ARTIFICIAL INTELLIGENCE IN FINANCE: METHODS, APPLICATIONS, AND GOVERNANCE

This paper examines the role of artificial intelligence in modern finance and how it is reshaping areas like riskassessment, credit decisioning, fraud and crime detection, algorithmic trading, wealth advisory, insurance, andregulatory oversight. We trace the evolution of methods from classic machine learning to deep learning and recentlarge language models, and describe the workflow of data pipelines along with governance practices. We alsopropose a practical reference architecture for building and operating AI systems at production scale within financialinstitutions. To support understanding, the paper includes synthetic experiments and visual examples. We addressongoing challenges such as bias, model drift, and limited interpretability, and close with a forward-looking researchagenda centered around responsible AI, privacy-aware techniques, and rigorous evaluation standards.