“The Role of Artificial Intelligence in Financial Decision-Making”

Name of the author: Soumya Rajkumar Chougale

Designation and Institutional Affiliation: Dayananda Sagar College of Engineering Bengaluru

Email ID: soumyachougale004@gmail.com

ORCID ID: 0009-0008-1614-1074

Corresponding Author: Mrs. Roopa U

Designation and Institutional Affiliation: Dayananda Sagar College of Engineering Bengaluru

Email ID: roopaamith10@gmail.com

ORCID ID: 0000-0002-1813-7203

Abstract

Artificial Intelligence (AI) has emerged as one of the most influential technological developments transforming financial decision-making. The increasing availability of structured and unstructured data, rapid developments in computational capabilities, and the growing complexity of global financial markets have accelerated the adoption of AI-based systems across financial institutions. Technologies such as machine learning, deep learning, natural language processing, predictive analytics, and reinforcement learning are increasingly being applied to improve the speed, accuracy, consistency, and scalability of financial decisions.

AI supports a wide range of financial activities, including credit risk assessment, fraud detection, investment analysis, algorithmic trading, portfolio optimization, financial forecasting, and automated advisory services. Machine learning models can identify complex patterns in large datasets that may not be easily captured through conventional statistical approaches. Deep learning techniques have further expanded the ability of financial institutions to process high-dimensional and unstructured information. In investment management, AI-based systems are used to analyse market trends, support trading strategies, optimize asset allocation, and provide personalized financial recommendations. Similarly, in banking and risk management, AI assists in credit scoring, transaction monitoring, fraud detection, and real-time risk assessment.

Despite these advantages, the adoption of AI in financial decision-making also creates significant concerns related to algorithmic bias, lack of transparency, privacy, accountability, cybersecurity, and regulatory compliance. Financial decisions often have substantial consequences for individuals, businesses, and institutions; therefore, the reliability and explainability of AI-generated outcomes are particularly important. Explainable Artificial Intelligence (XAI) has emerged as an important approach for improving the interpretability of complex AI models and supporting transparency, accountability, and trust.

This chapter adopts a descriptive and analytical approach based on secondary sources, including academic literature, financial reports, industry publications, and previously reported empirical findings. The chapter examines the role of AI across major areas of financial decision-making and compares traditional approaches with AI-based models. The findings indicate that AI can significantly improve predictive capability, operational efficiency, and responsiveness in financial services when supported by appropriate governance and human oversight. However, responsible implementation requires robust data governance, transparent model development, ethical safeguards, regulatory alignment, and continuous monitoring. The chapter concludes that AI is likely to play an increasingly central role in financial decision-making.

Keywords: Artificial Intelligence; financial decision-making; machine learning; deep learning; predictive analytics; Explainable AI; investment decision-making; credit risk assessment; algorithmic trading; portfolio optimization; fraud detection; financial risk management.