NLP in Financial Services: Extracting Intelligence from Financial Text
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Course Modules:
Module 1: Introduction to NLP in Finance
Why NLP matters in financial services
Common text sources: earnings calls, 10-Ks, news, social media, research notes
Challenges: jargon, ambiguity, and domain-specific vocabulary
Module 2: Text Preprocessing and Feature Engineering
Tokenization, stemming, lemmatization
Cleaning financial documents (removing headers, footnotes, boilerplate)
Creating TF-IDF, bag-of-words, and embedding features
Module 3: Classification and Sentiment Analysis
Classifying news by topic or market impact
Sentiment scoring of tweets, reports, and headlines
Supervised models: logistic regression, XGBoost, and BERT-based classifiers
Module 4: Named Entity Recognition (NER) and Event Extraction
Identifying companies, currencies, tickers, dates, and financial terms
Event detection (e.g., mergers, earnings surprises, policy changes)
Rule-based vs. model-based NER in finance
Module 5: Language Models and Real-Time Applications
Using transformer models (FinBERT, RoBERTa, GPT)
Text summarization for analyst reports and regulatory documents
Real-time alert systems for compliance and trading
Module 6: Capstone Project – Build a Financial NLP Pipeline
Choose a use case (e.g., sentiment scoring for stock news, document classifier)
Build a pipeline using real or synthetic financial text data
Submit your model, preprocessing logic, and final analysis report
Tools & Technologies Used:
Python
NLTK, spaCy, Transformers (Hugging Face)
Scikit-learn, XGBoost
FinBERT, RoBERTa, Word2Vec, Gensim
Target Audience:
Finance professionals and analysts
AI engineers and NLP researchers
Developers working in fintech and regulatory tech
Students building financial intelligence tools
Global Learning Benefits:
Extract value from unstructured financial text
Automate compliance monitoring and trading sentiment analysis
Apply state-of-the-art language models to high-impact use cases
Bridge AI and finance with real-world NLP pipelines
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