home / skills / cleanexpo / ato / historical_trend_analysis
This skill analyzes multi-year financial data to detect trends, anomalies, and audit risks, enabling proactive tax planning using historical transactional data.
npx playbooks add skill cleanexpo/ato --skill historical_trend_analysisReview the files below or copy the command above to add this skill to your agents.
---
name: historical-trend-analysis
description: Multi-year financial trend comparison, regression detection, and anomaly flagging for tax planning and audit risk assessment
---
# Historical Trend Analysis Skill
Analyses multi-year financial data to identify trends, detect anomalies, and flag year-over-year changes that may indicate audit risk, missed deductions, or tax planning opportunities. Uses Xero historical transaction data and analysis results across multiple financial years.
## When to Use
- Comparing income/expense patterns across 3-5 financial years for trend detection
- Identifying anomalous expense categories that deviate from historical norms
- Detecting revenue growth/decline trends for loss carry-forward planning
- Flagging sudden changes in expense ratios that may trigger ATO benchmarking
- Supporting the Similar Business Test (SBT) with historical consistency evidence
- Assessing amendment worthiness by comparing identified opportunities across FYs
- Providing context for Division 7A compliance (loan balance trends)
- Cash flow forecasting based on historical seasonal patterns
## Analysis Methods
### 1. Year-over-Year (YoY) Comparison
Compare each financial year against the prior year:
| Metric | Calculation | Significance |
|--------|------------|--------------|
| Revenue Growth | (Current - Prior) / Prior × 100 | Loss utilisation, GST threshold |
| Expense Ratio | Total Expenses / Total Revenue | ATO benchmark comparison |
| Category Shift | Category % of Total (current vs prior) | Misclassification detection |
| Net Profit Margin | Net Profit / Revenue × 100 | Loss carry-forward trigger |
### 2. Moving Average
3-year rolling average smooths one-off anomalies:
| Use Case | Window | Alert If |
|----------|--------|----------|
| Revenue trend | 3 years | Current deviates > 20% from average |
| Expense category | 3 years | Category deviates > 30% from average |
| Deduction claims | 3 years | Claims drop > 50% (may indicate missed deductions) |
| Contractor payments | 3 years | Sudden increase > 40% (contractor deeming risk) |
### 3. Anomaly Detection
Flag values that fall outside expected bounds:
| Method | Description | Application |
|--------|------------|-------------|
| Z-score | Standard deviations from mean | Expense category outliers |
| IQR (Interquartile Range) | Values beyond Q1-1.5×IQR or Q3+1.5×IQR | Revenue spikes/dips |
| Percentage change threshold | YoY change exceeding configurable threshold | ATO audit risk triggers |
### 4. Seasonal Pattern Analysis
Identify recurring seasonal patterns in cash flow:
| Pattern | Detection | Use |
|---------|-----------|-----|
| Quarterly spikes | BAS periods showing consistent revenue peaks | Cash flow forecasting |
| Year-end clustering | Expenses concentrated in June | Prepayment detection (s 82KZM) |
| Holiday dips | Consistent revenue drops (Dec/Jan) | Working capital planning |
## Data Sources
| Source | API Endpoint | Fields |
|--------|-------------|--------|
| Historical Transactions | `/api/audit/cached-transactions` | Amount, date, category, account |
| P&L Reports | `/api/xero/reports?reportType=ProfitAndLoss` | Income, expenses by category |
| Year Comparison | `/api/audit/year-comparison` | Pre-computed YoY metrics |
| Analysis Results | `/api/audit/analysis-results` | AI-classified findings per FY |
| Trends | `/api/audit/trends` | Pre-computed trend data |
## Trend Classification
| Trend | Criteria | Tax Implication |
|-------|----------|----------------|
| **Stable Growth** | Revenue growing 5-15% YoY consistently | Healthy; normal deduction patterns |
| **Rapid Growth** | Revenue growing > 30% YoY | May breach SG maximum contribution base; payroll tax threshold risk |
| **Decline** | Revenue falling > 10% YoY | Loss carry-forward planning; consider COT/SBT |
| **Volatile** | Revenue swinging > 25% YoY alternating | Cash flow risk; consider PAYG instalment variation |
| **Flat** | Revenue within ±5% YoY | Stable; check for inflation erosion of real deductions |
| **Seasonal** | Consistent intra-year pattern | Align BAS reporting with cash flow |
## Output Format
```xml
<trend_analysis>
<entity_id>org_456</entity_id>
<analysis_period>FY2020-21 to FY2024-25</analysis_period>
<revenue_trend>
<classification>stable_growth</classification>
<average_yoy_growth>8.3</average_yoy_growth>
<years>
<year fy="FY2020-21" revenue="850000" />
<year fy="FY2021-22" revenue="920000" yoy_change="8.2" />
<year fy="FY2022-23" revenue="1010000" yoy_change="9.8" />
<year fy="FY2023-24" revenue="1080000" yoy_change="6.9" />
<year fy="FY2024-25" revenue="1170000" yoy_change="8.3" />
</years>
</revenue_trend>
<anomalies>
<anomaly>
<category>Motor Vehicle Expenses</category>
<financial_year>FY2023-24</financial_year>
<value>45000</value>
<three_year_average>28000</three_year_average>
<deviation_percentage>60.7</deviation_percentage>
<z_score>2.4</z_score>
<risk>ATO benchmark deviation — motor vehicle expenses unusually high</risk>
<recommendation>Verify classification; may include personal use component</recommendation>
</anomaly>
</anomalies>
<sbt_evidence>
<expense_consistency_score>78</expense_consistency_score>
<top_categories_stable>true</top_categories_stable>
<business_type_consistent>true</business_type_consistent>
<sbt_assessment>likely_satisfied</sbt_assessment>
</sbt_evidence>
</trend_analysis>
```
## Best Practices
- **Minimum 3 years** of data required for meaningful trend analysis
- **Adjust for inflation** when comparing dollar amounts across years (use CPI)
- **Exclude one-off items** from trend calculations (e.g., asset sales, insurance payouts)
- **Normalise for business changes** — merger/acquisition/restructure events invalidate YoY comparison
- **ATO benchmarks are descriptive** — deviations are informational, not normative (AD-6)
- **Use Xero account codes** for consistent category mapping across years
- **Financial year convention**: Always use FY format (e.g., FY2024-25), never calendar year
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.
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.
How many years of data are required?
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.