence by scoring expense consistency and top-category stability
  • Prioritise amendment candidates by comparing missed-deduction patterns across multiple years
  • Forecast cash flow seasonality for BAS timing and PAYG instalment adjustments

FAQ

At least three full financial years for meaningful rolling averages and anomaly detection; five years improves confidence.

What thresholds trigger an alert?

Defaults include >20% deviation from 3‑year revenue average and >30% for category deviations, but thresholds are configurable to match client risk tolerance.

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