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The speed at which Artificial Intelligence is evolving and being applied will challenge the financial services industry.
The series AI and ML Application in Banking can be seen as a starting point to demonstrate how the working environment will change in the near future. The series (in the Category AI & ML) will start with two new modules:
Designed for financial services professionals in the Front Office and in Risk Management, the module shows how deep learning can increase the effectiveness of credit scoring models.
You will be introduced to the architecture of a neural network (ANN), learn all steps to preprocess data and get familiar with all relevant terminologies involved in ANN. The output is the result of a trained Tensorflow Keras model.
Also known as computational linguistics is a further application of AI in Banking. It is used for sentiment analysis, topic detection or in-text extraction of earning calls, customer reviews or social media blogs.
Get an insight into the techniques applied in sentiment analysis (e.g. relevance, polarity, subjectivity) and for topic detection (TF-IDF and LDA model). Lastly, we cover basic text extraction techniques such as Part-of-Speech (POS) or Named-Entity Recognition (NER).
