With AI and ML advanced algorithms, banking sectors use NLP to automate the various processes. The procedure includes document processing, analyzing, and activities related to customers for hassle-free services. The banking industry is one of the critical sectors for more than decades, and lots of transactions take place at every moment. And it is always a huge challenge to have everything on the record without a series of automation processes.
A Short Intro To Natural Language Processing
Natural Language Processing (NLP) is a branch of artificial intelligence and machine learning that enables machines to understand human language and interact with them. This entire process involves training computers to process text and speeches to interpret the meaning of the words, sentences, and paragraphs in the context.
Natural Language Processing often uses AI methods that include neural networks, deep learning, and optical character recognition. And for this, the two top-notch NLP models are word2vec and Bag of Words.
Interaction Between Humans and Machines: A Better Way To Make Conversations Easy.
When was the last time you unlocked your phone, opened Siri, or Alexa, or Google Assistant, and asked what the temperature in your city was? Or is it going to rain today? Or how is the traffic in your location? I hope everyone has tried this earlier and tries this every day. It is what NLP is. And has the best application in our day-to-day life.
These applications are coded with smart algorithms and trained to interact with humans by understanding their languages and giving accurate answers to their search queries.
Here are the steps involved in Natural Language Processing to make conversations between humans and machines better understanding.
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We provide a typing text or voice query as an input ( typing on the chatbot interface or talking to a smart device for the search queries.
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Then the computer converts the text/speech into a frequent format that only the computer understands. And this helps the computer to classify different words.
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Then the computer figures out the concept and context using its datasets.
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Then the computer looks for the appropriate response, and when it gets it, the computer converts the text into speech that we understand and responds to us.
Some of the best uses of NLP in our daily life are Google Translator, Grammarly, Smart Speakers, and Devices with inbuilt Alexa or Google Home Assistant.
5 Uses Cases How Bank Use NLP Will Amaze You
Intelligent Document Scanning With Smart Algorithms
JP Morgan Chase’s software ‘COIN’ uses NLP for extending help to the bank’s legal team. And to go for the reviews of the massive volume of legal documents. The COIN has the potential to save 360,000 hours or 15,000 days of search tasks per annum. It extracts prime data for the loan officers for commercial loan agreements. It uses advanced AI and ML to classify multiple documents and improve the search volume over time.
It helps to classify various customers regarding various bank activities such as who pays the bill on time and who are the defaulters to pay the loans back. So, it can help banks to know their customers using document analysis to make the whole process easier.
Investment Analysis For Facilitating Commercial Loans
Not only people but companies do take loans from the bank. And it is always tough to find out the meaningful insights within tons of reports and conference calls. Thus, data scientists play a crucial role in getting them meaningful insights. And classify and figure out the valuation models to read numerous documents and save a lot of banks’ time.
Bank uses NLP for sentiment analysis to analyze a large volume of news and social media posts to extract the prime information. So banks invest a lot in vendors for them to develop a system to automate their process. NLP can be a great help for them to identify related information from social media and financial reports. These involve trends, risks before the bank facilitates the loans.
Customer Service With Personalized Messages
Top-Notch customer services are what keep customers retaining for a longer time. But maintaining and handling tons of customers is never an easy task, but a massive headache for banks. Therefore, banks have got chatbots for the users and trained based upon advanced algorithms to understand and respond to the user queries.
But today, things have changed. Personalized messages are trending ever since they are out in the market. And people love it more often when they get filled with personalization and care. Even they feel the organization is caring for them. And that ultimately helps them to build trust and retain clients for a longer time.
Digital Financial Counseling Using Chatbots
With the blessing of NLP, chatbots are the new trend in the market that collects data for you from the customers even when you sleep soundly. They are active 24 x 7 x 365 days to educate the customers based on the existing algorithms based upon the bank guidelines. And this NLP can save more than 800 million working hours so that banking sectors could invest this time enhancing these technologies to provide a better customer experience.
To automate the series of processes is never easy, such as handling client inquiries, bank statements, transaction records, and notifying the security threads just in one go, and can manage thousands of customers at a time.
Underwriting Automation To Fill Your Required Data
Just like other websites, bank websites also ask to collect cookies. As we all know that typing is a tough job for each of us, but it is always cookies that save us and our time. It remembers everything and fills by itself when we log in. That’s what we call underwriting automation that works based on natural language processing.
One of the best advantages of NLP in underwriting automation is claiming the insurance process. It helps you claim it in under a minute, or ask some additional questions to select further questions that are most suitable to strengthen your claiming process with the real data. This way, it lightens the work of insurance specialists with the least errors.
Final thoughts
There is no doubt that the role and contribution of NLP in the banking and financial sectors are crossing boundaries on every occasion to make the service easy and hassle-free. With broad areas of applications, it saves millions of time and gives accurate results to all the complicated processes.
From generating real-time insights and analyzing data through contextual analysis, NLP has no boundaries. To extract and interpret the advanced techniques to maximize profits, trends, and integration of new systems to the banking system.
