How can NLP be useful in Finance

The article discusses the number of ways in which NLP techniques like sentimental analysis, chatbot assistants can be handy in the world of Finance.
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NLP refers to Natural Language Processing. In the simplest words, NLP is analyzing text data to extract relevant information. The term natural language refers to the spoken language in the form of writing i.e. text. Analytics and Data Science has shown its prowess in recent years. The only untapped area was text analytics, but research with text took a higher speed and progressed further. With the most advanced GPT-3 model coming into the picture, text related jobs are going to get way too easier. So, NLP techniques can be applied in various sectors. We can get maximum from analytics applications where there is a higher volume of text data available. Sentiment Analysis, Text Extraction, and Chatbots are some of the common examples of NLP applications. 

Nowadays, we can also see NLP being used in Finance. There are various applications where NLP is being used like :

  • Credit scoring for Under Banking Clients
  • Sentiment Analysis for Customer Service
  • Document Search for Business Intelligence
  • Virtual Assistants or Chatbots in Banking

Credit scoring for Under Banking Clients

Credit scoring is done in banks for clients who want to buy various loans for the customers. The bank decides for disbursing loans based on the demographics of the customer, CIBIL score, credit history, etc. But if a new customer approaches the bank for a loan, the customer’s digital footprint across the Internet is taken into consideration. The digital footprints could be product reviews on shopping websites, data on social media like Twitter, Facebook, Instagram, Linkedin, etc.

This data is generally a good mix of textual data as well as multimedia data. So after data preprocessing, the textual data is ready to use and can be analyzed further to generate insights using concepts like Topic Modelling or Word Clouds. These can be used for determining the customer’s behavior and it will help in filling the data points for the new customer. 

Credit scorecards are calculated to reduce the risks associated with loan defaults. Textual data along with numerical data helps in determining whether the customer will default on the loan or not. Once the dataset is ready, any classification model(for example, Logistic Regression) can be used to get the predictions. Textual data analysis will help in identifying the important topics in the data and will help improve the performance.

Sentiment Analysis for Customer Service

Sentiment Analysis is used for financial text document analysis. Depending on some predefined positive, negative, and neutral dictionaries, we can classify the documents to be in that category and take investment calls depending on that. The positive, negative, or neutral polarity for words is calculated.

The overall sentiment for the document is calculated by using either the Bag of Words Model, Embeddings, or Deep Learning techniques. The embeddings technique makes use of not only the words but also a step ahead – its context in the sentence. Embeddings project the words in a multi-dimensional space and use the words as a lookup index. The FinBERT architecture is one such architecture that makes use of the traditional BERT model and makes sentiment predictions for financial documents.

These techniques make use of multi-dimensional spaces. Training models are high on computation power and require resources to be equipped to let you perform the training. Transfer learning makes use of deep learning models which are trained on huge corpora. Often the context is not clearly defined. The high performance of these deep learning models is not all that you need. Human intervention becomes unavoidable in such cases.

Document Search for Business Intelligence

The finance world involves a lot of digital documents for loans, statements, etc. So to segregate these different documents into the right categories. NLP can help automate this task with the help of Topic Modelling. The NLP system will take the documents as input, it will read and understand the documents i.e. data preprocessing.

This will enable the system to extract important text from the document and will be able to summarize the major topic around which the document revolves. Text extraction and summarization both are important techniques for helping with the summarization of large PDF files which is a cumbersome process manually. NLP enables a faster utility for document classification as well as text summarization.

Virtual Assistants or Chatbots in Banking

Digital banking has been a convenient option in these tough times. Banks have adopted Virtual Assistants or Chatbots who are human replicas and provide customer support to the customers without the need for an actual human in that place. These bots are different from conventional rule-based chatbots.

AI-based chatbots make use of Machine Learning and NLP to learn autonomously from the data that is provided and also previous interactions. These are deep learning-based systems. The bot itself decides what kind of an answer it will give to the customer’s queries. AI chatbots can be useful for 24/7 customer support, lead generation and selling, proactive advice, and also for fraud prevention.

These are some of the common applications in Finance in the NLP space. Design systems that can benefit people and make use of NLP to reduce turnaround times and for making tasks easier.


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2 Responses

  1. November 19, 2020

    […] by “AI chatbots”. Most of the companies are effectively using “chatbots” to automate their customer service and minimize the work-load of their […]

  2. November 24, 2020

    […] linguistic structures to accurately interpret what is being said and make the correct inferences. NLP  can be used in both back-office and customer-facing roles in banking. Behind the scenes, banks have access to huge amounts of data, from multiple internal and external […]


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