Why this matters
Auto categorization uses intelligent algorithms to automatically suggest transaction categories based on description patterns, historical data, and entity-specific rules. This saves significant time on repetitive categorization tasks while maintaining accuracy and consistency across your financial records.
Prerequisites
- You must be logged in as an Owner or Administrator
- Have transactions imported into the system
- Some manual categorization history helps improve suggestions
- Understand the available transaction types and groups
How Auto Categorization Works
The system uses a multi-layered approach to provide accurate suggestions:
1. Safety Override Rules
Highest priority rules that override all other suggestions:
- Specific entity-based safety rules
- Compliance-related categorizations
- Critical business logic requirements
2. Entity History Analysis
Learns from your previous categorizations:
- Analyzes patterns in your transaction descriptions
- Remembers how you've categorized similar transactions
- Builds entity-specific categorization rules
- Improves accuracy over time
3. System Pattern Matching
Uses built-in patterns for common transactions:
- Bank fees and charges
- Tax payments and ATO transactions
- Salary and payroll transactions
- Common business expenses
- Investment-related transactions
4. Global History Analysis
Leverages system-wide categorization patterns:
- Common patterns across all users
- Industry-standard categorizations
- Popular merchant categorizations
5. Fallback Pattern Matching
Basic pattern recognition for unmatched transactions:
- Keyword-based suggestions
- Amount-based heuristics
- Generic category suggestions
Using Auto Categorization
1. Get AI Suggestions
When categorizing individual transactions:
- Click the lightbulb icon next to any category field
- System analyzes the transaction description and amount
- Provides suggested intent, type, and group
- Shows confidence level for the suggestion
2. Review Suggestions
Evaluate the auto-generated suggestions:
- High Confidence - Usually accurate, review and accept
- Medium Confidence - Likely correct, but verify details
- Low Confidence - May need manual adjustment
3. Accept or Modify
Handle the suggestions appropriately:
- Accept - Click to apply the suggestion
- Modify - Adjust the suggestion as needed
- Reject - Ignore and categorize manually
4. Provide Feedback
Help improve the system:
- Accept good suggestions to reinforce patterns
- Correct bad suggestions to improve accuracy
- System learns from your corrections
Common Auto Categorization Patterns
Banking & Fees
- Bank fees → Operating expenses
- Account fees → Operating expenses
- Monthly fees → Operating expenses
- Transaction fees → Operating expenses
Tax & ATO
- Tax payments → Tax expenses
- ATO payments → Tax expenses
- GST payments → Tax expenses
- Withholding tax → Tax expenses
Salary & Payroll
- Salary payments → Payroll expenses (for companies) or income (for individuals)
- Wage payments → Payroll expenses
- Superannuation → Superannuation expenses
Business Expenses
- Office supplies → Operating expenses
- Rent payments → Rent expenses
- Utilities → Operating expenses
- Professional services → Professional fees
Investment Transactions
- Share purchases → Investment expenses
- Dividend payments → Investment income
- Interest payments → Interest income/expense
- Management fees → Investment expenses
Improving Auto Categorization Accuracy
1. Consistent Manual Categorization
- Categorize similar transactions consistently
- Use the same categories for recurring transactions
- System learns from your patterns
2. Provide Feedback
- Accept good suggestions promptly
- Correct inaccurate suggestions
- System improves based on your feedback
3. Use Descriptive Transaction Names
- Clear, descriptive transaction descriptions help
- Avoid generic descriptions like "Payment"
- Include merchant names and purpose
4. Regular Review
- Review auto-categorized transactions periodically
- Make corrections as needed
- System accuracy improves over time
Bulk Auto Categorization
1. Bulk Apply Suggestions
For multiple similar transactions:
- Select multiple uncategorized transactions
- Use bulk categorization tools
- Apply consistent categories across similar transactions
2. Pattern-Based Bulk Categorization
- Identify recurring transaction patterns
- Create rules for similar transactions
- Apply categories to matching transactions
Expected Results
- Faster transaction categorization process
- Consistent categorization across similar transactions
- Reduced manual data entry time
- Improved accuracy over time as system learns
- Better compliance with consistent categorization
Example
Auto Categorizing a Bank Fee:
- Transaction: "Monthly Account Fee - Commonwealth Bank"
- Amount: $10.00
- System Suggestion:
- - Intent: Expense
- - Type: Operating
- - Group: Bank Fees
- - Confidence: High (95%)
- Action: Accept suggestion
Troubleshooting
- No suggestions appearing → Check that transaction has a clear description. Very generic descriptions may not trigger suggestions.
- Inaccurate suggestions → Provide feedback by correcting the categorization. System learns from corrections.
- Low confidence suggestions → Review and manually adjust as needed. System accuracy improves with more data.
- Suggestions not improving → Ensure you're providing consistent feedback and categorizing similar transactions the same way.
What Can Go Wrong
- Over-reliance on auto suggestions → Always review suggestions, especially for unusual or large transactions
- Inconsistent manual corrections → May confuse the learning algorithm and reduce accuracy
- Ignoring low confidence suggestions → May miss opportunities to improve the system through feedback
- Not reviewing auto-categorized transactions → Could lead to incorrect categorizations going unnoticed
Related Guides