AI categorization gets most transactions right. But “most” is not “all,” and the edge cases reveal why human judgment remains essential. Here is my collection of transactions that consistently confuse automated systems.
The Classic Edge Cases
The Ambiguous Merchant
AMAZON - Is it:
- Books (entertainment)?
- Office supplies (business)?
- Groceries (food)?
- Electronics (equipment)?
- Returns (negative expense)?
AI cannot know without seeing the receipt.
The Split Transaction
TARGET $127.34 - Actually:
- $45.00 groceries
- $32.00 household supplies
- $25.00 clothing
- $25.34 pharmacy
One transaction, four categories. Automation cannot split without the receipt.
The Reimbursable Expense
DELTA AIRLINES $450 - But:
- Business trip (reimbursable)
- Personal vacation (not)
- Mix of both?
Context determines category, not the merchant.
More Tricky Scenarios
The Cash Back vs Purchase
; Grocery store with cash back
2026-02-01 * "KROGER" "Groceries + cash back"
Expenses:Food:Groceries 85.00 USD
Assets:Cash 40.00 USD
Assets:Bank:Checking -125.00 USD
AI sees one transaction, but it is really two.
The Subscription That Changed
; Netflix raised prices but kept the same merchant ID
2026-01-15 * "NETFLIX" "Monthly subscription"
Expenses:Entertainment:Streaming 15.49 USD ; was $9.99
Assets:Bank:Checking -15.49 USD
Same merchant, different amount - is it an error?
The Medical vs Wellness Gray Area
; Gym membership for physical therapy?
2026-02-01 * "LIFETIME FITNESS" "Monthly membership"
Expenses:Health:Medical 89.00 USD ; or Expenses:Personal:Fitness?
Assets:Bank:Checking -89.00 USD
note: "Doctor prescribed for back rehabilitation"
The Human Advantage
What humans bring that AI cannot:
- Context - You know why you bought something
- Intent - Business or personal purpose
- Memory - “That was the trip where…”
- Judgment - Gray areas require decisions
Questions
- What edge cases have tripped up your categorization?
- Do you maintain a list of merchant-to-category mappings?
- How do you handle the truly ambiguous transactions?