How GPT-3 Can Create Ripples in the Financial Sector

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GPT-3 is already creating waves and might give programmers and journalists a run for their money, but how it will affect the financial sector. Read on to find out…
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GPT-2 was an API with a humungous computing power consisting of 1.5 billion parameters, got accolades from around the world but was also at the receiving end for facing a lot of flak and criticism. The AI evangelists believed that GPT-2 will help a lot in creating machine learning applications that will help in writing fake news, however, Allen Institute of AI helped GPT-2 by developing a fake news detector tool.

In May 2020, OpenAI backed by Musk and funded by Microsoft came up with another Artificial Intelligence that will go down in history as one of the most prolific inventions in the mid-human computing era, we’ve already passed the initial era with the advent of Siri, Cortana, Google Assistant, Machine Learning applied in search engine queries, in making business decisions.

Unlike GPT-2, GPT-3 consists of 175 billion parameters, that can help you write text, it can be any kind of texts like articles, writing codes, cover letters, presentation content.

To get started with the GPT-3 one needs to join the waitlist of thousands of Machine Learning enthusiasts waiting to get a hold of the coveted computing text generating power that the GPT-3 boasts of.

The finance sector will change?

Banking Sector

GPT-3 consists of 175 billion parameters that can also perform mathematical calculations and with the help of just a few good examples, it can be trained to an expert level quickly.

GPT-3 can help with fast & accurate sentimental analysis in the banking sector to study customer surveys, social media insights from customers.

Bank of Italy claims to have successfully carried out a sentimental analysis project, in which they collectively gathered data from customers posting social media feeds about the bank and later on performed a sentimental analysis to check the sentiments of the twitter users who are tweeting about the bank.

This can help all the banks majorly to make business decisions, and with GPT-3, it can be done with ease. 

Equity Research

The research team will be able to get data as input from a pile of online print media and research papers, it can help analyze and produce much probable research literature, better and faster than humans, and equally good in terms of the quality of content that a human writes.

JP Morgan tried using Natural Language Processing(NLP) to increase productivity in the research of their researchers and portfolio managers and underwriters in equity analysis.

Insurance

GPT-3 will help in improving customer experience with the help of chatbots, underwriting, processing of claims as mentioned before.

It can be very useful again in social media post analysis to gain in-depth insights into a company’s portfolio(corporate insurance).

As I’ve mentioned earlier that GPT-3 can write codes in Python, it can easily give the NLP code within seconds to give a suitable customer experience.

It can also help in creating general financial statements such as calculating balance sheets, with zero accounting knowledge required. Just a few lines of Python code and you are up & running.

Moving away a bit from the finance sector, GPT-3 can be used in creating Legal documents for the lawyers too.

It will make us productive in a plethora of ways but at the same time, we are looking at another loom in the job market with GPT-3 giving us an easy path, businesses will depend on this API, rather than the human brain.

People have built their own search engine within seconds using GPT-3.

Conclusion

GPT-3 is surely going to give professionals like content writers, journalists, researchers, and analysts, and programmers a major run for their money as companies will look at it as a major cost-cutting opportunity.

OpenAI’s GPT-3 will also compel other tech giants and startups to launch similar powerful text generators like this one. It is a major upgrade and achievement in NLP, called a Natural Language Generation(NLG) model too. 

Know more about GPT-3 business use cases.

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