Evolving of Smart Banking with NLP and Deep Learning

The banking world is moving faster with digital reality, where financial transactions, customer care, fraud prevention, and trading analysis are no longer handled by humans but by computers. Digitalization is marching ahead, and financial industries are not in the back seat to realize the same. Banks are producing a lot of information as part of their daily processes. This information stored either in legacy platforms or in the cloud is amorphous, and a lot of confidential information is kept inside it. Our objective is to read those unstructured data elements and extract meaning from them, which can be used for enterprises for managerial insights and business process innovation. With the evolution of deep neural networks, a sub-domain of artificial intelligence (AI), extracting and classifying unstructured data is much easier nowadays. This chapter is forwarding our research to use deep learning algorithms with natural language processing (NLP) to solve the challenges banks face in reading these unstructured data and extracting meanings. Our approach uses cognitive neural networks (CNN) and recurrent neural networks (RNN) in NLP to obtain performance results that substantially improve Spearman correlation scores above other traditional models. We will also perform a qualitative study of the importance of these unstructured data on why and when it is critical to utilize this framework to improve enterprises in insights extraction and classification. This chapter illustrates the role of deep learning in NLP for sentiment analysis and emotion detection using the extracted features from unstructured data for smart banking.