Webclass 134: Using python to learn to categorize your 3rd parties

Q1. Who said, “AI is not going to replace you. People who get good at using it quickly will.”?

Scott Galloway

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Donald Trump

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Q2. What can AI help us to do?

Integrate data from Web, do classification predictions, predict trends

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Understand the business context, consider the risk with relation to the global economic climate as well as the strategic direction that the company wants to take

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Q3. Why might we want to crawl the web?

Gather information for creating models more quickly, compare system data to external sources (such as pricing information), look up information about 3rd parties, collect information for sentiment analysis

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Predict a classification (anomaly or not anomaly) based on a historic set of data

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Q4. Why might we be interested in using AI to categorize third party names as individuals rather than organizations?

If we have a very large list of 3rd parties and we want to isolate the individuals for a specific set of audit questions

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If we don't have many 3rd parties and we want to understand why

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Q5. What type of information can we get from government websites concerning our third parties?

Confirmation that the tax code information is correct, check the status of the organisation, check the address of the organisation

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Confirmation that the organisation is not found on a PEP or OFAC list

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Q6. Which python library did we use to scrap DuckDuckGo?

Selenium

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Spacy

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Q7. Which python library did we use to guess the category of the 3rd party (ORG or PERSON)?

Spacy

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Selenium

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Q8. Which type of object did we use to open a chrome web browser page?

webdriver.chrome()

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.get('https://duckduckgo.com/q=')'

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Q9. Why did we get the driver to wait a bit?

So that the python program is less likely to be detected as a bot

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In order for the python program to run quicker

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Q10. How can we find the codes that we need to use in the drvier.find_elements() function?

Right-click on the element from the web-page that you want to get the information from and select Inspect

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Note down the position of the element on the page in terms of pixels x and y

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Q11. Why did we not use BeautifulSoup or Scrapy?

BeautifulSoup and Scrapy do not necessarily allow us to scrap information that is found in JavaScript elements or web applications and Scrapy has a steeper learning curve

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Because we want to scrap information from JavaScript elements

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Q12. If we first classify 3rd parties as ORG based on a keyword search list, is this particular step Artificial Intelligence?

No

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Yes

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Q13. What type of object did we create using the nlp model?

A doc object that contains all the inforamtion about the word

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A word that does not have any spaces or punctuation, etc.

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Q14. Why did we Lemmatize the doc object?

In order to reduce it to its root form so that different variations of a word can be considered the same meaning

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In order to remove any punctuation or special characters

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Q15. Once we have identified 3rd parties that are individuals, should we incude those found as individuals in our audit report?

No, we should use them for answering further audit questions, such as: "What is the 3rd party account group in the system?", "What are the transactions?", "Was there due diligence?", etc. in order to assess if there is a risk.

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Yes, because 3rd parties should never be individuals as this would create a risk of fines from the tax office or corruption due to payments to individuals that might be linked to State Owned Entities

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WC134 – Using python to learn to categorize your 3rd parties

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Webclass 134: Using python to learn to categorize your 3rd parties

admin June 9, 2026

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