Intelligent Financial Analytics by using AI and Machine Learning in Fintech.

Abstract

Recent advancements in the field of Financial Technology along with Machine Learning have transfigured the financial ecosystem by enhancing the business intelligence solutions that are driven by large scale financial data. Today, financial organizations are increasingly using Machine learning models for extensive financial data analysis in order to produce meaningful insights. This further enhances the operational effectiveness and minimizes the risk and also help in taking correct decisions. Present work explores the manner by which machine learning and advancements in Fintech sway business intelligence. This emphasizes fraud-detection, risk valuation, algorithmic trade-off and economic decision making. This research examines the way by which complex financial data is analysed by machine learning algorithms for predicting financial trends, detecting frauds, assessing risks and supporting automated trading. The work also examines the technological assemblies used in Fintech environments and accentuates the real-time case studies which display the effective application of ML driven Fintech solutions. Along with the advantages there are some challenges in financial related applications- namely data privacy issues, monitoring restrictions and interpretability of models. This work finds that Fintech solutions empowered by machine learning have given a new facet to business intelligence tasks and will escalate the future of digital finance. It also helps the organizations to take wise financial decisions.