Case Study:
Google’s understanding of the Finance sector

March 2022

Executive Summary

Google uses Natural Language Processing (NLP) routines to convert its understanding of text into topics and concepts (Entities). We looked out how much understanding Google misses using this approach in the Finance sector.

InLinks analyzed the page content of the top 10 Google results (in the US Market) ranking for the phrase wire transfer and compared the Named Entities recognized by Google’s NLP API with the proprietory routines designed by Inlinks to uncover gaps in Google’s machine learning in the Finance sector

The results showed that 20.5% of entities seen on the results in the Finance sector SERPS (search engine Results pages) were positively identified by Google.

This compares to 16.8% average across all analyzed industry sectors.

How the Sector Compares

Different sectors tend to be analyzed with a different degree of accuracy by the search engines. This stems from two main challenges.

  1. the more demand there is by consumers within a given sector, the more the need for search engines to apply more sophisticated entity recognition to better answer user queries.
  2. the more sophisticated the industry is in creating search-friendly content, the more search engines can surface topics and concepts.


Finance Industry Google Analysis InLinks Analysis
Avg. Number of words per page 1099
Avg. Number of Topics per page 8.9 43.4
Benchmark - Avg. nb of entities/page (all sectors) 8.2 50.5
Of which, Topic types are:    
- Persons 3 1
- Organizations 26 19
- Cities & geo. areas 30 29
- Concepts 14 213
Semantic Density   5.9

How the research was conducted

Google’s Search API returned URLs for the following sites competing for this phrase:

#en.wikipedia, #acgov, #bankofamerica, #intermexonline, #investopedia, #nerdwallet, #thebalance, #wellsfargo, #westernunion

The texts of each page are then sent to Google’s NLP API, in order to determine which entities are identified by the search engine. These are important for search since Google is then able to link these to its Knowledge Graph to feed its services including Google Discover, Google search, Voice Search and Google News. (Although, correct identification does not guarantee inclusion in these results)

Here is first of all the synthesis of the results returned by Google:

  • 29 geographical areas, including United States (detected 6 times) Puerto Rico (2) Guatemala (1)
  • 26 organizations, including Western Union (4) Clearing House Interbank Payments System (2) Wells Fargo (2)
  • 14 concepts, including Fedwire (3) International Bank Account Number (2) Bank code (1)
  • 3 persons, including SWIFT (2) Money transmitter (1) Tax collector (1)
  • 1 city, including Oakland, California (1)

Errors in Google’s Detection Rate

Here are some errors in the categorization of entities:

  • Florida categorized as a concept instead of geographical area
  • Great_Britain categorized as a concept instead of geographical area
  • California categorized as a concept instead of geographical area

Most important entities (provided by InLinks), compared to those identified by Google:

  • Money (seen 9 times) => NOT detected by Google
  • Wire transfer (9) => detected by Google
  • Payment (8) => NOT detected by Google
  • Bank (7) => detected by Google
  • Financial transaction (7) => NOT detected by Google
  • Beneficiary (6) => NOT detected by Google
  • United States (6) => detected by Google
  • Domestication (6) => NOT detected by Google
  • Service (economics) (5) => NOT detected by Google
  • Location (5) => NOT detected by Google
  • Electronics (5) => NOT detected by Google
  • Bank account (5) => NOT detected by Google
  • Fee (5) => NOT detected by Google
  • Currency (5) => NOT detected by Google
  • Balance transfer (4) => NOT detected by Google

How can the Finance Industry benefit from this report?

By understanding where Google is failing to recognize important concepts within the industry, there is an opportunity for companies in the sector to write clearer content that Google can better understand.

Another option is to explicitly state these concepts in Webpage schema for machine learning algorithms to take into account. This would require using and the "about" and mentions" properties for important concepts such as Money, Bank account, Currency, Balance transfer.


Internal linking of topics topages dedicated to each important topic will also help to reduce cannibalisation of content in Google’s understanding of contect within your content.

In Summary

Google's understanding of the Finance market, based on its NLP algorithms remains limited at 20.5% for this industry. Businesses either need to improve their schema or make their content more understandable by Google to improve its level of understanding.

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