> ## Documentation Index
> Fetch the complete documentation index at: https://docs.spade.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Understanding match scores

## Overview

Spade’s unique approach to transaction enrichment involves matching each transaction to a real merchant entity (”counterparty”) in our database, and returning granular merchant, category, and location information.

Matching accuracy is critical, and we have developed a “match score” model that assesses how confident we are that each match is correct. This is a machine learning model that is constantly fine-tuned to create more and more accurate predictions.

In addition to using this model to filter out matches that don’t meet a quality bar, we surface results via our API to allow you to make decisions about when and how to use our data.

## How do I interpret match scores?

Match scores range in value from 0.00 to 100.00 — the higher the match score, the higher the likelihood that a counterparty or location returned was the one involved in a transaction. We return two types of match scores: counterparty match score and location match score. This is not a probability of a match being correct, but simply a representation of our confidence. Over 99% of our matches are accurately scored by the model, despite most confidence scores falling in the 85-95 range.

Note, the returned model score is not a probability of a match. While these numbers are not representative of probabilities, they are directionally relevant to the probability of match accuracy. You can be more confident in a 99 score than a 90 score, more confident in a 90 score than an 80 score, and so on.

## What is the counterparty match score?

* Counterparty match score is an assessment of how confident we are that a specific counterparty we return is the one involved in a transaction (e.g., how likely it is that `WALMART002191BRYANOH` is a transaction occurring at Walmart)
* Counterparty match scores appear in the `counterparty` portion of the response.

## What is the location match score?

Note: location match scores are only available on card enrichments.

* Location match score is an assessment of how confident we are that a specific location we return is the one involved in a transaction (e.g., how likely it is that `WALMART002191BRYANOH` is a transaction occurring at the Walmart at `1215 S Main Street, Bryan, Ohio`).
* Location match scores are only returned when we match on a location and `transactionInfo.spendingInfo.channel.value` is `physical` (the location match score is `null` for digital transactions)
* Location match scores appear in the location portion of the enrichment

## When is a match score not returned?

A `null` match score can mean:

* No matching counterparty or location was found in our database.
* Match scores are not enabled for your product package.
* Location scoring is unavailable: location scores are `null` for transfer and universal enrichments, and for digital card transactions.

Use the counterparty or location `id` to determine whether that entity was matched; a `null` score alone does not mean there was no match. Counterparty and location scores are independent, so one may be numerical while the other is `null`.

## How should match scores be used?

Match scores can help you make decisions about how to use enriched data in your systems. For example, for decision-making processes (e.g. card authorization flows or fraud assessments) we suggest setting a higher threshold on counterparty match score, whereas for customer analysis, budgeting, or UX/UI improvements, a lower counterparty score threshold is sufficient.

To get a match score recommendation for your use case reach out to [sales@spade.com](mailto:sales@spade.com).

\**Note that as our model improves and becomes more accurate, these guidelines may be adjusted.*


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