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Quality-Control-for-Banking-using-LDA-and-LDA-Mallet

Learning and extracting information from a Financial Institution’s rationale.

If you are looking to skim over the project without going into too much detail, you can always access it through here.

The Canadian banking system continues to rank at the top of the world thanks to our strong quality control practices that was capable of withstanding the Great Recession in 2008. One approach to improve quality control practices is by analyzing a Bank’s business portfolio for each individual business line. Each business line require rationales on why each deal was completed and how it fits the bank’s risk appetite and pricing level. I will be attempting to create a “Quality Control System” that extracts the information from the Bank’s decision making rationales, in order to determine if the decisions that were made are in accordance to the Bank’s standards.

The dataset I will be using is directly from a Canadian Bank, Although we were given permission to showcase this project, however, we will not showcase any relevant information from the actual dataset for privacy protection.

This project was completed using Jupyter Notebook and Python with Pandas, NumPy, Matplotlib, Gensim, NLTK and Spacy.

This project allowed myself to dive into real world data and apply it in a business context once again, but using Unsupervised Learning this time. I will continue to innovative ways to improve a Financial Institution’s decision making by using Big Data and Machine Learning.