This document discusses the use of artificial intelligence, specifically chatbots, in the financial sector. It begins by providing examples of early chatbots used by banks to provide customer service. It then defines chatbots and explains how they use natural language processing to answer customer queries. The document outlines the typical working methodology of chatbots, including how they analyze user requests, perform actions, and retrieve information from knowledge bases or APIs. It also summarizes prior studies that have examined factors like customer acceptance, satisfaction, and trust of chatbots for customer service. Finally, it discusses the future of AI in finance, noting that business leaders see technology and AI playing a significant role in financial services and that the AI market is expected to grow substantially in coming

1. AI IN
FINANCE
B.TECH-3 YEAR
VINIT,RUDRA,KARTIK,AYUSH,PRIT

2. App
 “Eno” was the first natural language SMS text-
based assistant offered by a bank in the United
States.

3. What is chatbot?
 This AI makes the use of Natural Language Processing to give relevant answers for the
different queries of the customers.
 Basically, chatbots are an automated chat program that either runs automatically or on a
pre-determined path.
 Nowadays, the banking sector makes the chatbots in use to provide their customers with a
24/7 service experience.

4. Abstract Working Methodology
1 A CHATBOT is an artificially created virtual
entity that interacts with users using interactive
textual or speech skills.
After receiving the user’s request, the Natural Language
Language Understanding (NLUs) component analyse it
or maps it to the user’s intention and, consequently,
gathers further related information. BOT intended to
perform the requested actions or retrieves the data of
interest from its data sources,
a BOT Knowledge Base database, or an API call that
access external resources.
2 It is a computer program, which responds like a
smart entity when conversed with through text
or voice and understands one or more human
languages by Natural Language Processing (NLP)
Speech recognition Speech synthesis Conversation
manage NLU

5. Abstract Working Methodology
3 The present article highlights the characteristics of
empirical studies on chatbots in the context of the
financial sector. From acceptability and user
interaction to engagement, satisfaction, customer
service, security, customer and company perception,
perception, trust and implementation of a chatbot
that, these factors allow the financial sector to
transform their activity and operations
A systematic methodological review has been carried out
out on the basis of a two-step approach . In the first step ,
, relevant dataset has been identified on the bases of
keywords , synonyms and search strings . In the second
step on the bases of keywords and search string the
relevant data that is required by the user is provided.
4 This study examines technology effectiveness for
industry demand in which artificial intelligence (AI) is
applied in the financial sector. It summarizes prior
studies on chatbot and
customer service and investigates theories on
acceptance attitudes for innovative technologies..
We analyzed the statistical significance bank profits based
on customer information using financial products
customer service calls or chatbot systems.
Customers service helper system, Meaning of customers
Question in real time, Used Semantic Resoning Techniques,
Knowledge based Q&A, Large scale Knowledge
database,Retrivel based Q&A Oreder a list of answers,
Large document Set

6. Future of AI in Finance

7. Future of AI in Finance
Business leaders have revealed that the use of technology including AI plays a significant
role in filling gaps within financial services offerings.
47% of digitally mature companies stated that they have a defined AI planning to boost up
their business.
According to experts, the AI market will grow to $190 billion until the year 2025.
Forbes reports that already “70% of all financial services firms are using machine learning
to predict cash flow events, fine-tune credit scores and detect fraud”.

8. Thank You