REVOLUTIONIZING BANKING OPERATIONS: THE ROLE OF ARTIFICIAL INTELLIGENCE IN AUTOMATED TRADING AND COMPLIANCE

Banking Innovation ISBN 978-93-93996-89-3 192
15
REVOLUTIONIZING BANKING OPERATIONS: THE
ROLE OFARTIFICIAL INTELLIGENCE IN
AUTOMATED TRADING AND COMPLIANCE
DR. DILIP.S.CHAVAN
(M.Com, Ph.D, MBA (FIN),SET, MPM (HR), DTL, GDC &
amp; A)
Associate Professor and Research Guide
Department of Commerce and Management
SBES College of Arts and Commerce, Aurangabad
Dr. SHALLU SEHGAL
Associate Professor
Department of Business Management
Shoolini Institute of Life Sciences and Business Management,
Solan (H. P.)
PRIYANKA PATHANIA
Assistant Professor
Department of Business Management,
Shoolini Institute of Life sciences and Business Management,
Solan, H.P., India
Abstract
Artificial intelligence (AI) is rapidly transforming the banking
industry, enabling banks to offer more efficient, personalized, and
secure services to their customers. This paper explores the various
banking innovations that have been made possible through AI,
including chatbots, fraud detection, loan underwriting, and
personalized banking services. We also discuss the potential
benefits and challenges of AI adoption in the banking sector and
provide recommendations for banks looking to implement AI

technologies. AI can help banks provide seamless, around-the-
clock service to their customers, but this technology isn't limited
to retail banking apps. Artificial intelligence could be useful in
the back and middle offices of investment banking, as well as in
other parts of the financial sector. All the major banks in India
were privately owned before independence, so the government's
plan to nationalize them caused alarm among those who still
relied on money lenders to get by in the countryside. In 1949, the
government of India took over the Reserve Bank of India. When
a country's banking system is nationalized, it benefits the
economy, increases employment opportunities, and helps the
agricultural and rural sectors of the economy.
Key words: - Artificial Intelligence, Fintech, block chain, banking
technology, Intelligence.
Introduction:
In recent years, the financial services sector has been going
through a period of fast transformation, which can largely be
attributed to developments in digital technology. The term
"artificial intelligence" (AI) refers to a set of computer
capabilities that have recently become increasingly important in
the realm of financial innovation. These capabilities include the
ability to automate mundane jobs, improve customer service, and
strengthen risk management. This article investigates the myriad
of ways in which AI is presently reshaping the banking sector, as
well as the potential advantages and disadvantages associated
with the implementation of AI. Artificial intelligence, also known
as AI, is quickly becoming the technology of choice for
businesses all over the world that want to provide individuals with
a more customized experience. The technology is continually
evolving to become more advanced and intelligent, making it
possible for a wider range of businesses and industries to utilize
AI in a variety of contexts. The financial services industry is
quickly becoming one of the earliest users of AI. And just Banks,
just like other industries, are investigating and utilizing the

technology in a variety of different ways. The most fundamental
applications of artificial intelligence include making chatbots for
customer service more intelligent, tailoring services for individual
users, and even installing AI robots in banks so customers can
serve themselves. In addition to these fundamental applications,
banks can utilize this technology to improve the productivity of
their back offices, thereby lowering both the likelihood of fraud
and the dangers associated with it.
Scope
This paper covers Artificial intelligence and its technological
advancements in the banking industry.
Objectives:
1. To give an overview of Artificial Intelligence.
2. To study the application of Artificial Intelligence in the
Banking Sector.
Methodology:
The study is descriptive in nature and is based on secondary data.
The information was gathered from a variety of reports, journals,
news stories, several bank websites, the RBI portal, and other
resources.
Overview of Artificial Intelligence:
The field of artificial intelligence (AI) involves the development
of computer systems that can perform tasks that typically require
human intelligence, such as learning, reasoning, problem-solving,
perception, and decision-making. AI systems can be classified
into three categories: rule-based systems, machine learning
systems, and cognitive computing systems.
Rule-based systems, also known as expert systems, use a set of
rules and logical deductions to make decisions. These systems are
often used in fields like medicine and law, where there are well-
defined sets of rules that need to be applied consistently. Machine
learning systems, on the other hand, use statistical algorithms to
learn from data and improve their performance over time. There

are three main types of machine learning: supervised learning,
unsupervised learning, and reinforcement learning. In supervised
learning, the system is trained on a labeled dataset and learns to
make predictions based on that data. In unsupervised learning, the
system learns to identify patterns and relationships in unstructured
data. In reinforcement learning, the system learns through trial
and error, receiving feedback in the form of rewards or
punishments. Cognitive computing systems are designed to
simulate human thought processes, using techniques such as
natural language processing, image recognition, and sentiment
analysis. These systems are often used in fields like customer
service and marketing, where the ability to understand and
respond to human emotions is important.
Some of the key applications of AI include:
Natural language processing: AI systems can analyze and
understand human language, allowing them to perform tasks like
language translation, speech recognition, and text-to-speech
conversion.
Image and video recognition: AI systems can analyze visual
data and identify patterns and objects within images and videos.
Robotics: AI is used to control and program robots to perform
tasks like manufacturing, assembly, and inspection.
Autonomous vehicles: AI is used to control self-driving cars and
other autonomous vehicles.
Healthcare: AI is used in fields like medical diagnosis, drug
discovery, and personalized medicine.
Finance: AI is used in fields like fraud detection, risk assessment,
and trading.

Figure 1: Evolution of Artificial Intelligence
Source: Overview of Artificial Intelligence and NLP in Big Data -
Don (jamieson-don.azurewebsites.net).
The term "artificial intelligence" (AI) refers to the broad field of
study concerned with programming machines to mimic human
intellect in areas including perception, reasoning, learning,
problem solving, and decision making. There are primarily two
types of AI systems, and they are "classic" AI and "state-of-the-
art" AI. Rule-based systems and expert systems provide the
foundation of classical AI, while machine learning and deep
learning form the backbone of contemporary AI.
To help machines learn from data on their own, without being
explicitly programmed, researchers have focused on a branch of
artificial intelligence known as machine learning (ML). Three
broad types of ML algorithms exist: supervised learning,
unsupervised learning, and reinforcement learning. In supervised
learning, the algorithm is trained using labeled data, while in
unsupervised learning, it is trained using data that has not been

labeled. Training an algorithm to make choices in response to
rewards and penalties is what reinforcement learning is all about.
The subject of Artificial Intelligence known as Natural Language
Processing (NLP) is concerned with teaching computers to read,
comprehend, and even create new human language. The field of
natural language processing (NLP) entails the creation of
algorithms and models that can handle text data, speech data, and
other forms of natural language input. Text categorization,
emotional analysis, naming names, machine translation, and voice
recognition are all examples of NLP methods.
To create artificial neural networks (ANNs) that can learn from
massive datasets is the primary goal of Deep Learning (DL), a
subfield of ML. Training ANNs with several layers of neurons to
recognize patterns and make predictions is at the heart of DL
algorithms, which take their cues from the workings of the human
brain. Success stories for DL include applications in NLP, speech
recognition, and image recognition.
Neural Networks are a class of algorithms that attempt to mimic
the brain's structure and behavior. Deep Learning necessitates the
use of Neural Networks, which are made up of a large number of
interconnected artificial neurons. They are programmed to
analyze data and draw conclusions depending on what they find.
Image and speech recognition, NLP, and even game playing are
just some of the difficult problems that have been successfully
tackled by Neural Networks.

Figure-2: Differnce between AI, NLP, ML.DL and Neural
Networks
Source: Designed by Author2—
Dr Shallu Sehgal
Artificial Intelligence and Indian Banks
In recent years, AI has emerged as a crucial resource for the
financial sector. Financial institutions are adopting AI to boost
productivity, enrich interactions with customers, and mitigate risk.
Chatbots and digital assistants are two of the most common
applications of AI in financial institutions today. Customers can
get details about their purchases, subscriptions, and more with the
use of these tools. They can also help financial institutions save
money on customer service by automating mundane tasks like
responding to frequently asked questions.
Artificial intelligence (AI) is also being utilized to improve
banking fraud detection and prevention. Artificial intelligence
systems are able to detect potential fraud by evaluating massive
amounts of transaction data for abnormalities and patterns. As a
result, banks will be better able to safeguard their operations and
reduce the likelihood of monetary losses as a result of fraud
occurring in real time.

Additionally, financial institutions are implementing AI to
enhance credit risk evaluations. Artificial intelligence algorithms
can improve credit risk assessments by analyzing data from
multiple sources, such as credit reports, bank records, and even
social media profiles. By doing so, financial institutions may
better assess creditworthiness and issue loans with lower default
rates and higher quality.
So, banks are making use of AI to streamline their back-office
processes. AI can assist financial institutions in lowering
operational expenses and increasing overall efficiency by
automating mundane processes such as data entry and
reconciliation.
State Bank of India
Using technologies like predictive analytics, fintech/blockchain,
digital payments, the Internet of Things (IoT), artificial
intelligence (AI), machine learning (ML), bots (robotic process
automation), and more, SBI has launched a national hackathon
called "Code For Bank" to encourage developers, startups, and
students to come up with innovative ideas and solutions for the
banking sector. The bank is presently utilizing Chapdex (the
winning team from its inaugural hackathon), an AI-based
technology that records consumer facial expressions and provides
insights into customer behavior.
Bank HDFC
HDFC Bank's "Eva" (Electronic Virtual Assistance) chatbot,
developed by Bengaluru's Senseforth, is an artificial intelligence
(AI)-based virtual assistant that has responded to 2.7 million
client questions, communicated with 530,000 users, and had 1.2
million interactions. More than 100,000 questions were answered
by the device in its first few days of release, from thousands of
users in 17 countries. The bank is also exploring the use of an
IRA (Intelligent Robotic Assistant) robot in-branch.
The ICICI Bank

Over 200 different business processes at ICICI Bank have been
automated using software robots.
The bank has implemented what it calls robotic software, which it
says is the first of its kind in the country and one of just a handful
of institutions worldwide to use the technology.
Bank of Axis
Axis Bank has now released a conversational banking app
powered by AI and NLP (Natural Language Processing) to assist
customers with banking and non-banking needs, such as
answering frequently asked questions and connecting with the
bank about loans.
Figure:3 Consumer Fintech Adoption Rates Worldwide
Source : https://balancingeverything.com/fintech-statistics/
The Role of Artificial Intelligence in Banking: Benefits,
Drawbacks, and Emerging Applications
The term "artificial intelligence" is used to describe the ability of
robots to mimic human intellect by acquiring new skills and
knowledge through observation and experience. Artificial
intelligence (AI) is being put to use in the banking industry to
customize banking experiences for customers and automate

mundane operations like customer care, fraud detection, and loan
underwriting.
Chatbots:
Chatbots are one of the most obvious applications of AI in the
financial sector. Chatbots are artificially intelligent messaging
programs that employ NLP and machine learning to mimic human
communication. Financial institutions are increasingly turning to
chatbots to field frequently asked queries from customers and
deliver account details around the clock. Complex operations, like
as completing an application for a loan or mortgage, are well
within the scope of chatbot assistance to customers.
Figure : Role of Artificial Intelligence in Banking
Source: Designed by Author2
- Dr Shallu Sehgal
Finding Conspiracies:
Artificial intelligence is also having a major effect in the field of
fraud detection. Artificial intelligence algorithms can sift through
mountains of data, such as account histories, transaction records,

and information from other sources, in an effort to detect fraud.
Financial institutions are increasingly relying on fraud detection
systems powered by artificial intelligence to keep an eye on
customer transactions in real time and flag anything out of the
ordinary.
Underwriting a Loan:
The underwriting of loans is another area where AI is being
utilized to save time and effort. Underwriting a loan the old-
fashioned way might be a laborious process. In order to evaluate
creditworthiness and predict the possibility of default, AI
algorithms can examine credit histories, income statistics, and
other pertinent information. This might make it quicker for people
to apply for loans and simpler for banks to assess those
applications.
Customized Banking Products and Services:
Banks can provide individualized product suggestions, focused
marketing efforts, and unique pricing by evaluating consumer
data such as transaction records and web browsing habits. The
result could be greater client satisfaction and loyalty at financial
institutions.
Advantages and Drawbacks:
There is a wide range of possible benefits to adopting AI
technologies in banking, including increased productivity, greater
risk management, and better customer service. Data privacy and
security worries, regulatory constraints, and the possibility of
algorithmic bias are all major obstacles to the widespread use of
AI. When introducing AI technologies, banks should keep these
complications in mind and take precautions as necessary.
The usage of artificial intelligence (AI) in this field has been on
the rise in recent years. Investment advisory services are
becoming increasingly customized through the use of AI
algorithms by wealth management organizations. Wealth
managers can benefit from AI since it can help them spot
opportunities and threats in the market.

Voice assistants are another area where AI is having a major
impact in the banking industry. Hands-free banking services such
as checking account balances, making payments, and transferring
funds are now possible thanks to voice assistants. Voice assistants
are also being considered by financial institutions for handling
more involved processes like processing loan applications and
mortgage approvals.
The growing reliance of financial institutions on digital
technologies has made cybersecurity a top priority. Using AI,
financial institutions can better detect and respond to
cybersecurity threats in real time. Cyberdefense systems backed
by artificial intelligence may also examine massive datasets, look
for patterns, and anticipate security breaches.
Artificial intelligence (AI) is also being utilized in automated
trading, in which algorithms analyze market data and other
information to determine how to best allocate capital. Banks can
take advantage of market opportunities and reduce risk with the
help of automated trading due to the increased speed and accuracy
it provides.
Another area where AI is having a major impact in banking is
compliance. Financial institutions are employing AI algorithms to
scour regulatory data for red flags and spot problems with
compliance. This can help financial institutions guarantee they are
in compliance with regulations and prevent any penalties that may
result from not doing so.
Benefits of ai for banking sector
Credit card fraud and money laundering detection can both
benefit from the use of anomaly detection to improve accuracy.
Assistance Desk and Support Services:
Conversations with customers can be made more productively
and cheaply with humanoid Chatbot interfaces.
Risk Management

Effective risk management involves analyzing past data, doing a
risk analysis, and replacing manual, error-prone processes with
computer simulations.
Security
Tracking suspicious activity, analyzing logs, and investigating
bogus emails all contribute to a more secure system and can even
be used to foresee security breaches.
Digitization and computerisation in back-office processing:
Back-office processing times can be drastically reduced by the
use of digitization and automation techniques, such as optical
character recognition (OCR) for document data capture and
subsequent machine learning/AI for insight generation from the
text data.
Wealth management for the masses
Bot Advisors may manage customers' personalized portfolios by
factoring in lifestyle, risk tolerance, predicted returns, etc.
ATMs
Using real-time camera images and sophisticated AI techniques
like deep learning, ATMs can implement image/face recognition
to detect and prevent fraud and other crimes.
Conclusion:
In conclusion, AI is rapidly reshaping the financial services sector
by allowing banks to improve efficiency, personalization, and
safety for their consumers. Banks that implement AI technologies
report higher customer satisfaction, more efficient operations, and
better risk management. Nonetheless, there are substantial
obstacles to the widespread use of AI that must be carefully
considered. Banks who are able to meet these problems head-on
will have a huge competitive edge in the future of banking.
Artificial intelligence is having far-reaching effects on the
financial services sector. A tremendous competitive advantage
awaits financial institutions that can successfully leverage AI.
Adopting AI, however, comes with its own set of risks and

challenges that must be carefully considered and addressed before
moving forward.
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REVOLUTIONIZING BANKING OPERATIONS: THE ROLE OF ARTIFICIAL INTELLIGENCE IN AUTOMATED TRADING AND COMPLIANCE