Artificial Intelligence in Financial Systems

Abstract

Artificial intelligence is being used in financial systems to make tasks more efficient, detect fraud, and offer personalized services. It is changing industries like banking, trading, and insurance by making processes faster, cutting costs, and helping manage risks better. Some key uses include automated trading, financial advisors that operate on their own, and making sure companies follow financial rules. Objectives of Study: Automating repetitive and data-heavy tasks like entering data and processing invoices to cut down on mistakes, speed up processes, and save money. Research Methodology: Research on AI in financial systems uses different methods, like looking at past studies, examining real-life examples, and using data to see how AI affects areas such as trading, risk management, and compliance. Data Analysis: AI watches patterns in transactions in real time to spot suspicious behavior. This helps reduce false alarms and improves how risks are managed. High-frequency trading systems use machine learning to notice small price changes and make trades with very little delay. Finding: AI helps cut costs by automating tasks. In banking and capital markets, 32 to 39% of work could be done by AI. Mid-sized financial firms have already seen a 28% drop in their operating costs. Recommendations: Create complete frameworks that cover all parts of AI use, including regular checks, making sure data is accurate, and identifying bias. This is important for dealing with issues like false information and cyber threats. Conclusion: AI is not just a new tool but a big force that is shaping the future of finance. It’s important to use its benefits while being careful about the ethical and system-wide risks it can bring.