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Transactions on Engineering and Computing Sciences - Vol. 12, No. 4
Publication Date: August 25, 2024
DOI:10.14738/tecs.124.17427.
Kumar, A. (2024). Redefining Finance: The Influence of Artificial Intelligence (AI) and Machine Learning (ML). Transactions on
Engineering and Computing Sciences, 12(4). 59-69.
Services for Science and Education – United Kingdom
Redefining Finance: The Influence of Artificial Intelligence (AI)
and Machine Learning (ML)
Animesh Kumar
Computer Science, Illinois Institute of Technology, Illinois, USA
Computer Technology, Nagpur University, Maharashtra, State, India
ABSTRACT
With rapid transformation of technologies, the fusion of Artificial Intelligence (AI)
and Machine Learning (ML) in finance is disrupting the entire ecosystem and
operations which were followed for decades. The current landscape is where
decisions are increasingly data-driven by financial institutions with an appetite for
automation while mitigating risks. The segments of financial institutions which are
getting heavily influenced are retail banking, wealth management, corporate
banking & payment ecosystem. The solution ranges from onboarding the customers
all the way fraud detection & prevention to enhancing the customer services.
Financial Institutes are leap frogging with integration of Artificial Intelligence and
Machine Learning in mainstream applications and enhancing operational efficiency
through advanced predictive analytics, extending personalized customer
experiences, and automation to minimize risk with fraud detection techniques.
However, with Adoption of AI & ML, it is imperative that the financial institute also
needs to address ethical and regulatory challenges, by putting in place robust
governance frameworks and responsible AI practices.
Keywords: Artificial Intelligence, Machine Learning, Retail Banking, Predictive Analytics,
Fraud Detection, Customer Experience, Ethical Considerations, Regulatory Challenges
INTRODUCTION
The combination of Artificial Intelligence (AI) and Machine Learning (ML) is one area in which
the age-old financial system will witness a revolution - making it more efficient, precise, and
innovative. In this paper, we delve into changing the face of finance by AI & ML ranging from
personalised customer experience to advanced risk management and much more.
AI and ML have started transforming the financial sector by managing complicated tasks,
processing vast data volumes at lightning speed with accuracy, as well as predictive insights
that were unattainable earlier with traditional methods. In several ways, these advancements
are not only refining operational efficiencies in financial institutions but also improving the
decision-making abilities available with more personalized solutions that suit varied needs of
clients and stakeholders.
AI and ML are also substantially contributing to effective risk management with intricate
algorithms that can quickly detect anomalies, predict market trends by identifying patterns in
the data stream, adhering more rigorously than ever to stringent regulatory requirements. To
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Transactions on Engineering and Computing Sciences (TECS) Vol 12, Issue 4, August - 2024
Services for Science and Education – United Kingdom
protect themselves from potential threats, the agencies assertively test for vulnerabilities in a
financial system based on trust and reliable information.
Moreover, the contribution of AI/ML in finance has upgraded traditional services to tech- enabled solutions with automated investment advisory, trade algorithms and personalized
wealth management solutions. These advancements are empoweringfinancial services,
enabling both individuals and businesses to make informed decisions and complete their
respective fiscal goals more efficiently than ever.
Summing up, it is not merely a technological upgrade but a change in thinking for the finance
industry to leverage AI and ML as a transition from traditional mode of doing business which
will open unimaginable areas of growth, efficiencies besides customer centricity. As banks and
other financial institutions adopt these technologies, they are set to alter the state of finance by
opening up new opportunities and creating unprecedented value within an ever more
interconnected global economy.
From retail banking to corporate banking, from property and casualty to personal lines, and
from portfolio management to trade processing, the next wave of digital disruption in financial
services has been unleashed by the concepts and applications of Artificial Intelligence (AI) and
Machine Learning (ML). Together, AI and ML are undoubtedly creating one of the largest
technological transformations the world has ever witnessed. Within the advanced streams of
research in AI and ML, human intelligence blended with the cognitive reasoning of machines is
finally out of the labs and into real-time applications.
The Financial Services sector is one of the early adopters of this revolution and arguably much
ahead of its leverage compared to other sectors. Built on the conceptual foundations of
Innovation diffusion, and a contemporary perspective of enterprise customer life-cycle journey
across the AI-value chain defined by McKinsey Global Institute (2017), the current study
attempts to highlight the features and use-cases of early-adopters of this transformation. With
the theoretical underpinning of technology adoption lifecycle, this paper is an earnest attempt
to comment on how AI and ML have been significantly transforming the Financial Services
market space from the lens of a domain practitioner, [1-2].
WHAT IS AI & ML
AI (Artificial Intelligence) and ML (Machine Learning) are the two most powerful words that
have revolutionized every industry from finance to healthcare, retail as well more.
Artificial Intelligence (AI)
The Actual Meaning:
The ability to simulate human intelligence in systems that are capable of thinking and learning
like humans. It involves a wide spectrum of strategies and methods processed towards
empowering computers to do things which are exclusive to human Intelligence. Few of the
areas include optical recognition, speech acknowledgment, senses deduction, decision-making
with language understanding.
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Kumar, A. (2024). Redefining Finance: The Influence of Artificial Intelligence (AI) and Machine Learning (ML). Transactions on Engineering and
Computing Sciences, 12(4). 59-69.
URL: http://dx.doi.org/10.14738/tecs.124.17427
AI Can Be Classified into Two Main Streams
Narrow AI (Weak AI):
Category of artificial intelligence that acts optimally when applied to a task constrained within
very specific parameters. It includes voice assistants, e.g. Siri or Alexa; recommendation
systems and face verification software etc.
Artificial General Intelligence/AGI (Strong AI):
Refers to intelligent systems that can understand, learn and apply human intelligence for any
task. General AI is actually not a reality but only the concept extension of it.
Machine Learning (ML)
ML (Machine Learning) is a type of AI that provides computers with the ability to learn without
being explicitly programmed and therefore allow them to predict, make decisions based on
data. Unlike traditional programs, which are purposefully built to perform specific tasks, ML
algorithms use data (by feeding it back in through the loop) in order to train themselves and
make better future decisions.
Types of Machine Learning
Supervised Learning:
In the case of supervised, an algorithm should learn from labeled data. It is the approach of
mapping function from input to output and it learns this mapping by predicting on new data.
Unsupervised Learning:
In this, an algorithm is trained on unlabeled data and learns patterns without relying on any
form of guidance.
Reinforcement Learning:
Reinforcement learning is an area of machine learning that focuses on how actions should be
taken in the environment so as to maximize some notion of cumulative reward.
AI and ML are disrupting industries by automating processes, strengthening decision-making
ability, and providing a personalized approach to customers. Applications, which range from
autonomous vehicles and medical diagnostics to financial trading have transformed the way
businesses operate and deliver value in this digital age.
AI and ML have proven to be game-changers for the financial sector. These technologies grant
financial institutions the ability to process exabytes of data at unparalleled velocities and levels
of accuracy, sustenance by better understanding across multiple dimensions. With AI
algorithms, businesses are able to detect anomalies in real-time and optimize investment
strategies as well as predict future market trends and intelligent risk management practices. In
addition, ML Models also does an outstanding job in everyday tasks e.g., detecting frauds or
making customer service easier and thus leaving resources for more strategic plays. This
enables organisations to streamline operations and reduce costs, significantly speeding up the
time taken for financial services deployment.