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Banking Automation: NLP in the Finance Industry

By Nitin Gangwar 27th November 2019

The multiplied growth of data from various sources like- Internet, electronic devices, social media, etc. is flooding the databases. But in-turn it is providing organizations in the finance sector bountiful opportunities to improvise their business. 

Research shows that more than 80% of the data in the financial sector is unstructured. Data present in the form of excel files, SQL databases, stock information, relational databases are denoted by structured data. This data can be utilized with the help of advanced analytical programs and algorithms which reveal the patterns. The challenge is not to manage and utilize this remaining 20% of the structured data. But, the real challenge is managing and using the ocean amount of unstructured data which is present in the form of audio and video files, social media posts, PDF files, and similar files. To drive out value from this data, financial institutions are moving to NLP.

The financial sector including other businesses is now using NLP. In fact, the global NLP market in 2017 was US$ 3.2 billion and is expected to hit the market value of US$ 28.6 billion in 2026. 

Natural Language Processing, NLP is a subfield of Artificial Intelligence. It deals with the interaction between computers and humans using natural languages, in particular how to program computers to process and analyze huge amounts of natural language data.

Why the finance industry needs to use NLP and other AI-powered solutions-

  • To personalize communication in financial services
  • To increase the productivity of the workforce
  • To identify opportunities in data, which otherwise would have been missed
  • To come out as an innovative industry

How is the financial sector using NLP?

NLP helps the banks automate and optimize tasks like searching for documents and collecting customer information. Other ways in which financial sectors use NLP are-

  • Sentiment analysis: Most commonly the sentiment analysis is used to analyze the financial news. Particularly predicting the future market trends and stock exchange market.  
  • Risk management: Artificial Intelligence aids to identify potential risks and fraudulent activities. It provides tools and AI solutions to banks and credit unions. 
  • Sustainability: As the risks associated with investments increase. The need for sustainable evaluation increases. Thus sustainable considerations and sustainable finance, as a result, are increasingly critical to financial decision-making.

Here’s how NLP and AI will help the financial sector in the future:

  • Anti-money laundering pattern detection – This technique is to keep a check on the methods used to generate income by using illegal methods.
  • Chatbots – These are used to handle human chats without any human intervention
  • Recommendations to the customers – It is based on the historical data analysis and recommending the best according to consumer behavior.  
  • Algorithmic trading – A trading method that executes orders based on pre-programmed instructions based on metrics like- price, volume, time, etc.

The financial sector is improving a lot with NLP in terms of operational efficiency, customer interaction and of course, there is a lot more to come. 

Kato provides customized solutions for the financial sector businesses by integrating artificial intelligence, machine learning, and AI-powered chatbot. 

To know more, visit Kato

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