DYNAMIC PRICING IN FINANCIAL TECHNOLOGY: EVALUATING MACHINE LEARNING SOLUTIONS FOR MARKET ADAPTABILITY

Zenodo2024-10-28 更新2026-04-07 收录

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https://zenodo.org/doi/10.5281/zenodo.14015364

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The rapid advancement of technology has transformed the financial services sector, leading to the rise of fintech companies that leverage cutting-edge tools such as artificial intelligence (AI) and machine learning (ML) to offer innovative solutions. One area where fintech is particularly impactful is dynamic pricing, which involves adjusting prices in real-time based on market conditions, user behavior, and external factors. The ability to optimize pricing in response to fluctuating conditions is critical for maximizing profitability, improving customer satisfaction, and maintaining competitiveness. In this context, machine learning algorithms provide a powerful framework for making data-driven pricing decisions by learning from historical data and predicting future trends.

提供机构：

Department of Management Science and Quantitative Methods, Gannon University, USA; Dahlkemper School of Business, Gannon University, USA; Department of Information Assurance and Cybersecurity, Gannon University, USA; Master of Science in Information Technology, Washington University of Science and Technology, USA; Department of Information Technology Project Management, Business Analytics, St. Francis College, USA; Department of Computer & Info Science, Gannon University, Erie, Pennsylvania, USA; Department of Management Science, Adelphi University, Garden city, New York, USA

创建时间：

2024-10-28