sidhellman/clean_data.txt · Datasets at Hugging Face

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past technologies that have come and
gonethink metaversethis latest one
looks set to stay. OpenAIs chatbot, ChatGPT, is perhaps
the best-known generative AI tool. It reached 100 million
monthly active users in just two months after launch,
surpassing even TikTok and Instagram in adoption speed,
becoming the fastest-growing consumer application in
history.
1
Several innovations inherent within generative AI
have obvious potential benefits for businesses. Its
large language models (LLM) can learn from even
bigger quantities of data than classical AI, including
incorporating information from unstructured inputs.
Users can interact with generative AI tools and receive
responses in natural language. And finally, according to
user needs, generative AI canas the name indicates
generate a range of new outputs including text,
pictures, computer code, and data streams.
Such breakthroughs have led to high expectations.
According to a McKinsey report, generative AI could
add $2.6 trillion to $4.4 trillion annually in value to the
global economy.2 The banking industry was highlighted
as among sectors that could see the biggest impact (as
a percentage of their revenues) from generative AI. The
technology could deliver value equal to an additional
$200 billion to $340 billion annually if the use cases
were fully implemented, says the report.
For businesses from every sector, the current challenge
is to separate the hype that accompanies any new
technology from the real and lasting value it may bring.
This is a pressing issue for firms in financial services.
The industrys already extensiveand growinguse
of digital tools makes technology advances particularly
likely to affect the sectors companies.
According to a recent UBS report on the impact
of generative AI, statistics from the U.S. Bureau of
Labor suggest that banks and insurance are among
the industries with the greatest proportion of their
workforces exposed to potential automation.3 This
MIT Technology Review Insights report delves further
into that possibility, examining the early impact of
generative AI within the financial sector, where it is
starting to be applied, and the barriers that need
to be overcome in the long run for its successful
deployment.
The following are the reports key learnings:
Corporate deployment of generative AI in financial
services is still largely nascent. The most active
use cases revolve around cutting costs by freeing
employees from low-value, repetitive work. Companies
have begun deploying generative AI tools to automate
time-consuming, tedious jobs, which previously required
humans to assess unstructured information. Employees
are thereby freed for more creative work, and in some
cases, the tools outperform people. The following are
common areas of deployment:
5
MIT Technology Review Insights

Customer service: Generative AI chatbots are
already deployed to assist human customer service
agents and may soon directly advise customers on basic
questions. An academic study found that they increase
customer service agent productivity and consumer
happiness.

Fraud prevention and risk management: The
technology allows software to incorporate a wider and
richer set of data into risk and fraud detection.

Coding: Tools that can produce bespoke code for
specific tasks have begun to appear. In a controlled
experiment, an Australian bank found that these raised
programmer productivity by 46%.

Information analysis and summarization:
Generative AI tools are already creating summaries of
business conversationsa previously human task. On a
larger scale, BloombergGPT provides the company
another channel to monetize its archives by letting
subscribers search them using natural language
questions.
There is extensive experimentation on potentially
more disruptive tools, but signs of commercial
deployment remain rare. Academics and banks are
examining how generative AI could help in impactful
areas including asset selection, improved simulations,
and better understanding of asset correlation and tail
riskthe probability that the asset performs far below
or far above its average past performance. So far,
however, a range of practical and regulatory challenges
are impeding their commercial use.
Legacy technology and talent shortages may slow
adoption of generative AI tools, but only temporarily.
Many financial services companies, especially
large banks and insurers, still have substantial,
aging information technology and data structures,
potentially unfit for the use of modern applications.
In recent years, however, the problem has eased with

past technologies that have come and

gonethink metaversethis latest one

looks set to stay. OpenAIs chatbot, ChatGPT, is perhaps

the best-known generative AI tool. It reached 100 million

monthly active users in just two months after launch,

surpassing even TikTok and Instagram in adoption speed,

becoming the fastest-growing consumer application in

history.

1

Several innovations inherent within generative AI

have obvious potential benefits for businesses. Its

large language models (LLM) can learn from even

bigger quantities of data than classical AI, including

incorporating information from unstructured inputs.

Users can interact with generative AI tools and receive

responses in natural language. And finally, according to

user needs, generative AI canas the name indicates

generate a range of new outputs including text,

pictures, computer code, and data streams.

Such breakthroughs have led to high expectations.

According to a McKinsey report, generative AI could

add $2.6 trillion to $4.4 trillion annually in value to the

global economy.2 The banking industry was highlighted

as among sectors that could see the biggest impact (as

a percentage of their revenues) from generative AI. The

technology could deliver value equal to an additional

$200 billion to $340 billion annually if the use cases

were fully implemented, says the report.

For businesses from every sector, the current challenge

is to separate the hype that accompanies any new

technology from the real and lasting value it may bring.

This is a pressing issue for firms in financial services.

The industrys already extensiveand growinguse

of digital tools makes technology advances particularly

likely to affect the sectors companies.

According to a recent UBS report on the impact

of generative AI, statistics from the U.S. Bureau of

Labor suggest that banks and insurance are among

the industries with the greatest proportion of their

workforces exposed to potential automation.3 This

MIT Technology Review Insights report delves further

into that possibility, examining the early impact of

generative AI within the financial sector, where it is

starting to be applied, and the barriers that need

to be overcome in the long run for its successful

deployment.

The following are the reports key learnings:

Corporate deployment of generative AI in financial

services is still largely nascent. The most active

use cases revolve around cutting costs by freeing

employees from low-value, repetitive work. Companies

have begun deploying generative AI tools to automate

time-consuming, tedious jobs, which previously required

humans to assess unstructured information. Employees

are thereby freed for more creative work, and in some

cases, the tools outperform people. The following are

common areas of deployment:

5

MIT Technology Review Insights

Customer service: Generative AI chatbots are

already deployed to assist human customer service

agents and may soon directly advise customers on basic

questions. An academic study found that they increase

customer service agent productivity and consumer

happiness.

Fraud prevention and risk management: The

technology allows software to incorporate a wider and

richer set of data into risk and fraud detection.

Coding: Tools that can produce bespoke code for

specific tasks have begun to appear. In a controlled

experiment, an Australian bank found that these raised

programmer productivity by 46%.

Information analysis and summarization:

Generative AI tools are already creating summaries of

business conversationsa previously human task. On a

larger scale, BloombergGPT provides the company

another channel to monetize its archives by letting

subscribers search them using natural language

questions.

There is extensive experimentation on potentially

more disruptive tools, but signs of commercial

deployment remain rare. Academics and banks are

examining how generative AI could help in impactful

areas including asset selection, improved simulations,

and better understanding of asset correlation and tail

riskthe probability that the asset performs far below

or far above its average past performance. So far,

however, a range of practical and regulatory challenges

are impeding their commercial use.

Legacy technology and talent shortages may slow

adoption of generative AI tools, but only temporarily.

Many financial services companies, especially

large banks and insurers, still have substantial,

aging information technology and data structures,

potentially unfit for the use of modern applications.

In recent years, however, the problem has eased with