Integration of Artificial Intelligence in the Financial Sector

Integration of Artificial Intelligence in the Financial Sector

Mohammad Kashif, Arokiaraj David, Vijay Prakash Gupta, Shahid Alam
Copyright: © 2024 |Pages: 13
DOI: 10.4018/979-8-3693-3264-1.ch005
OnDemand:
(Individual Chapters)
Available
$37.50
Current Special Offers
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

Artificial intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence. Learning, reasoning, problem-solving, vision, language comprehension, and even some types of decision-making are among these tasks. The goal of artificial intelligence (AI) is to build devices or software that can replicate cognitive processes and adjust to various environments, becoming more and more effective over time in the financial sector. Within the discipline of artificial intelligence (AI), machine learning (ML) focuses on developing models and algorithms that enable computers to learn from data and make decisions or predictions without explicit programming. This chapter introduces how machine learning is playing a pivotal role in the financial industry. Key concepts in machine learning for finance and advantages are also discussed.
Chapter Preview
Top

1. Introduction

1.1 Definition and Outline of Machine Learning

Machine learning (ML) is a subfield of artificial intelligence (AI) that concentrates on creating models and algorithms that let computers learn from data and make judgments or predictions without needing to be explicitly programmed. The basic idea underlying machine learning is to let computers identify trends, gain new perspectives, and eventually become more efficient as they process and analyze larger amounts of data (Chiu, 2016; Hickey & Conway, 2021; Thacker, 2020). Finding underlying patterns and relationships in datasets helps machines learn by enabling them to generalize and apply what they have learned to new, unobserved data. The area of machine learning is dynamic and ever-evolving, incorporating a range of methods such as reinforcement learning, supervised learning, and unsupervised learning, each customized for particular task types and data sets (Zavolokina et al., 2016).

There are multiple primary categories of machine learning, each with a specific function in addressing a range of issues. Models are trained on labeled datasets in supervised learning, where the algorithm learns to map input data to the appropriate output. In contrast, unsupervised learning—which is frequently applied to tasks like clustering and dimensionality reduction—involves identifying patterns and structures within unlabeled data. The goal of reinforcement learning is to teach models to make a series of decisions by interacting with their surroundings and picking up lessons via failure. Machine learning applications are used in a wide range of industries, including marketing, finance, healthcare, and more (Tian et al., 2015; Zavolokina et al., 2016). They tackle problems including speech and picture identification, natural language processing, and self-sufficient decision-making. As technology advances, machine learning continues to play a pivotal role in shaping the future of AI-driven solutions and innovation across a wide range of domains.

1.2 Evolution of Machine Learning in Finance

In the finance sector, machine learning has advanced remarkably, changing conventional methods and decision-making procedures. The chronology that follows outlines significant turning points and advancements in the use of machine learning to finance. In the field of finance, machine learning has progressed from simple statistical techniques to intricate algorithms that can instantly analyze enormous datasets. Applications include investing strategy automation, fraud detection, risk management, and customer experience enhancement. Future technological advancements and the use of machine learning into various financial activities are anticipated in the finance sector (Catania, 2021).

Table 1.
Evolution of machine learning
YearEvolution and Development of ML
1950sEarly applications of statistical methods in financial modeling.
1980sAdvent of algorithmic trading with the introduction of computer technology.
1990sIncreased use of neural networks for risk assessment and fraud detection.
2000sGrowing adoption of machine learning for credit scoring and portfolio management.
2008Financial crisis prompts the need for more sophisticated risk management models.
2010sRise of robo-advisors utilizing machine learning for automated investment advice.
2015Expansion of natural language processing for sentiment analysis in financial news.
2018Increased use of machine learning in anti-money laundering (AML) and regulatory compliance.
2020sContinued advancements in predictive analytics, fraud prevention, and algorithmic trading strategies.

Source: Authors’ compilation

Complete Chapter List

Search this Book:
Reset