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Natural Language Processing in Financial Industry



  Natural Language Generation in Finance
Natural Language Generation in Finance   
Financial Institutions already use the well-mined data from well structured data-sources to enhance their day-to-day operations as well as actionable insights thus improving their business practices. Nowadays, a major part of the financial industry is turning towards the extraction of unstructured data with the help of Natural Language Processing, Artificial Intelligence, Information Retrieval and Machine Learning. These financial institutions have realised the existence of an enormous amount of unstructured data that can be gathered from social media, internet, personal devices, emails, text messages, images, videos, audios, news, articles, feedbacks, etc, which can be analysed and used to present these enterprises with unprecedented opportunities of growth. With the help of such unstructured data, companies get a better insight of the likability of their services and products amongst their customers and their competitors. Recent advances in technology and the ever-growing development of unstructured data have lead to companies using ML and NLP for their risk management, frauds management, personal finance management, enhancing customer services, wealth management, improving their operational efficiency and taking crucial decisions. Companies nowadays have too much data but too few analysts and much fewer time to analyse this vast data, therefore they use NLP for the same.



Contribution of NLP in various financial aspects:


  • Gathering insights and real- time alerts on the dynamic movements of various stocks and security markets.

  • Recording company sentiments - any major news such as acquisition or investor sentiments that may affect the business.

  • Finding out and addressing positive/negative insights of clients and customers through social media.

  • Organisational Analyses becomes easier and more efficient giving better results.

  • Analysis of how certain news articles and press releases (at national, international and governmental level) can affect the company.

  • Verification of quickly accessible, relevant and filtered information to maintain the consistency between different sectors of an enterprise.

  • Providing timely updates for important decisions.

  • Helps in prediction and detection of fraud with the help of neural networking and deep learning techniques.

  • Monitor profitability, risk management and other performance related factors.

  • Banks are using NLP to manage customer accounts and improve customer experience with the help of chatbots and automated systems.

  • ML techniques can help banks to determine the credit-worthiness of their customers/clients. They could also help the bank to decide the amount of credit to be provided based on customer’s past records and status.





Future of NLP in Financial Industry:


NLP in itself contains many challenges that are yet to be resolved i.e., computational complexities in some cases, understanding of full language, etc. but still it is believed to be one of the most promising technologies for development. Several sectors have made significant progress with the help of NLP techniques and are still making new progresses. More and more understanding of languages will make Human Computer Interaction more easier and hence financial institutions with the help of virtual assistants, will be able to interact more efficiently with their clients. Technological advancements in almost every field are also leading to exponential growth of quantitative and qualitative data which further leads to new affordances for financial industry. Leading innovators and tech giants like Google and Facebook heavily rely on their NLP and AI technologies to extract cutting-edge insights from their data and provide dynamic customer experiences.





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