Rating Step

You have a set of rating factors - area, age band, NCD level - and a table of relativities for each. The Rating Step looks up the right factor for each row and combines them into a single multiplier (or sum). This is how you build a traditional multiplicative or additive rating structure.

Spreadsheet equivalent

Like VLOOKUP or INDEX/MATCH in Excel, but it handles multi-dimensional lookups and combines the results automatically.

When to use

• Building a traditional multiplicative or additive rating structure.
• Recreating factor tables from a spreadsheet or another rating tool.
• Looking up relativities based on one, two, or three dimensions.
• Use Banding first if your tables expect banded inputs rather than raw values.

This node accepts a single input.

Config	Description
tables	Required. List of rating tables
operation	Required. How to combine factors across tables: "multiply", "add", "min", or "max"
combinedColumn	Name of the column containing the combined result. If omitted, individual factor columns are still created but no combined column is produced.

Each table has:

Field	Description
name	Required. Table name
factors	Required. Input columns to match on (up to 3 for multi-way lookups)
outputColumn	Required. Column name for this table's looked-up value
defaultValue	Value used when the input doesn't match any entry in the table (e.g. an area code you haven't mapped)
entries	Required. The factor table. In JSON sidecars, factor values are nested keys and the leaf is the looked-up value.

A one-way table maps a single column. A two-way table maps two columns. In the sidecar JSON, a one-way area factor and a one-way age factor look like this:

{
  "tables": [
    {
      "name": "Area Factor",
      "factors": ["area"],
      "outputColumn": "area_factor",
      "defaultValue": "1.0",
      "entries": {
        "London": 1.25,
        "Manchester": 1.10,
        "Rural": 0.85
      }
    },
    {
      "name": "Age Factor",
      "factors": ["age_band"],
      "outputColumn": "age_factor",
      "defaultValue": "1.0",
      "entries": {
        "18-25": 1.40,
        "26-65": 1.00,
        "65+": 1.15
      }
    }
  ],
  "operation": "multiply",
  "combinedColumn": "location_age_factor"
}

For two-way tables, each factor adds one nesting level in the order listed in factors. For three-way tables, the sidecar matches the editor: the third factor is the outer dropdown, the second factor is the column group, and the first factor is the row key. With factors set to ["vehicle_age_band", "cover_type", "channel"], entries nest as channel -> cover_type -> vehicle_age_band -> value. When Haute loads the sidecar, it expands these maps back into row entries with one key per factor plus value for the editor, execution, and trace.

Before and after:

BEFORE                                AFTER
| area       | age_band |            | area   | age_band | area_factor | age_factor | location_age_factor |
|------------|----------|            |--------|----------|-------------|------------|---------------------|
| London     | 18-25    |      →     | London | 18-25    | 1.25        | 1.40       | 1.75                |
| Rural      | 26-65    |            | Rural  | 26-65    | 0.85        | 1.00       | 0.85                |
| Manchester | 65+      |            | Man... | 65+      | 1.10        | 1.15       | 1.265               |

String matching

Factor values are matched as strings. If your data has "London" but your table has "london", it won't match. Use a Polars node upstream to normalise casing if needed.