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

Include 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

Include 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.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

FieldTypeDescription
codestring6-digit NAICS code for the predicted industry (e.g., 722511).
titlestringOfficial NAICS title for the predicted code (e.g., Full-Service Restaurants).
accuracyfloatBaselayer's confidence in the prediction, from 0 to 1. ≥ 0.75 is recommended for automated decisioning.
keywords[]array4–8 keywords extracted from the business's online presence describing core activity. Useful for detecting sensitive terms within permitted industries.
risk_levelenumBaselayer's normalized risk assessment: low, medium, or high.
reasoningstringNatural-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[]arrayMerchant Category Code objects, see below.
sic_codes[]arrayStandard Industry Code objects, see below.

mcc_codes[] entry fields

FieldTypeDescription
codestring4-digit Merchant Category Code.
descriptionstringHuman-readable MCC description.
mastercard_riskbooleanWhether Mastercard considers this MCC high-risk per their BRAM program.
visa_risk_tierstring | nullVisa'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_risk and visa_risk_tier - a single flagged entry is sufficient to trigger compliance controls.

sic_codes[] entry fields

FieldTypeDescription
codestring4-digit Standard Industry Code.
descriptionstringHuman-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 threshold
  • code - check against your prohibited and restricted NAICS lists at 2-digit, 4-digit, and 6-digit levels
  • keywords[] - scan against your sensitive keyword watchlist for restricted activities within otherwise permitted sectors; a complete recommended keyword list is available from your account representative
  • mcc_codes[].mastercard_risk and mcc_codes[].visa_risk_tier - check all entries; any flagged MCC triggers card network compliance controls
  • reasoning - 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.

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