Proceedings of International Conference on AI and Financial Innovation : AIFI 2025 (Smart Innovation, Systems and Technologies)

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Proceedings of International Conference on AI and Financial Innovation : AIFI 2025 (Smart Innovation, Systems and Technologies)

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  • 製本 Hardcover:ハードカバー版/ページ数 558 p.
  • 言語 ENG
  • 商品コード 9789819672684

Full Description

This book features high-quality research papers presented at the International Conference on AI and Financial Innovation (AIFI 2025), IIMT Bhubaneswar, Odisha, India, during February 7-8, 2025. The integration of artificial intelligence (AI) into financial services is ushering in a new era of technological transformation. The book discusses AI's ability to analyze large volumes of data, automate processes, and generate predictive insights which are revolutionizing how financial institutions operate. It also discusses how AI is becoming a critical tool for enhancing efficiency, improving decision-making, and fostering financial inclusion on a global scale. Furthermore, it presents contents on how integration of AI in payment systems, credit scoring, fraud detection, and regulatory compliance is reshaping the financial services industry, making it more secure, efficient, and accessible.

Contents

Risk Management for Distributed Arbitrage Systems Integrating Artificial Intelligence.- Analysis of Influencing Factors of Regional Economic Stability Development Combined with Data Mining Technology.- Using Big Data to Optimize Financial Resource Allocation Strategies in Economic Growth.- Analysis of the Effects of China's Economic Policies on the Fluctuations of the Economic Cycle.- Analysis of the Influence of Uncertainty on the Operation and Management of Chinese Commercial.- Research on the Selection of Credit Risk Assessment Indicators for Small Loan Companies Based on Analytic Hierarchy Process.- Dynamic Analysis of the Impact of Digital Currency on the Velocity of Money Circulate.- A Study on the Impact of Big Data-Driven Price Discrimination on Customer Citizenship Behavior Evidence from Ctrip Users.- A Machine Learning-based Model for Credit Risk Prediction in Banking Customers.- New Quality Productivity Enables Digital Financial Development Empirical Evidence.

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