AI-Based Real-Time Problem-Solving Using Smart Technologies

Nahita Pathania, Balraj Singh and Isha Batra

Chapter 11 in AI in Finance:Shaping the Future of Intelligent Automation and Financial Services, 2026, pp 243-255 from World Scientific Publishing Co. Pte. Ltd.

Abstract: The increasing rate of development of smart technologies has highlighted effective scheduling algorithms as a solution to the real-time problem-solving aspects of the cloud. Smart technology where real-time problem-solvers develop solutions is a high-performance ecosystem driven by the Internet of Things (IoT), artificial intelligence (AI), and the cloud. At the core of these systems are resource allocation and scheduling algorithms that optimize performance, minimize latency, and ensure efficient operations. Common scheduling algorithms are crucial for dynamic environments with changing constraints because they allow for high-fidelity decisions in applications ranging from autonomous vehicles and healthcare monitoring to industrial automation. Those systems demand that tasks be completed within milliseconds, which calls for algorithms that can handle real-time task execution and task prioritization according to set criteria. Moreover, IoT applications are complemented by cloud computing, which offers scalable infrastructure to process and analyze large volumes of real-time data. While these benefits exist, there are still challenges regarding conflicting scheduling objectives, such as latency and energy constraints. Smart technologies, IoT applications, and cloud environments may implement scheduling algorithms to optimize resource allocation and decision-making, focusing on providing efficient services to end users. These algorithms have had a transformative impact across industries, with case studies available in healthcare, industrial automation, smart cities, transportation, etc. AI-enabled routing, edge computing, and ethical governance frameworks are just a few of the features that will enhance the efficiency and scalability of real-time smart systems.

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)
JEL-codes: C45 D81 G17 G21 O33 (search for similar items in EconPapers)
Date: 2026
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