I’m a trainee at a fintech startup, and I’m working on a project that involves classifying transactions using Natural Language Processing (NLP) and fuzzy matching techniques. The main goal is to categorize transactions based on merchant names, including the ability to recognize and match abbreviations to their full names. For example, if a transaction mentions ‘AMZ’, I want to use a fuzzy function to match it to ‘Amazon’ and classify it as a ‘shopping’ transaction.

I’m reaching out to the community for advice and insights on the following aspects:

  • Approach: What are some effective methods or algorithms for implementing a fuzzy matching function that can accurately match abbreviations to full names in transaction classification?
  • NLP Integration: How can I integrate NLP techniques with the fuzzy matching function to improve the accuracy of transaction classification?
  • Tools and Technologies: Are there any recommended tools, libraries, or frameworks for implementing these techniques in a fintech environment?

I’d greatly appreciate any guidance, recommendations, or examples you could share. Thank you in advance for your help!



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