From Transaction to Transformation: AI and Machine Learning in FinTech

Abstract

Artificial Intelligence technology and Machine Learning operate in FinTech industries by automating processes while giving complete risk assessments, plus private customized solutions. This research studies how AI and ML technology support modern FinTech business functions, specifically fraud prevention, business credit rating, automated investment advice, and automatic market trades. The research conducts the empirical evaluation of Large Language Model integration within Zero Trust security structures by using adversarial testing together with case studies and explainability assessment methods. Research indicates that LLMs improve threat detection performance by 21% and traditional systems yet they fail in 38% of cases when prompt injection happens. The study highlights the need for adversarial training combined with fairness auditing and regulatory oversight in order to establish AI deployment safety in cybersecurity. The research pairs contemporary studies with specific examples to uncover AI and ML's main advantages of better results, less manual work, and better services for customers. This study researches both data protection risks and official rules while looking at the weaknesses of programmed systems. Through research the paper determines how applications based on AI machine learning propel financial services toward automated systems that adjust their behavior for optimal results. The analysis generates new ideas to develop FinTech innovation models and provides useful directions for business creators plus oversight agencies plus financial establishments. This study brings a new method to view AI/ML integration while showing the need to align ethical and legal rules in future FinTech systems.