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Word Embeddings instead of One-hots: Using Transformers word embeddings to improve categorical feature encoding in financial fraud data
Detecting financial fraud is complex. One important source of information is textual data, such as the text within reports, descriptions, or comments. Two different words may have similar meanings but vary differently in their characters and length. In this project, we evaluate how the meaning of a word may be used to improve financial fraud detection models that leverage textual data.
Faculty Supervisor:
Terrence Tricco
Student:
Partner:
NASDAQ Canada Inc
Discipline:
Computer science
Sector:
Finance and Insurance; Information and Communications Technology; Artificial Intelligence
University:
Memorial University of Newfoundland
Program:
Accelerate
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