Satpathy / Mohanty / Pani AI-Driven Competitive Intelligence and Next-Generation Security

1. Auflage 2025
ISBN: 979-8-89881-213-3
Verlag: Bentham Science Publishers
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 265 Seiten

Reihe: Applied Artificial Intelligence in Data Science, Cloud Computing and IoT Frameworks

Advancing Intelligence: Exploring Smart Systems and Future Innovations with Machine Learning is a comprehensive volume collection examining how Artificial Intelligence (AI) and Machine Learning (ML) are reshaping industries, societies, and technologies worldwide. Written by global experts, the series combines theoretical foundations with practical applications, showcasing how intelligent systems address pressing challenges across healthcare, agriculture, urban development, business intelligence, and cybersecurity. Volume 2: Transformative Healthcare Solutions Powered by AI & ML investigates intelligent tools for precision medicine, medical informatics, wearable devices, and patient-centric care, demonstrating how AI enhances diagnosis and treatment outcomes. Volume 3: AI and ML Solutions Driving Modern Farming and Urban Innovation explores sustainable farming, smart agriculture, environmental monitoring, and intelligent city infrastructures for improved quality of life. Volume 4: AI-Driven Competitive Intelligence and Next-Generation Security focuses on financial analytics, business intelligence, cybersecurity, and digital forensics, emphasizing strategies for resilience in a data-driven era. Key Features Integrates AI and ML into healthcare, agriculture, urban, and business systems. Applies intelligent tools to improve diagnosis, sustainability, efficiency, and security. Analyzes case studies and global perspectives for real-world relevance. Explores future-ready innovations shaping intelligent, resilient ecosystems. Addresses both societal opportunities and technical challenges of AI adoption.

Autoren/Hrsg.

Satpathy, Suneeta

Mohanty, Sachi Nandan

Pani, Subhendu Kumar

Leseproben

Revolutionizing Financial Futures: The AI Impact on Forecasting and Risk Management

Preethi Nanjundan1, *, Mary Analiya Babu2, Lijo Thomas2

1 Department of Data Science, CHRIST University, Lavasa Campus, Pune, India

2 Department of Management, CHRIST University, Lavasa Campus, Pune, India

Abstract

For forecasting and risk management, the financial sector has historically depended on statistical models and historical data. Artificial intelligence (AI), on the other hand, is poised to completely transform traditional methods. This book examines how artificial intelligence (AI) can revolutionize risk management and financial forecasting. The book explores the ways in which artificial intelligence (AI) methods, such as machine learning and deep learning, may analyse enormous volumes of data from various sources to spot intricate patterns and relationships that conventional approaches might overlook. As a result, risk assessments and projections become more precise, empowering financial institutions to make more educated choices. The book also discusses the difficulties in integrating AI in finance, such as obstacles related to regulations, data quality, and model explainability. It offers suggestions on how to get past these obstacles and successfully incorporate AI into financial processes. Financial organizations can obtain a major competitive edge by utilizing AI. Anyone interested in the future of finance and the potentially disruptive power of artificial intelligence should read this book.

Keywords: Artificial intelligence (AI), Big data, Deep learning, Financial forecasting, Machine learning, Pattern recognition, Predictive analytics, Risk management, Regulatory challenges.

* Corresponding author Preethi Nanjundan: Department of Data Science, CHRIST University, Lavasa Campus, Pune, India; E-mail: [e-mail]

INTRODUCTION TO AI IN FINANCE

Artificial Intelligence (AI) has become a disruptive force in the financial industry, reshaping conventional procedures and transforming the way decisions are made. Artificial intelligence has become essential for risk management and financial forecasting because of its unparalleled speed at which it can analyse massive volumes of data and identify intricate patterns. Businesses need to get ready for the next wave of digital disruption that artificial intelligence is about to unleash. It

is now more important than ever for other businesses to speed up their digital transitions since we are witnessing tangible benefits for a select group of early adopting companies [1]. The evolution of financial forecasting has been marked by a shift from sophisticated artificial intelligence approaches to conventional statistical methodologies. Because machine learning models can learn from previous data and produce highly accurate predictions, they have become popular. Examples of these models are regression, decision trees, and random forests [2]. By identifying intricate links in data that produce deeper insights, deep learning techniques—such as neural networks—have further increased prediction potential. Furthermore, novel approaches to mine unstructured data sources—such as news articles, social media, and incoming calls—for insightful information have been made possible by natural language processing, or NLP. NLP-based algorithms can analyze text data to forecast market movements, gauge the impact of news events, and gauge market sentiment [3]. Artificial intelligence has not only greatly improved forecasting but also risk management procedures in the financial industry. Credit and market risks can be evaluated and reduced by institutions through the use of predictive analytics.

Additionally, investors can design diversified portfolios that are suited to their risk tolerance and financial objectives with the aid of AI-based portfolio optimization tools. The extensive application of AI in banking is not without difficulties, though. Data security, legal compliance, and ethical concerns all demand that AI technologies be used carefully and responsibly. Notwithstanding these obstacles, artificial intelligence has a bright future in banking, and more advancements could significantly alter the sector's structure.

The Evolution of Financial Forecasting

The practice of financial forecasting has changed dramatically throughout the years, moving from an intuitive to a technologically advanced era. In the beginning, traders made educated forecasts based on prior trends and market whispers by using their instincts and experience. Despite its shortcomings, this method cleared the path for more methodical approaches. The 20th century saw the advent of the data-driven age, in which analysts started to spot trends by looking back at previous price, volume, and economic indicator trends. Time series models were developed to better forecast by capturing the subtleties of financial data, and moving averages were used to reduce volatility and identify short-term patterns. The Monte Carlo simulations gained prominence in the 1960s and are still very important today. Risk could be evaluated and simulated by analysts.

The Role of AI in Modern Finance

Artificial Intelligence (AI) is being rapidly incorporated into the operations of the financial industry, resulting in a huge transformation. AI is having a wide range of effects on the sector, from bettering investor decisions to automating backend chores. Enhanced Efficiency and Risk Management: Artificial Intelligence (AI) frees up human specialists for more strategic work by automating repetitive processes like fraud detection and loan application processing. Large volumes of financial data may be analyzed by AI algorithms to find trends and forecast hazards related to investments, loans, and even money laundering. As a result, organizations are able to decide more quickly and intelligently. An organizational culture that welcomes human and machine collaboration is necessary to reap the long-term benefits of artificial intelligence. Here, trust is a crucial facilitator. The relationship between a machine's internal operations and the output it generates can become fairly hazy because of the interaction between training and inference in artificial intelligence [4].

AI gives financial experiences to clients and institutions a personalized touch. Banks can use artificial intelligence (AI) to comprehend client behaviour and provide individualized financial products or wealth management guidance. In a similar vein, AI-driven robo-advisors evaluate each client's risk tolerance and investing objectives before generating automated investment plans. To forecast future market movements, investment and artificial intelligence algorithms can examine social media data, news sentiment, and market patterns. This gives hedge funds and investment firms the ability to make data-driven judgments about their investments and maybe increase profits. AI-powered algorithmic trading has the ability to complete deals extremely quickly and take advantage of short-lived market opportunities. Financial services are becoming more widely available thanks to AI-powered financial solutions. For those without the funds to hire a traditional financial counsellor, robo-advisors offer investing advice.

Furthermore, AI can evaluate creditworthiness from non-traditional data sources, giving institutions access to populations that were previously unbanked. Even though artificial intelligence (AI) has a lot of promise, ethical issues and potential biases in algorithms must be addressed. In general, AI is bringing about a new era of intelligent finance that will be characterized by increased accessibility to financial services, efficiency, and personalization.

AI TECHNIQUES FOR FINANCIAL FORECASTING

For a very long time, financial forecasting has been an essential instrument for managing market volatility. These days, Artificial Intelligence (AI) is changing this area by providing strong methods that improve precision and reveal hidden patterns. Machine Learning (ML) is one of the main AI techniques. Large volumes of historical data, such as market patterns, economic indicators, and corporate financials, can be analyzed using machine learning algorithms. ML models are capable of forecasting future changes in stock prices, market movements, and even creditworthiness by seeing intricate links in this data.

Deep Learning is another effective technology. This branch of machine learning uses artificial neural networks to simulate the architecture of the human brain. Even more complicated data sets, such as social media trends, satellite images, and news sentiment, can be processed by deep learning models, which could result in more accurate projections.

Additionally, AI is able to comprehend and evaluate textual data such as financial reports, social media debates, and news articles thanks to Natural Language Processing, or NLP. NLP has the ability to extract sentiment and spot...