PRACTICAL FILE

ON
HOW AI IS REVOLUSTIONARY FOR
INDIAN BANKING SYSTEM

GURU GOBIND SINGH INDRAPRASTHA UNIVERSITY

In partial fulfilment of the requirement for the award of

the degree of

BACHELOR OF BUSINESS ADMINISTRATION

Batch 2023 – 2026

SUBMITTED BY: SUBMITTED TO:

Akanksha Ms Jaya
Enrolment NO.: 10315501723 (Asst. Prof)

NEW DELHI INSTITUTE OF MANAGEMENT

61A, TUGHLAKABAD, NEW DELHI-62

CERTIFICATE
 I Mr/Ms Akanksha Thakur Roll No. 10315501723 Certify That The Minor Project
Report ( Paper Code Bba 114 ) Entitled “ How Ai Is Revolutionary For Indian Banking
System” Is Completed By Me By Collecting The Material From The Referenced
Sources . The Matter Embodied In This Has Not Been Submitted Earlier For The Award
Of Any Degree Or Diploma To The Best Of My Knowledge And Belief.

Signature Of Student :
Date :

Certified That The Minor Project Report ( Paper Code BBA-114) Entitled “ How Ai Is

Revolutionary For Indian Banking System’’ Done By Ms Akanksha Thakur Roll No.

10315501723 Is Completed Under My Guidance.

Signature of the guide :

Name of guide :
Designation :
Date :

Countersigned
Director/ project coordinator
 ACKNOWLEGEMENT

At the very outset I would express my sincere thanks and deep sense of gratitude to all the personnel who
helped me during the collection of data gave me rare and valuable guidelines for the presentation of the
project.

My profound gratitude to miss jaya , faculty guide for giving me an invaluable opportunity without whose
guidelines this project would not have been seen the light of the day. I am also thankful for her timely help,
guidance an encouragement that influenced the development of this project.

Last but not the least I would like to thank my collage NEW DELHI INSTITUDE OF MA NAGEMENT for
providing me this opportunity to work on this project.
TABLE OF CONTENTS
Ch. TOPI PAGE
C
NO. NO.
01 Introduction

 AI History

 Benefits of AI
 Limitation of AI
 Application of AI
 AI Banking In India

02 Literature Review

03 Research Methology

 AI in banking Service
 Impact of ai in Banking
 Type of AI

04 Data Collection and Interpretation

 Swot analysis of Banking Industry

 Self -learning and AI and Adaptive analytics

05 Finding and Conclusion

Bibliography
Chapter 1
INTRODUCTION

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 INTRODUCTION

Artificial intelligence (AI) which is also called as “machine intelligence” is intelligence exhibited by machines
contrary to the usual intelligence displayed by people. Artificial Intelligence is often used to describe machines
that humans associate with the human mind, such as “learning” and “problem solving”.

Artificial Intelligence is the ability of a machine or a computer to copy from something that is natural, in terms
of acquiring and applying knowledge and skills. When a machine mimics a human mind by thinking for itself, it
is known as Artificial Intelligence.

Artificial intelligence and advanced analytics driven decision making has transformed the ability of smaller and
financial service providers to compete with larger institutions. Today, big data coupled with advanced statistical
needs and machine learning algorithms can uncover pattern in organization’s data which are otherwise
impossible using traditional analytical tools.

In the context of banking, Accenture defines AI as, “A computer system that can sense, commprehend, act and
learn. A system that can perceive the world around it, analyse and understand the information it receives, take
actions based on that understanding, and improve its performance by learning from what happened. And by
enabling machines to interact more naturally - with their environment, with people and with data - the
technology can extend the capabilities of both humans and machines far beyond what each can do on their
own.”

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 Components of Artificial Intelligence
The components of artificial intelligence are as follows:
• Computer science
• Psychology
• Neuron science
• Biology
• Maths
• Sociology
• Philosophy

The Role of Banking Industry

Banks play an important role and considered as life blood of today’s economy because it handles cash, credits
and other financial transactions. Banks help customers and motivates them to save money and earn interest for a
secure future. Banks also extend financial assistance to the expansion of industries. Every financial transaction
done through the banks must be properly documented. To implement this, the banks primarily use computers.
Number of the channels that banks use for operations are through ATM’s, mails, telephone banking, online
banking and mobile banking. The smooth operation of the banking world is done through computers and
networks are feasible only because banks use AI.

OBJECTIVES OF THE STUDY

▪ To study the areas in which Artificial Intelligence (AI) can be implemented in banking Sector in India.
▪ To analyse the impact of AI in the growth of banking sector in India.
▪ To examine various challenges faced in implementing AI in banking sector.
▪ To identify various initiatives to overcome the challenges in implementing AI in banking Sector and how it
will help in sustainability and adoption .

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8
 Artificial Intelligence History
The term artificial intelligence was coined in 1956, but AI has become more popular today thanks to increased
data volumes, advanced algorithms, and improvements in computing power and storage. Early AI research in
the 1950s explored topics like problem solving and symbolic methods. In the 1960s, the US Department of
Defense took interest in this type of work and began training computers to mimic basic human reasoning. For
example, the Defense Advanced Research Projects Agency (DARPA) completed street mapping projects in the
1970s. And DARPA produced intelligent personal assistants in 2003, long before Siri, Alexa or Cortana were
household names. This early work paved the way for the automation and formal reasoning that we see in
computers today, including decision support systems and smart search systems that can be designed to
complement and augment human abilities.
While Hollywood movies and science fiction novels depict AI as human-like robots that take over the world,
the current evolution of AI technologies isn‘t that scary – or quite that smart. Instead, AI has evolved to provide
many specific benefits in every industry. Keep reading for modern examples of artificial intelligence in
healthcare, retail and more.

Artificial Intelligence Industry In India –

The Current Status A news report published in October in The Economic Times said, ―Startups witness 108%
growth in funding in India in 2018.‖ The news report further mentioned that Artificial Intelligence was among
those domains which witnessed fastest adoption among industry sectors. Currently there are about 400 start-ups
working on AI and machine learning domains. About $150 million dollars is invested in India‘s AI sector by
private players alone and the number has been growing since 2016. Though there has been growth, India lags
far behind countries like US and China in terms of investment. With a copious pool of STEM talent and with
growing population of youngsters, India will be banking on AI for its economic growth and improvement in
quality of life of its citizens.

There are several start-ups that are based in cities such as Bengaluru, New Delhi,
Mumbai and Hyderabad which work on artificial intelligence principles to serve
consumers better. Their product range vary from multi-lingual chat bots to online
shopping assistance and automated consumer data analysis. The companies have
been working in areas such as e-commerce, healthcare, edtech, fintech etc. Though
in their nascent stage, the performance of these companies have been promising.

Benefits of Artificial Intelligence

o For the economy, business and industries: AI can benefit the economy by helping the
evolution of work. Robots and AI will help people perform their work better, not take
their jobs.

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o For humanity and society: AI enhances information throughput and efficiency, helping
people create new opportunities. People will have more time to learn, experiment and
explore.

o Expenditure pattern: AI can help in understanding the customer’s expenditure pattern

And customized plans can be offered to customers.

o Online banking and mobile banking: The online banking and mobile banking become
popular as a tool for 24/7 transaction. AI access customer data, such as detailed
demographics, website analytics and records of online and offline transactions, machine
learning can integrate and analyse information.

o Offer High security: AI offers high security to the banking sector; AI mobile
applications can make the transaction quicker and safer. Understand the user’s behaviour
and offer personalized experience through an app, banks handle customer-oriented
operations easily while reducing the cost of hiring additional employees.

10
 Limitations of Artificial Intelligence

o The production and maintenance of AI requires high costs.

o AI consists of advanced software programs that require regular updates to meet the
needs of the changing environment

o AI might able to do, replacing adaptive human behaviour.

o AI makes the people lose their creative power, and can increase the unemployment
rate.

o AI in wrong hands can be serious threat to human kind, if individuals start thinking
destructively, they can generate these advanced machines.

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 Application of Artificial Intelligence

Application of AI in Banking Sector:

Artificial Intelligence (AI) is fast evolving as the go-to technology for companies across the world to
personalise experience for individuals. The technology itself is getting better and smarter day by day, allowing
more and newer industries to adopt the AI for various applications. Banking sector is becoming one of the first
adopters of AI. And just like other segments, banks are exploring and implementing the technology in various
ways. The rudimentary applications AI include bring smarter chat-bots for customer service, personalising
services for individuals, and even placing an AI robot for self-service at banks. Beyond these basic applications,
banks can implement the technology for bringing in more efficiency to their back-office and even reduce fraud
and security risks. Unsurprisingly, research firms are bullish on the potential of AI in banking. According to
Fintech India report by PwC in 2017, the global spending in AI applications touched $5.1 billion, up from $4
billion in 2015. There is a keen interest in the Indian banking sector as well.

Advent of AI banking in India

According to Accenture’s recent Accenture Banking Technology Vision 2018 report, 83% of
Indian bankers believe that AI will work alongside humans in the next two years — a higher
than the global average of 79%. “93% bankers in India said they increasingly use data to drive
critical and automated decision-making. More partner-supplied customer data means a higher
degree of responsibility for banks. Yet, 77% Indian bankers agree that most firms are not
prepared to confront impending waves of corrupted insights from falsified data," said the report.
“AI is not new to India. Research institutions and universities have been working with various
AI technologies for decades, and especially in the area of social transformation. With enabling
technologies becoming a lot more accessible and inexpensive, AI is now becoming mainstream,
with large enterprises and start-ups looking at different opportunities. Our research shows that
the adoption of AI has the potential to add nearly $1 trillion to the Indian economy in 2035. AI
adoption is still in its nascent stages, and a lot more needs to be done to realise its full
potential," says Rishi Aurora, managing director, financial services, Accenture. “Application of
AI and ML (machine learning) to different functions within the banking industry has enabled
them to offer a far more personalised and efficient customer service. By achieving that, banks
have also been able to gain better insights into their customers’ preference and expectations
from the bank. Accordingly, automation of back-end workflows has shown better outcomes.
According to various industry reports, more than 36% of large financial institutions are already
investing in such technologies, and close to 70% are planning to in the near future," according
to Darshan Shah, MD, South Asia, LenddoEFL, a Singapore-based fintech company.

12
Not just customer support

State Bank of India, the largest bank in India, last year conducted “Code for Bank" hackathon to encourage
developers to build solutions leveraging futuristic technologies such as AI and Blockchain into the banking
sector. Private banks like HDFC Bank and ICICI Bank have already introduced chat-bots for customers service.
Some have even gone ahead with placing robots for customers service. Last year, Canara Bank installed Mitra
and Candi robots at some of its offices. “Payment companies are using AI to offer personalised payment
experience to consumers. By applying AI and analyzing past payment patterns, payment systems can prompt
the preferred payment instrument which best suits a purchase at the time of checkout. Say a consumer avails
EMI option frequently for his big-ticketpurchases, then the best EMI option is made available to the consumer
at the time of checkout. Such personalised consumer experiences drive up consumer spending and creates
stickiness to the product consumers are using," saidVarun Rathi, cofounder and COO, Happay, a Bangalore-
based start-up focused on digital payment solutions.

Pune-based Persistent Systems’ chief architect, corporate CTO, Abhay Pendse lists out
some common uses of AI in banks:
• Fraud Detection: Anomaly detection can be used to increase the accuracy of credit card fraud
detection and anti-money laundering.
• Customer Support and Helpdesk: Humanoid Chatbot interfaces can be used to increase
efficiency and reduce cost for customer interactions.
• Risk Management: Tailored products can be offered to clients by looking at historical data,
doing risk analysis, and eliminating human errors from handcrafted models.
• Security: Suspicious behaviour, logs analysis, and spurious emails can be tracked down to
prevent and possibly predict security breaches.
• Digitization and automation in back-office processing: Capturing documents data using OCR
and then using machine learning/AI to generate insights from the text data can greatly cut down
back-office processing times.
• Wealth management for masses: Personalized portfolios can be managed by Bot Advisors for
clients by taking into account lifestyle, appetite for risk, expected returns on investment, etc.
• ATMs: Image/face recognition using real-time camera images and advanced AI techniques
such as deep learning can be used at ATMs to detect and prevent frauds/crimes.

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14
ICICI Bank

ICICI Bank is one of the leading private sector banks in India. The Bank’s total consolidated
assets stood at Rs 14.76 trillion as of September 30, 2020. ICICI Bank currently has a network
of 5,288 branches and 15,158 ATMs across India.

History

ICICI was established in 1955 on the initiative of the World Bank, the government of India and
representatives of Indian industry. The main objective is to create a development finance
institution to provide medium and long term project finance to Indian companies. Until the late
1980s, ICICI mainly focused its activities on project finance, providing long-term capital for
various industrial projects. With the liberalization of the financial sector in India in the 1990s,
ICICI transformed its business from a development finance institution that only provided
project finance to a service provider. Diversified Finance, along with its subsidiaries and other
group companies, offers a wide range of products and services. As the Indian economy becomes
more market oriented and integrates with the global economy, ICICI has taken advantage of
new opportunities to offer a wider range of financial products and services to a wider range of
clients. ICICI Bank was created in 1994 within the ICICI group. In 1999, ICICI became the first
Indian company and the first bank or financial institution in Asia outside of Japan to be listed
on the New York Stock Exchange.

The question of global banking, in the Indian context what it means to convert long-term
lending institutions such as ICICI into commercial banks, was discussed at length in the late
1990s. the bank gives ICICI low-cost demand deposits and offers a wider range of products and
services, as well as a greater opportunity to earn non-fundable income in the form of bank fees
and roses.
After examining the different business structure alternatives in light of the emerging
competition in the Indian banking sector and the move towards global banking, the management
of ICICI and ICICI Bank stated that the merger of ‘ICICI with ICICI Bank will be the optimal
solution. Strategic alternative to both entities, and will create the optimal regulatory structure
for the ICICI group’s global banking strategy. The merger will increase ICICI’s shareholder
value through the merged entity’s access to low-cost deposits, greater opportunities to earn
15
commission-based income, and the ability to participate in payment system and providing
transaction banking services. The merger will increase ICICI Bank’s shareholder value through
its substantial capital base and scale of operations, seamless access to strong ICICI corporate
relationships established over five decades. Century, entering new business segments, a higher
market share in various business segments, especially services, and accessing the vast talent
pool of ICICI and its subsidiaries. In October 2001, the boards of directors of ICICI and ICICI
Bank approved the merger of ICICI and two of ICICI’s wholly-owned retail finance
subsidiaries, ICICI Personal Financial Services Limited and ICICI Capital Services Limited,
with ICICI Bank. The merger was approved by the shareholders of ICICI and ICICI Bank in
January 2002, by the High Court of Gujarat in Ahmedabad in March 2002, and by the High
Court of Mumbai and the Reserve Bank of India in April 2002. The merger, the banking and
financial activities of the ICICI group, both wholesale and retail, were integrated into a single
entity.

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State bank of india

STATE BANK OF INDIA With over $630 Billion in assets and 500 Million customers,
State Bank of India (SBI) is one of the Top 50 global banks and well on course to break
into the Top 30 in a few years. SBI operatesout of 26 countries with over 25,000 branches
and 60,000 ATMs generating revenues close to$61 Billion. Over 70 Million customers
actively use their Internet and Mobile Banking channels.About 45% of all transactions in
India go through SBI’s payment gateways which is setup tohandle 50,000 concurrent
transactions.Goals of implementing an Intelligent Virtual Assistant •Reduce Opex Cost
– Significantly reduce customer support cost through automation •Increase Conversion –
Enable seamless customer onboarding through AI Assistance •Maximize Revenue
Opportunities - Upsell/cross sell through AI-led productrecommendations
•Improve Customer Life-cycle Experience - Enable personalized, channel-
agnosticcustomer journeySIA — SBI INTELLIGENT ASSISTANT.State Bank of India
(SBI) is currently beta-testing its intelligent assistant called SIA — SBIIntelligent
Assistant. SIA is a chatbot which is aimed at handling customer queries and helpguide
them through the various retail products and services offered by SBI. SIA
works onartificial intelligence and is an effort by SBI to identify work processes that can
be transferred torobots so that human resources can be more creatively used.The SIA
chatbot has been developed by Allincall, a startup backed by IIT Bombay. It makes useof
machine learning and bot experience to be able to respond to customer queries.The chat
assistant, known as SBI Intelligent Assistant, or SIA, will help customers with
everydaybanking tasks just like a bank representative, the company said in a statement

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 .

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Artificial Intelligence Banking In India

According to PwC FinTech Trends Report (India) 2017, global investment in AI applications
touched USD 5.1 billion
(Euro 4.3 billion) in 2016. Not only PNB but banks like SBI, HDFC, ICICI, HSBC and
Axis banks in India have
turned towards AI.

State Bank of India (SBI)

SBI launched a national hackathon called ‗Code For Bank‘ for developers, startups and
students to come up with innovative ideas and solutions for banking sector that focuses
on technologies such as predictive analytics,
fintech/blockchain, digital payments, IoT, AI, machine learning, BOTS and robotic process
automation. The bank is
currently using an AI- based solution developed by Chapdex (the winning team from its first
hackathon), that captures
the facial expressions of the customers and helps them in understanding the behavior of its
customers

HDFC Bank
HDFC bank has developed an AI- based chatbot called ‗Eva‘ (Electronic Virtual
Assistance), built by Bengaluru-
based Senseforth that has addressed over 2.7 million customer queries, interacted with over
530,000 unique users, and
held 1.2 million conversations. The device can provide answers in less than 0.4 seconds and has
in the first few days
of its launch answered more than 100,000 queries from thousands of customers from 17
countries. The bank is also
experimenting with in-store robotic applications called IRA (Intelligent Robotic Assistant).

ICICI Bank
ICICI bank has deployed software robotics in over 200 business processes across various
functions of the company.
Calling it the robotic software the bank claims it to be the first in the country and among very
few in the world to
deploy this technology, that emulates human actions to automate and perform repetitive,
high volume and time
consuming business tasks.

Axis Bank

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Axis Bank recently launched an AI and NLP (Natural Language Processing) enabled app for conversational
banking,
to help consumers with financial and non-financial transactions, answer FAQs and get in touch with the bank
for loans.

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benefits of ai for banking sector fraud detection :
Anomaly detection can be used to increase the accuracy of credit card fraud detection and anti-
money laundering.

Customer Support and Helpdesk:

Humanoid Chatbot interfaces can be used to increase efficiency and reduce cost for customer
interactions.

Risk Management:Tailored products can be offered to clients by looking at historical data,

doing risk analysis, and eliminating human errors from hand-crafted models.

Security:
Suspicious behaviour, logs analysis, and spurious emails can be tracked down to prevent
and possibly predict
security breaches.

Digitization and automation in back-office processing:

Capturing documents data using OCR and then using machine learning/AI to generate insights
from the text data can
greatly cut down back-office processing times.

Wealth management for masses:

Personalized portfolios can be managed by Bot Advisors for clients by taking into account
lifestyle, appetite for risk,
expected returns on investment, etc.

ATMs:
Image/face recognition using real-time camera images and advanced AI techniques such as
deep learning can be
used at ATMs to detect and prevent frauds/crimes.

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22
 Chapter-2
Literature review

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 LITERATURE REVIEW

(Hickam Sadok, 2022) This article explores the effects of artificial intelligence (AI) use on
banks and other financial organisations' credit score assessment processes. These restrictions
serve as the foundation for a new age of economic law that introduces the certification of AI
algorithms and bank-used data.

(Chandrima Bhattacharya, 2022) Through this paper we understood that literature evaluation
and theoretical studies is completed for diverse worldwide and Indian banks with admire to the
combination of AI to enhance client interactions and inner banking processes. Chatbot
useinstances on banking systems are ranked primarily based totally on client experience.
Practical/Theoretical implications: Based at the entire image of AI integration with banking
operations, evolving Indian banks should recognition at the maximum famous use-instances to
draw customers. The correlation among Chatbot use-instances can also additionally gain the
installed Indian banks to similarly amplify business.

As discussed in (Board, 2017) the loss of interpretability or “auditability” of AI and gadget

getting to know techniques may want to come to be a macro-stage danger. Similarly, a
significant use of opaque fashions can also additionally bring about unintentional consequences.
As with any new product or service, there are vital problems round suitable danger control and
oversight. It might be vital to evaluate makes use of AI and gadget getting to know in view in
their dangers, which include adherence to applicable protocols on information privacy,
behaviour dangers, and cybersecurity. Adequate checking out and `training` of equipment with
impartial information and remarks mechanisms is vital to make sure programs do what they're
supposed to do. Overall, AI and gadget getting to know programs display full-size promise if
their unique dangers are well managed. The concluding phase offers initial mind on governance
and improvement of fashions, in addition to auditability through establishments and
supervisors.

(Neeraj Gupta, 2020) As they discussed here, various financial institution-specific factors, such
as size, capitalization ratio, risk, price-to-earnings ratio, investment price, sales diversification,
labour productivity, and age, are analysed and their effects on financial institution performance
are discussed. The findings of the examination also show that the key factors of the
performance of commercial banks in India are financial institution size, non-appearing
mortgage percentage, and sales diversification. Additionally, the effects show that the impact of
financial institution size, financial institution age, workforce productivity, and sales
diversification on the overall performance of the Indian banks is significant during the
catastrophe length.

(Report, 2020) Says that these technologies have the ability to disrupt the manner we have
interaction with every other, function our businesses, or even how governments paintings for
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his or her citizens. Although the adoption of AI varies substantially throughout geographies,
there are wallet of industries even in the evolved nations which might be more and more
adopting AI to higher carrier their clients and produce in efficiencies of scale. The authorities
have said that for banks to fulfil India`s developing needs, they should harness technology
along with AI and huge data. Whether to enhance typical client experience, take extra
knowledgeable selections on credit score underwriting, come across frauds and defaults early,
enhance collections or boom worker efficiency, AI has the ability to convert India`s banks.

(Ankur Aggarwal, 2022) Explained that once all the banking offerings had been revolving
across the salaried or earners, it emerge as a crucial part of our life. Present look is primarily
based upon the scope of synthetic intelligence in client revel in and robot technique automation
in banking zone in India. Most of the client revel in associated factors confirmed right
correlation with AI primarily based total offerings through banks.

(Saloni Tripathi, 2022) Points out the dynamics of AI platforms in the banking industry and
how they are becoming a significant disruptor. Banks are facing challenges from current
technology that uses intelligent algorithms to replace human labour. Companies must integrate
AI into their business strategies and practises to stay competitive.

(Omar H. Fares, 2022) The findings show how three important study areas—Strategy, Process,
and Customer—are covered by the literature on AI and banking. A systematic consumer credit
solution application blueprint (Service Blueprint) that details the customer journey, front stage,
backstage, and support procedure in banks was also stated by him in his study paper.

(Sindhu J, 2019)In this study, artificial intelligence (AI) adoption in five Indian commercial
banks— SBI, ICICI, Axis, HDFC, and HSBC—is discussed with reference to cost-benefit
analysis. The data is gathered from secondary sources based on literature to determine the
information utilised in the banking business. Search for AI technology services offered in India.

(Mehrotra, 2019) In this, he discusses the possibility of artificial intelligence (AI) taking the
place of people in the banking and financial services industry, unwittingly bringing about the
demise of the individualised attention and personal touch that are the cornerstones of customer
satisfaction and delight in industries like banking and financial services, which are renowned
for their fiduciary and responsible nature. Additionally, he stated that human intervention
cannot be fully replaced by AI because it cannot handle complex personalised requests,
comprehend sentiments, establish trust, or emotionally connect with a customer in order to
capture his interest and earn his brand loyalty.

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Chapter3

Research
methology

26
ARTIFICIAL INTELLIGENCE IN BANKING SERVICE
 To increase productivity, humans have continually developed newer machines. Consider how human
movement was dramatically altered by the introduction of bicycles and subsequently automobiles, which
increased both the distance and the speed at which humans could move. These machines were built
using internal combustion engines and wheels, which were both generalpurpose technology. The most
recent general purpose technology, artificial intelligence (AI), is being utilised to revolutionise the
banking industry and commercial economics, just like the computer and Internet did before it
(Accenture). A significant disruption in the financial services industry is being caused by the
development of artificial intelligence, as more banks try to innovate under the aegis of AI powered
technology in order to enhance current business processes. For instance, AI is changing how we interact
with technology and moving some of the cognitive load from people to robots. You can probably just
ask Google, Siri, or Alexa now days when in the past we had to know where to go and what to do in
order to execute a task.
 The banking environment has recently been more volatile and competitive as a result of globalisation
and enhanced economic openness. Customers now demand superior treatment when using a company's
products or services, or put another way, there is a greater focus on their satisfaction. Due to cutting-
edge technology like artificial intelligence, which have become more prevalent in businesses over the
past several decades, the banking industry has been thriving, and customer loyalty will continue to rise.
Almost every business, from deposit-taking and lending to investment banking and asset management,
depends on artificial intelligence applications because of the way that the modern corporate environment
is structured. Therefore, autonomous data management that doesn't include human intervention might be
very beneficial to banks in terms of enhancing speed, accuracy, and efficiency. Four categories may be
used to group the numerous possible uses of AI in the banking sector. There are first front-office apps
targeted at clients and back-office programmes targeted at operations. The second issue is with the
regulations and laws governing trading and portfolio management. Most banks are still in the testing
stage, however some have fully incorporated modern technology into their operations. Third, online
banking fraud is investigated as a potential application for artificial intelligence. With the rise of online
and mobile payments, credit card fraud has swiftly become one of the most common types of
cybercrime. As a result, a lot of companies have started utilising artificial intelligence (AI) algorithms to
compare the amount and location of current credit card transactions to historical ones in order to validate
their legality in real time.
 Financial institutions are also experimenting with AI technology in the field of chatbots. Chatbots are
virtual assistants that can interact with bank clients via text or voice in an effort to meet their needs

27
 without employing a human employee. Financial institutions are also experimenting with artificial
intelligence (AI) to present data from reports and legal papers, such as annual reports, to extract the
necessary provisions. AI software may construct models by analysing data and using back testing to
learn from previous errors. Several already-existing financial technology tools have evolved into precise
AI solutions as time has gone forward. For instance, online financial planning tools that assist
consumers in making wiser purchasing and saving decisions, robot advisers that enable total automation
in some asset management activities, and A new PWC estimate suggests that artificial intelligence (AI)
might contribute close to $16 trillion to the global economy by 2030. Over $5 billion has reportedly
been invested globally on AI applications. By 2030, it is predicted that the banking industry would save
$1 trillion thanks to the implementation of AI, primarily as a result of branch closures.
 Technology advancements in more recent years have allowed AI to provide workplace cognitive
computing, which comprises incorporating algorithms into apps to assist organisational procedures
(Tarafdar et al., 2019).This calls for increasing the effectiveness of information analysis, creating more
accurate and reliable data outputs, and empowering workers to do high-level tasks. In recent years, AI-
based solutions have shown to be effective and valuable. However, many corporate leaders are still
unsure about how to strategically implement AI in their organisations. Despite 85% of corporate
executives saw AI as a crucial tool for offering organisations a sustainable competitive edge,
Ransbotham et al.'s (2017) research revealed that only 39% of company leaders had a strategic plan for
the usage of AI because they were unclear of how to apply it inside their organisations.
 Artificial intelligence (AI) is being used in banking in a variety of settings, including the front office
(voice assistants and biometrics), middle office (complicated legal and compliance processes, antifraud
risk monitoring), and back office (credit underwriting with smart contracts infrastructure).

Front office Middle office Back office

 Conversational  Anti fraud  Underwriting
 Chat bot  auditing  Data processing

Front Office

Researchers have discovered that AI has had an impact on the whole banking industry. At this stage of banking
operations, voice help, chatbots, and biometric systems, among many others. This task was previously carried
out by people. These tasks must be allocated to an individual, however since AI has been used, things have

28
changed. Chatbots are crucial because they can interact with users like real people can. After-hours tech support
lines are now available 24 hours a day to respond to client questions. They are able to manage several queries.
Even after working for a longer period of time, they are careful and incapable of making mistakes. The cost has
decreased while the client experience has enhanced

. Back office

They are an important component of banking services. The middle level activities of banks have
been enhanced using AI. The intermediate level operations have a significant impact on all
banking scams. At this level, KYC, Antifraud ML, and other monitoring actions are carried out.
AI is used to support past transaction-based notifications and CIBIL monitoring. Banks can
benefit from more robotic process automation in areas such as loan approval, account opening,
automated report generating, anti-money laundering, and KYC. Some application areas of AI in
banking services include facial recognition for the initial transaction, micro-expression analysis
with virtual loan officers, biometric authentication and authorization, machine learning to detect
fraud and cybercrimes, and real-time transaction analysis to prevent fraud.

29
30
 (A) AI Transform Banking for Customer

Convenience is something that customers are always looking for. Customers could obtain a necessary service
even when banks were closed, for instance, making the ATM a success. More inventions have been stimulated
by that degree of convenience. With the use of their cellphones, customers can now open bank accounts from
the comfort of their couches. A decision management system (DMS) helps speed up the process of gathering
Know Your Customer (KYC) data while also reducing errors, which can help businesses with their turnaround
times. Also, company decisions can be rolled out without arduous procedures with the right business rules
software. According to McKinsey's global AI survey study, virtual assistants and conversational interfaces used
in frontoffice settings account for about 32% of all AI technology. Along with the usage of digital banking,
customer expectations are increasing. During the COVID-19 pandemic, internet usage grew by up to 50%, and
it is anticipated that this tendency would persist long after the pandemic has ended. Up to 45% of consumers
could soon cease regularly visiting branches. As a result, it is crucial to maintain and build a user-friendly
digital banking platform. The 24/7 assistance and recommendations offered by chatbots dramatically improve
the banking experience for customers.

B) How AI helps in Detecting and preventing Frauds in banking sector

Fraud and Anti-Money Laundering

Every day, a sizable number of digital transactions take place as customers use apps or online
accounts to pay bills, withdraw money, deposit checks, and do a variety of other tasks. As a
result, the banking sector must increase its efforts in cybersecurity and fraud detection.
Artificial intelligence in banking can be helpful in this situation. AI can help banks lower risks,
track system problems, and improve online banking security. AI and machine learning can
detect fraudulent behaviour fast and inform banks as well as customers. For instance, Danske
Bank, the largest bank in Denmark, switched from a rules-based to an algorithm-based fraud
detection system. AI offers anti-money laundering measures to protect its clients' money. AI
offers anti-money laundering measures to protect its clients' money. AI creates several methods
to protect bank accounts from such scammers.

IMPACT OF ARTIFICIAL INTELLIGENCE IN BANKING

31
Banks are using AI applications to recommend, forecast and execute tailored financial advice to customers and
also gain quick information on financial strategies, loan rates and the future market progress.
Customer Satisfaction: AI helps banks in providing personalized and more efficient services to
customers and in increasing revenue, faster decision making and having a good customer relationship.

Chatbots: Bot is the short form of Robot and Chatbot is an automated chat program that is either run
automatically or follows a pre-determined path. Chatbot is a way of using AI in the form of robotics in
banking. Chatbots are available 24/7 and provide efficient customer service.

Personalized Financial Guidance: AI helps customers to make easy and quick financial Decisions by
way of up to date information on the current market structure as well as provide suggestions on stocks
and bonds in which customers can invest.

Digital Wallets: Digital wallets provide digital money to the customers in purchasing any item online either
with a mobile phone or a computer.

Interactive voice response systems (IVRS): An automated voice system which interacts with customers,
answer certain questions and routing calls to appropriate banking departments and gives a pleasant
experience to customers.
Detecting Fraud: AI captures banking fraud by scanning through the vast transactional data and tracking
down any unorthodox activities or irregular behavior patterns. AI minimizes banking fraud, helps in
immediate action, protect security breaches and helps in powerful machine learning.
Improving customer support: Customer satisfaction impacts the performance of Banking industry and
shapes people’s perceptions of the financial institution’s brand and also influences banks client targeting and
retention efforts.
Helps in knowing Creditworthiness: Banks still rely on one’s revenue and banking transactions to determine
whether they are creditworthy. AI loan decision systems are using machine learning to observe the patterns
and behaviours to determine whether a user can really be a good customer or not.

Better Customer Services: Using data gathered from users’ devices, AI-based relay information using
machine learning by redirecting users to the source. AIrelated features also enable services, offers, and
insights in line with the user's behaviour and requirements.
32
 Better regulatory compliance: AI tools usually rely on cognitive fraud analytics that observe customer
behaviours, track transactions, identify suspicious activities, and assess the information of different
compliance systems. AI is providing greater value to customers through personalization, minimizing risks
and costs, improving employee productivity and ensuring higher regulatory compliance.

Risk management: Mitigating fraud by scanning transactions for suspicious patterns in real-time, measuring
clients for creditworthiness, and enabling risk analysts with right recommendations for curbing risk.

Trading and Securities: Robotic Process Automation (RPA) helps in security settlement through
reconciliation and validation of information in the back office with trades enabled in the front office. AI
facilitates the overall process of trade enrichment, confirmation and settlement.

Credit Assessment: AI can analyse all data sources together to generate a coherent decision. In fact, banks
today look at creditworthiness as one of their everyday applications of AI.

Portfolio Management: AI and machine learning-based technology platform disrupti customised portfolio
profiles of customers based on their investment limits, patterns and preferences. Banking and artificial
intelligence are at a vantage position to unleash the next wave of digital disruption.

Thus, AI has transformed every aspect of the banking processes faster, money transfers safer and back-end
operations more efficient.

33
34
35
 Types Of Artificial Intelligence

ANI – Artificial Narrow Intelligence It comprises of basic/role tasks such as those performed
by chatbots, personal assistants like SIRI by Apple and Alexa by Amazon.
AGI – Artificial General Intelligence Artificial General Intelligence comprises of human-level
tasks such as performed by self-driving cars by Uber, Autopilot by Tesla. It involves continual
learning by the machines
ASI – Artificial Super Intelligence Artificial Super Intelligence refers to intelligence way
smarter than humans.
Difference between Nlp, AI, Ml, Dl & Nn
AI or Artificial Intelligence building systems that can do intelligent things.
NLP or Natural Language Processing Building systems that can understand language. It is a
subset of Artificial Intelligence.

ML or Machine Learning Building systems that can learn from experience. It is also a subset of
Artificial Intelligence.

NN or Neural Network Biologically inspired network of Artificial Neurons.

DL or Deep Learning Building systems that use Deep Neural Network on a large set of data. It
is a subset of Machine.

This Photo by Unknown Author is licensed under CC BY

36
 CHAPTER-4
DATA COLLECTION
AND
INTREPRETATION

37
 SWOT ANALYSIS OF BANKING INDUSTRY

SWOT Analysis of Banking industry focuses on strength, weakness, opportunities and threats.
Strength and weakness are the internal factors opportunities and threats are external factors.
SWOT Analysis is a validated framework that helps to evaluate business performance of Banking Industry.
Banks are the need of today’s world. Everybody needs banking. Banking fulfills need of Industries and
individuals. Bank provides various types of services to the people who are in business or in Service. Banking
Industry is taking new shapes to provide financial services to customers. Banking has developed from
traditional banks to mobile banking, and internet banking solutions. Banks provide loans to SMEs Individuals
and Corporate. For a very long time, one of the oldest remaining styles of financial institution banks has been
around.

Strength:-
• Banking Industry is the Oldest Industry: Due to Technological advancement Industries are changing their
structure. Banking has also changed its structure and system. Banking Industry has proved to be one of the wide
spread and widely acknowledged industry. It has also supported the human race. Banking has adapted and
updated itself to suit the new needs. Banks today play a critical and indispensable role in society, from
inculcating the habit of savings to helping people with financial instruments.
• Financial Stability of Nation: In ensuring a nation’s economic growth and financial stability, the banking
industry plays a vital role. By fostering prosperity, banks contribute to the economy. They assist the masses to
maintain their resources and become important contributors to both the national and international economy.
• Supplier of Financial Instruments: Banks have a wide range of financial instruments for their customers. Fixed
Deposits, Stocks, bonds, insurance and savings accounts are some of the varied products sold by banks.
Furthermore, to provide online banking solutions, banks have also embraced and incorporated digital
technologies.
• Good Employment Source and Helps in GDP growth: There is a widespread consensus that perhaps the
improvement of the financial system leads to economic growth. Financial development establishes
encouraging conditions for growth by either supply-led (financial development stimulates growth) or demand-
driven growth. It is this industry that works constantly to ensure financial stability, encourage foreign trade ,
promote jobs and reduce poverty around the world.
• Financial Assistance: whether natural calamity or man-made calamity banks alleviate the after-effects of
disaster by offering financial assistance to victims to rise up and lead a peaceful life again.

Weakness:-

38
• Global Economics Susceptibility: Due to Exchange Rate changes and changes in world economy banking
Industry is effected. It is also seen that slight shifts in the exchange rates of currencies or the spending and
saving patterns of the citizens of one major nation can directly impact the entire banking industry.
• Non Performing Assets: The major weakness of the banking sector is NPAs (NonPerforming Assets).
Typically, NPAs denote loans that are not recoverable. This leads to financial losses for the bank, inevitably.
For the banking sector and the economy as a whole, NPAs can have a debilitating impact. Developing countries
like India face instances of high NPAs that have dealt a significant blow to the nation’s banking industry.
• Lack of coverage in rural areas: It has been observed that the banking industry focuses more on urban areas in
most countries, while rural regions are ignored. In the banking sector, this is a considerable weakness. Villages
are now home to a significant majority of the world’s population. In developed countries, this is more. Banks
are working in main stream don’t want to concentrate on mainstreams. Banks must try to capture Rural
Markets.

Opportunities:-
• Advancements in Technology: The banking industry has always based on technology. This is evident that
digital services provided by banks today are totally based on technology. However, banks should continue to
adopt the latest technological advances. To draw future generations, they should focus on putting out newer
goods and services.
• Opportunities for rural growth: One of the banking industry’s weak points is its limited presence in rural
areas. But this vulnerability can actually be turned into an opportunity. Banks will increase their customer base
considerably by expanding into villages and providing their services to the rural population.
• Societal Evolution: Both economically and culturally, human society is changing. The needs and demands of
customers with increasing income levels are bound to change in this complex landscape. It is necessary for
banks to adapt to this changing society. The sector will solidify its position in the future by offering better
services.
• Rising in the private banking sector: the banking industry around the world is highly regulated by Public
sector banks and their respective central banks. With the emergence of private sector banks, this sector is
experiencing structural and functional shifts, primarily due to the adaptation of new technology and intensified
competition, thereby benefiting end-customers.

Threats:-
• Lack of Cyber Defence Proper: The current banking industry relies entirely on the cyber-world. Whether it is
data storage, monetary transactions or personal information, everything is stored digitally. This makes the
banking sector a primary target for hackers who are seeking to benefit financially by leveraging flaws in the
banks digital infrastructure. Unless banks take effective cybersecurity steps to safeguard their records, they will
face a significant cyberspace threat.

• Competition Stiff: Worldwide, banks face stiff competition. Not only from other
banks, but also from institutions like Non-Banking Financial Companies that sell a

39
range of financial products that are not available to all banks. This has contributed to
a change of the consumer base from banks to NBFCs, which are more embraced by the new skilled breed.
• Global Uncertainty in Economics: The world is going through difficult economic times at present. The
international banking sector has all been affected by trade wars, protectionist policies, and economic downturns.
If the world’s economic conditions do not change, banks will face a bleak future.
• Recession: This is one of the biggest challenges to the nation’s financial system. The traumatic shock of
economic crises and the collapse of a number of companies will impact the banks and vice versa.
• System stability: the failure of certain poor banks has also undermined the stability of the system. Government
Regulations can directly effect the Banking Sector of a country.

40
Self-Learning AI and Adaptive Analytics
AI and ML excels at securing customer accounts, which fraudsters make exceedingly challenging and
dynamic. Adaptive solutions designed to sharpen the reactions, at small judgments, for continuous improvement
of performance should be considered by fraud detection experts. These transactions, which are very close to the
trigger of the investigation, are either slightly above or slightly below the threshold. If there is a narrow window
between a false positive event (a legal transaction that has scored too well) and a false negative event (a
fraudulent transaction that has scored too low), accuracy is extremely crucial. Adaptive analytics, which
provides a current understanding of a company's risk characteristics, emphasises this contrast. By immediately
reacting to freshly established case disposition, adaptive analytics systems increase sensitivity to shifting fraud
trends, enabling a more accurate distinction between frauds and non-frauds. Whether a transaction is found to
be legitimate or fraudulent, the results of an analyst's examination are sent back into the system. By doing this,
analysts can accurately describe the fraud environment in which they are working, including new tactics and
long-dormant misleading fraud practises. The model is automatically modified using this adaptive modelling
approach. The underlying fraud models' anticipated feature weights are automatically modified by this adaptive
modelling method. It is a useful technique for improving fraud detection at the edge and stopping new fraud
attacks.
Using supervised and unsupervised AI models together for safeguarding
Supervised learning, which is based on many precisely "classified" transactions, is the most popular type of
machine learning. Every transaction is classified as fraudulent or not fraudulent. Large volumes of labelled
transaction data are ingested to train the models, which then look for patterns that most strongly suggest genuine
activities. How much clean, pertinent training data was used to create a supervised model has a direct impact on
how accurate it is. Unsupervised models are used to find abnormal behaviour when there is little or no
annotated transaction data. In these situations, self-learning must be employed to uncover the data patterns that
traditional analytics miss.

C) Cost Benefits to banks by AI and ML

The BFSI business is using artificial intelligence (AI) more and more. Around 85% of banks, according to IDC,
installed AI solutions last year to allow intelligent choices and automated processes for corporate know-your-
customer (KYC) procedures, significantly cutting the time it takes to authorise enrolments for new corporate
accounts. Moreover, personalization, efficiency, and reaction time are all being enhanced by AI technology and
conversational interfaces.
According to Autonomous Next study, banks may save an estimated $447 billion overall by 2023 thanks to AI
applications, with $416 billion going to front and middle office costs.
According to the financial research firm Autonomous, there are an estimated 22 billion smart computing
devices worldwide, outnumbering people by a factor of three. A recent study by Autonomous also
shown that traditional financial institutions may save expenses by 22% by 2030 by utilizing
artificial intelligence technologies. Today, banks have a huge chance to use artificial
intelligence to advance their operations while also boosting client happiness

41
According to Forbes, 51% of businesses consider cost savings as the main advantage of artificial intelligence
technology. Furthermore, according to a 2019 estimate by Juniper Research, operational cost savings from
chatbot use in banking would rise to $7.3 billion globally by 2023 from an anticipated $209 million in 2019.

How is AI cutting cost by adopting AI?

1) Chatbots and Digital Banking A bank must pay its expenses through physical offices, which include
buying/renting the building, power consumption, and salaries of the human workforce. AI-supported
Chatbots can be used to mimic the human representative, allowing banks to operate for an extended time
without giving any over-time and handle multiple clients at the same time with the same accuracy.
For example: With a total revenue of US$6.394 billion and a net income of US$1.721 billion, Ally
Financial is a completely online bank. It provides managed portfolios, self-directed trading, and
mortgages. Discover Bank is also such example.
2) Reduce Human Workforce Errors to a Minimum AI can help to minimize errors caused by human
workforce, such as miscalculations and human error. Human mistake accounts for 38% of banking
losses, resulting in a loss of revenue. If a bank can minimize the loss, it can increase its net income.
Example: OSP offers Role-Based Access Control, AI-driven Audit Reporting, Audit Planning Systems,
clever Data Sampling, and Journal Entry Testing. High Radius offers Bank Reconciliation Cloud.
Source: EYGM Limited

D) AI for Credit Analysis

Traditional credit decisions are made using a small set of data points, such
as credit agency scores and information from a borrower's application. Credit risk
has always been a difficult subject for banking majors because of the many
variables that go into determining a person's risk profile. The process is more difficult
42
for business borrowers because data from many time periods and attributes must be combined and analysed to
produce an overall picture of risk. By combining alternative information like utility bills and rent payments with
legally permitted information like the borrower's credit history with other lenders, an AI system may create a
more complete picture of the borrower.

43
 I
CICI Bank use ML and AI for credit analysis

Techniques of Zero Credit Touch the Bank was attempting to create "Zero Credit Touch" (ZCT) solutions,
wherein loan facilities could be granted without any credit intervention and additional information being
obtained from consumers. The following difficulties arise when developing ZCT techniques using conventional
credit underwriting models.
Many of the current ICICI Bank clients are not eligible for credit models that combine business rules and
scorecards. Customers who don't have salary accounts with the bank may have their required amount supplied
to them because their anticipated income is smaller in certain cases. Machine learning models have been
developed to address these issues by taking into account all customerrelated data that is currently available to
the bank, including transaction data from savings bank accounts, credit card usage patterns, repayment history
from credit bureaus, and other profilerelated data recorded in various CRM / internal platforms. To forecast
44
clients' income in situations where it is unavailable, a machine learning-based model of income prediction was
developed. These initiatives caused a sizable part of credit card and personal loan sourcing to occur through
ZCT tactics.

E) Challenges for adoption AI in India

AI is becoming increasingly popular in India, with 32% of financial service providers now
using it. AI is being used by banks like SBI, Bank of Baroda, HDFC, ICICI, Yes Bank, and
others to streamline their business processes. In the next two years, AI and humans will coexist,
according to 83% of Indian bankers, and 77% of them feel that banks must effectively create
and/or use AI solutions.

 Trained Manpower There aren't many strong data scientists who can work on AI.
Along with a lack of qualified human resources, banks also lack personnel that is up to
date on the newest equipment and software. The financial services sector must
collaborate with Indian institutions to recruit qualified data scientists and create internal
training programmes that would teach staff how to successfully deploy AI technology
for banking operations. Offering undergraduate and masters programmes in fintech,
universities throughout the world, including the US and UK, are starting to adjust to the
changes that AI is bringing about in the banking sector.

 User capacity It might be difficult to formulate requests or questions in a way that is

understandable to AI. Customers that utilise financial services come in a wide variety,
and their degrees of digital literacy vary widely, which makes the issue more difficult to
solve. Only when the customer- provided data is relevant and understandable by the AI
algorithms in use can a financial/banking service be considered effective. They may then ask questions,
and the AI systems will be able to recognise them and provide an appropriate answer.

 Multiple Languages Given the diversity of Indian languages, the AI-enabled

communication systems that reach most Indians in their first or preferred languages
would be the most effective. Due to the small machinereadable corpus of vernacular
languages available for the training of natural language processing and creation
algorithms, this is currently a difficulty. Now, there are significant disparities between
AI that can process and grasp local languages and AI that only works in English or
bilingual mode. An AI-based communication platform must be able to comprehend the
customer's spoken language and respond in that language while providing banking or
financial services.

45
 Data privacy and protection AI systems need a lot of training data as an input.
Consumer data is gathered by monitoring customer behaviour both online and offline,
archived, and combined with data from other sources to create big data sets. These data
set often include details on transactions, emails, videos, search inquiries, health records,
and activities on social media. Unauthorized access to this data frequently occurs due to
security flaws and unsecured servers. Cyberattacks against India ranked second between
2016 and 2018, so it is important to use the same language as the consumer when
communicating with them.

46
47
CHAPTER – 5

FINDING AND
CONCLUSION

48
FINDINGS

1) Financial services that are focused on building a personal connection with the client in order to offer
automated financial advice as well as professional guidance for helping clients make financial
decisions. Moreover, it assesses market volatility and makes recommendations on how users can
manage their portfolios in order to meet their financial objectives .

2) Since technology enables users to access financial services with voice commands and touch displays,
physical presence is gradually vanishing. Natural language processing technology can process queries
to provide information, respond to inquiries, and link consumers to other financial services. As a result,
efficiency is systematized, reducing human error.

3) The Indian banking industry is being impacted by artificial intelligence. The major players in the
banking industry are incorporating artificial intelligence technology into some of their processes to
improve the efficiency of banking. As a result, the banking industry will have more time to devote to
other tasks that will enhance banking operations and relieve it of tedious tasks.

4) When voice processing and natural language processing technologies are improved, customer
concerns relating to the banking sector will be answered quickly. The time when computers might
handle the majority of customer support inquiries is rapidly approaching. Due to the elimination of line
waiting, effective customer service would ensue.

5) We have identified that banking system are lacking behind in its back-end operation with respect to
protection as data suggests that around 15%-20% is being allocated to data privacy and protection and
also reveals that bank focusing more on generating revenue.

6) Nowadays, the majority of Indian banks are either preparing to employ AI to boost customer
effectiveness and/or operational efficiency, or they have already tried out certain AI/ML models. Yet, it
is crucial for each organization to evaluate where they are in the AI process and what level of maturity,
they have for building and owning a production-grade AI/ML system. The bank may then choose a
course of action based on the assessed degree of maturity.

7) It has been identified that banks have major challenge of acquiring huge amount data of every
customer because for the purpose of training AI algorithms, an organization must spend in the
production and storage of substantial volumes of data. The quantity and quality of data that these
businesses have captured or retained are connected to the AI dividends that have been generated.

49
 CONCLUSION

From the above study we came to the conclusion that banks are experimenting with

and utilising artificial intelligence (AI) to modify how customers are handled, as the

technology is growing in popularity. The banking industry will benefit greatly from

artificial intelligence in the future. Customers now have more flexibility to

complete transactions whenever they want, wherever, without having to stand in

huge queues at the bank thanks to the introduction of AI. The purpose of artificial

intelligence is to provide highly customised, high-quality services that are also

quick and efficient. With the help of AI bank has reduce the cost on the repetitive

task by automation. AI has also help bank to reduce fraud and to analysis the credit

risk but still there are many challenges in front of AI. Challenge of acquiring huge

amount of data of every customer of bank. Bank has to spend money on the

production and storage of data. But still AI has bright future in banking sector as

time will move forward there will be improvement in AI.

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Website Links
1) How AI Is Making Banks Cost-Effective and Efficient
2) https://datatechvibe.com/ai/how-ai-is-making-banks-cost-effective-and-efficient/
3) Banking of Tomorrow: Top Indian Banks Using Artificial Intelligence
4) https://www.analyticsinsight.net/banking-of-tomorrow-top-indian-banks-using-
artificialintelligence/
5) AI-bank of the future: Can banks meet the AI challenge?
6) https://www.mckinsey.com/industries/financial-services/our-insights/ai-bank-of-
thefuture-can-banks-meet-the-ai-challenge
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7) Banking on Artificial Intelligence
8) https://www.wipro.com/business-process/why-banks-need-artificial-intelligence/
Books referred
1) AI and Future of Banking System by Tony Boobier,2020
2) Hands-On Artificial Intelligence for Banking: A practical guide to building
intelligent financial applications using machine learning techniques by Jeffery Ng,
2020

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