Paper Title
DATA SCIENCE TECHNIQUES FOR PREDICTIVE ANALYTICS IN FINANCIAL SERVICES

Abstract
Business intelligence has become one of the crucial technologies in financial services and with the help of predictive analytics; the organisations have been able to improve a lot in various fields. In this paper, the author focuses on the role of prediction in financing, as well as explaining the main data science approaches and technologies that support innovations in this area. In more detail, the specifics of using the predictive model in the estimation of risks and in investment are discussed through case studies, which allow to reveal the advantages and positive results of the application. In addition, the paper a examines the issues that arise in the implementation of financial data analytics including data issues, compliance and growth factors. Through a consideration of these issues, we hope to orchestrate the best understanding of today and tomorrow’s perspectives on prediction analysis for financial services. Keywords - Predictive Analytics, Financial Services, Data Science, Risk Assessment, Investment, Predictive Modeling, Financial Data Analytics, Machine Learning, Regression Analysis, Decision Trees, Neural Networks, Time Series Analysis, Clustering, Credit Scoring, Fraud Detection, Algorithmic Trading