Industry Prediction
Classify a business's industry using NAICS, MCC, and SIC codes.
Industry Prediction classifies a business's industry based on its online presence: website content, business name, address, and any other signals Baselayer discovers. It returns a 6-digit NAICS code, SIC and MCC codes, confidence score, risk level, keywords, and card network risk indicators.
Note: Baselayer uses 2017 NAICS codes across all industry prediction and classification endpoints. The 2017 revision is the basis for all returned codes, filters, and industry-related fields. If you're cross-referencing against another system, confirm it also uses the 2017 standard.
When to use it
- You need to screen applicants against prohibited or restricted industry lists
- You want to validate user-submitted industry classification
- You are running card network compliance checks (Mastercard BRAM, Visa risk tiers)
- You want to classify industry for underwriting or risk-based decisioning
- You need early industry classification before proceeding with a full Business Search
How to request it
Industry Prediction is available on both integration paths.
Via POST /web_presence_requests
POST /web_presence_requestsInclude Order.NaicsPrediction in the options array. Results are returned inline in the response at industry_prediction.
{
"name": "Lucali",
"address": "575 Henry St, Brooklyn, NY 11231",
"options": ["Order.NaicsPrediction"]
}Via POST /searches
POST /searchesInclude Order.NaicsPrediction in the options array. Results are not inline - Baselayer returns a NAICSPredictionRequest tracking object in orderables[]. Fetch the result using the URL in that object, or listen for the NaicsPredictionRequest.completed webhook.
{
"name": "Lucali",
"address": "575 Henry St, Brooklyn, NY 11231",
"options": ["Order.NaicsPrediction"]
}The search response will contain:
{
"orderables": [
{
"type": "NAICSPredictionRequest",
"id": "7f1f1bc6-119d-4613-af28-7f885d37cf2c",
"url": "https://api.baselayer.com/naics_prediction_requests/7f1f1bc6-119d-4613-af28-7f885d37cf2c",
"option": "Order.NaicsPrediction"
}
]
}Fetch the result:
GET /naics_prediction_requests/{id}Via Order.Enhanced
Order.EnhancedOrder.Enhanced on POST /searches includes Industry Prediction automatically alongside Website Analysis, Social Profiles, Reviews, and expanded officer/address discovery. See Online Presence: Basics for when to use Enhanced vs. individual orderables.
Response fields
Core fields
| Field | Type | Description |
|---|---|---|
code | string | 6-digit NAICS code for the predicted industry (e.g., 722511). |
title | string | Official NAICS title for the predicted code (e.g., Full-Service Restaurants). |
accuracy | float | Baselayer's confidence in the prediction, from 0 to 1. ≥ 0.75 is recommended for automated decisioning. |
keywords[] | array | 4–8 keywords extracted from the business's online presence describing core activity. Useful for detecting sensitive terms within permitted industries. |
risk_level | enum | Baselayer's normalized risk assessment: low, medium, or high. |
reasoning | string | Natural-language explanation of why this NAICS code was selected, citing the evidence sources Baselayer used (website content, search results, Google Places, etc.) and occasionally noting runner-up codes considered. Useful for auditing predictions and explaining classifications to reviewers. |
mcc_codes[] | array | Merchant Category Code objects, see below. |
sic_codes[] | array | Standard Industry Code objects, see below. |
mcc_codes[] entry fields
mcc_codes[] entry fields| Field | Type | Description |
|---|---|---|
code | string | 4-digit Merchant Category Code. |
description | string | Human-readable MCC description. |
mastercard_risk | boolean | Whether Mastercard considers this MCC high-risk per their BRAM program. |
visa_risk_tier | string | null | Visa's risk tier: "1" (high risk), "2" (standard), "3" (emerging high risk), or null. |
A single industry prediction may return multiple MCC codes. Check all of them for
mastercard_riskandvisa_risk_tier- a single flagged entry is sufficient to trigger compliance controls.
sic_codes[] entry fields
sic_codes[] entry fields| Field | Type | Description |
|---|---|---|
code | string | 4-digit Standard Industry Code. |
description | string | Human-readable SIC description. |
Interpreting results
Industry Prediction produces two types of signals: industry classification (what does this business do?) and compliance screening (is this business in a permitted sector?).
The accuracy score determines how much confidence to place in the result. Importantly, accuracy is not a measure of whether the prediction is correct - it measures how much data was available and how consistently it pointed in the same direction. A low score means insufficient signal, not a wrong answer. Never auto-decline on a low accuracy score; route to manual review instead.
What drives accuracy: The model draws on website content, social media profiles and online listings, public records and licenses, and business name analysis. A business with a detailed website, consistent information across platforms, and an industry-specific name will typically score higher than one with minimal online presence. The more signals available and the more they agree, the higher the accuracy.
For the full decisioning framework - including confidence thresholds, prohibited industry list structure, keyword scanning, and card network compliance - see Industry Prediction section in the Best Practices guide: tiered review policy, NAICS hierarchy strategy, keyword watchlists, and MCC handling.
The key fields to evaluate in every application:
accuracy- apply automated decisioning at≥ 0.75; require manual review below that thresholdcode- check against your prohibited and restricted NAICS lists at 2-digit, 4-digit, and 6-digit levelskeywords[]- scan against your sensitive keyword watchlist for restricted activities within otherwise permitted sectors; a complete recommended keyword list is available from your account representativemcc_codes[].mastercard_riskandmcc_codes[].visa_risk_tier- check all entries; any flagged MCC triggers card network compliance controlsreasoning- read when a prediction looks surprising, when a reviewer needs justification, or when debugging why a borderline classification went one way over another. It surfaces the evidence Baselayer relied on and occasionally runner-up codes that were considered.
Related guides
- Online Presence: Basics — integration path decision guide and data model
- Online Presence: Best Practices — full decisioning framework
- Industry Prediction Accuracy — how confidence scores are calculated
- Online Presence: Response Reference — match values, match sources, and response shape by integration path
- Website Analysis — request alongside Industry Prediction for best accuracy
- Industry Prediction API Reference — full endpoint documentation
