Step 4: Categorize Transactions

Map Merchant Category Codes (MCC) to human-readable categories for analytics. This replaces DataStage Switch/Case stages with Expanso's match expression.

The Goal

Convert numeric MCC codes like 5411 into readable categories like GROCERY for:

• Dashboard filtering
• Spending analytics
• Fraud detection rules

DataStage Equivalent

In DataStage, categorization typically requires:

1. Switch/Case Stage with dozens of conditions
2. Lookup Table maintained in a separate database
3. Default handling for unknown codes

Expanso simplifies this with inline match expressions that use pattern matching.

Implementation

Add the categorization processor after account masking:

step-4-categorize.yaml

pipeline:
  processors:
    # Steps 1-3 from previous...
    
    # Step 4: Categorize transactions by MCC
    - mapping: |
        root = this
        
        # Map MCC code prefixes to categories
        let mcc = this.MERCHANT_CATEGORY_CODE.string()
        
        root.transaction_category = match mcc {
          this.has_prefix("54") => "GROCERY",
          this.has_prefix("55") => "AUTOMOTIVE",
          this.has_prefix("58") => "RESTAURANT",
          this.has_prefix("59") => "RETAIL",
          this.has_prefix("47") => "TRANSPORTATION",
          this.has_prefix("40") || this.has_prefix("41") => "TRAVEL",
          this.has_prefix("60") || this.has_prefix("61") => "FINANCIAL",
          this.has_prefix("80") => "PROFESSIONAL_SERVICES",
          _ => "OTHER"
        }

Understanding the Code

Expression	What It Does
.string()	Convert MCC to string for pattern matching
match mcc {...}	Pattern matching expression
this.has_prefix("54")	Check if string starts with "54"
||	Logical OR for multiple prefixes
_ => "OTHER"	Default case (underscore = wildcard)

MCC Code Reference

Common MCC prefixes and their meanings:

Prefix	Category	Examples
54xx	Grocery	Supermarkets, food stores
55xx	Automotive	Gas stations, auto dealers
58xx	Restaurant	Fast food, dining
59xx	Retail	Department stores, misc retail
47xx	Transportation	Airlines, rail, taxi
40-41xx	Travel	Hotels, lodging
60-61xx	Financial	Banks, insurance
80xx	Professional	Legal, medical, accounting

Expected Output

Input:

{
  "MERCHANT_CATEGORY_CODE": "5411",
  "MERCHANT_NAME": "ACME Electronics GmbH",
  ...
}

Output:

{
  "MERCHANT_CATEGORY_CODE": "5411",
  "MERCHANT_NAME": "ACME Electronics GmbH",
  "transaction_category": "GROCERY",
  ...
}

Production Considerations

Exact MCC Matching

For precise MCC codes instead of prefixes:

let mcc_map = {
  "5411": "GROCERY_SUPERMARKET",
  "5412": "GROCERY_CONVENIENCE",
  "5541": "GAS_STATION",
  "5542": "GAS_AUTOMATED"
}

root.transaction_category = $mcc_map.get(this.MERCHANT_CATEGORY_CODE.string()).or("OTHER")

External Category Lookup

Load categories from a file or API:

# Load MCC mappings from file at startup
input:
  file:
    paths: ["mcc-categories.json"]
    
# Or fetch from API
- http:
    url: "https://api.internal/mcc-categories"
    verb: GET

Subcategories

Add hierarchical categorization:

root.category_level1 = match mcc {
  this.has_prefix("5") => "RETAIL",
  this.has_prefix("4") => "TRAVEL",
  this.has_prefix("6") => "FINANCIAL",
  _ => "OTHER"
}

root.category_level2 = match mcc {
  this.has_prefix("54") => "GROCERY",
  this.has_prefix("58") => "DINING",
  _ => root.category_level1
}

Flagging High-Risk Categories

Add fraud indicators:

root.high_risk = match mcc {
  this.has_prefix("59") => true,   # Misc retail
  this.has_prefix("79") => true,   # Recreation
  this.has_prefix("76") => true,   # Misc services
  _ => false
}

---

Next Step

📖 Step 5: Standardize Schema →