October 30 ~ 31, 2025, Virtual Conference
Naisha Vatsya, Independent, United States of America
The integration of artificial intelligence (AI) into financial technology (fintech) marks a transformative shift in financial services, reshaping operations, customer engagement, risk management, and innovation. This paper provides a comprehensive overview of AI adoption in fintech, examining market growth, core applications such as fraud detection, credit scoring, and algorithmic trading, as well as challenges including regulatory compliance and cybersecurity risks. The analysis highlights how AI drives operational efficiency, revenue growth, and financial inclusion while anticipating future trends in generative AI, quantum computing, and autonomous financial services. The paper concludes with implementation strategies and economic implications, emphasizing the necessity of strategic governance and risk management to achieve sustainable AI integration. This study contributes to understanding the evolving landscape of AI-driven fintech and outlines pathways for research and practice.
Financial Technology, Artificial Intelligence, Generative Artificial Intelligence, Automation
Munawar Yusuf Sayed and Dr. Yashwant Waykar, Babasaheb Ambedkar Marathwada University, INDIA
The use of social media has increased rapidly, and this has led to the creation of a large amount of content. There has been a heightened demand in society to study insight from people, making it essential to understand the role of sentiment analysis, where the insights of people’s nature and their responses to any event analysed. In sentiment analysis, machine learning (ML) and deep learning (DL) algorithms used to understand people’s nature and responses to events. Sentiment analysis widely used across various fields to study opinions or feedback and to improve services. It used to analyse sentiment documents and classify their polarity as positive, negative, or neutral. Recent research work and studies that are ongoing are focused on Multiclass Sentiment Analysis (MCSA) aimed at analysing textual documents and deriving insights from them. A study using LSTM neural networks has presented a comprehensive analysis of academic performance measurement data that that transforms measurement metrics into three sentiment categories. The educational systems involved in learning, decision-making, and evaluation process improved through the performance of multiclass sentiment analysis. A study using Long Short-Term Memory (LSTM) neural networks for measuring student’s performance data achieved excellent performance with an accuracy of 99.89% through careful feature engineering, class balancing, and LSTM architecture optimization. The proposed research provides insights into the applications of these technologies across various fields of machine learning and deep learning including education, customer voice, workforce analysis, politics, digital marketing, and social media monitoring, and established a framework for this.
Opinion Mining, AI, ML, DL, ANN, NLP, LSTM, MCSA, Sentiment Analysis, Multiclass Sentiment.