 AI-Driven Automation: Revolutionizing Financial Operations and EfficiencyBrijlal Mallik, Shivangi Kashyap, Robert Ślepaczuk, Manish Kumar and Dev Kumar Mandal Chapter 1 in AI in Finance:Shaping the Future of Intelligent Automation and Financial Services, 2026, pp 1-26 from World Scientific Publishing Co. Pte. Ltd. Abstract: The integration of artificial intelligence (AI) in financial management is revolutionizing financial operations by enhancing efficiency, accuracy, and decision-making capabilities. AI-driven automation plays a critical role in risk management, fraud detection, investment strategies, and customer service, significantly transforming the financial landscape. Through advanced technologies such as machine learning, natural language processing, and robotic process automation, AI enables financial institutions to process vast datasets rapidly, recognize patterns, and generate valuable insights for strategic decision-making. This chapter explores the historical evolution of automation in financial services, from ATMs and online banking to high-frequency trading and AI-powered analytics. The literature review highlights AI’s profound impact on operational efficiency and competitiveness while also addressing concerns regarding transparency and the “black box” model of AI-driven systems. The potential applications of AI in finance continue to expand, with automation already being widely implemented in areas such as payments, expense management, and financial analytics. By leveraging AI technologies, financial institutions can optimize resource utilization, enhance client services, and maintain a competitive edge in an evolving digital landscape. Keywords: Artificial Intelligence; AI in Finance; Financial Technology; FinTech; Machine Learning; Deep Learning; Neural Networks; Automation; Robotics; Intelligent Automation; Algorithmic Trading; Robo-Advisors; Predictive Analytics; Data Science; Big Data; Risk Management; Credit Scoring; Fraud Detection; AI Ethics; Responsible AI; AI Governance; Regulatory Compliance; Financial Regulations; Cybersecurity; Blockchain; Cryptocurrencies; Bitcoin; Ethereum; InsurTech; Digital Banking; AI in Banking; AI in Investments; AI in Insurance; AI in Wealth Management; AI in Payments; Natural Language Processing; AI Chatbots; Virtual Assistants; Customer Experience; Personalization; Sentiment Analysis; Credit Risk Modeling; Financial Forecasting; AI-powered Decision Making; Quantitative Finance; Trading Algorithms; High-Frequency Trading; AI in Hedge Funds; AI-driven Market Analysis; Automated Financial Services; Smart Contracts; Digital Assets; AI-driven Portfolio Management; Financial Planning; AI in Asset Management; AI in Lending; AI in Mortgage Industry; Financial Inclusion; Alternative Data; Explainable AI; Model Interpretability; AI and Human Collaboration; AI-driven Credit Analysis; Robo-Trading; Supervised Learning; Unsupervised Learning; Reinforcement Learning; AI-driven Customer Insights; Data-driven Decision Making; Financial Market Predictions; Behavioral Finance; Smart Finance; AI-based Anomaly Detection; Computational Finance; Financial Data Analytics; Financial Fraud Prevention; Future of Work in Finance; AI Strategy in Financial Firms; Financial Risk Analytics; Digital Transformation in Finance (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wsi:wschap:9781800618428_0001 Ordering information: This item can be ordered from Access Statistics for this chapter More chapters in World Scientific Book Chapters from World Scientific Publishing Co. Pte. Ltd. |
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