Innovative Applications of Machine Learning in Fintech

The use of Machine Learning (ML) in the FinTech business has transformed financial services by increasing efficiency, lowering operating costs, and improving client experience. This study investigates the novel uses of ML in fraud detection, credit scoring, algorithmic trading, customer service automation, and risk assessment. Despite its disruptive potential, problems like as data privacy, regulatory compliance, algorithmic bias, and high implementation costs continue to impede mainstream use. A mixed-methods approach was used, which combined primary data from surveys and expert interviews with secondary data from academic literature and industry reports. The study polled 200 FinTech experts to assess the level of ML usage, efficiency gains, and consumer opinions. According to the findings, 76% of FinTech organisations use ML, with an average 44% increase in efficiency and a 35% decrease in costs. However, trust in ML-driven financial choices is still moderate (3.04/5), indicating continued worries about transparency and fairness. The study finds that, while ML is an important driver of innovation in FinTech, ethical AI frameworks, increased regulatory compliance, and improved ML model explainability are required for long-term growth. These conclusions have substantial implications for financial institutions, regulators, and technology developers that want to optimise ML use in financial services.