This study reviews and integrates literature on the use of predictive analytics to support financial decisions and the management of risks. The research employs the approach of a literature review analysis, based on articles, industry studies, and case-study materials focusing on the application of predictive analytics in financial services. This study then examines the primary use of predictive analytics such as credit scoring, fraud detection, liquidity forecasting, and market analysis, to establish the extent to which they minimize risk, optimize operations, and promote financial sustainability. Further, the analysis assesses how, through the use of predictive analytics, financial institutions can effectively mitigate and manage diverse varieties of risks such as credit, market, operational and liquidity risks. It also identified the performance problems of using predictive analytics that financial institutions encounter such as problems in data quality, problems of algorithm bias, compatibility problems with integrated large traditional systems and problems of human resources. Based on the results of the study, recommendations for the financial institutions are made regarding the effective usage of the predictive analysis, and it is still designed to impact the decision-making part and move the risk management part to the next level. Consequently, the study concludes that predictive analytics has the potential to revolutionize financial practices, but the exercise needs investment in data quality, algorithmic fairness, system integration and training of human capital. The study’s limitations include the use of secondary data and literature review, and it does not reflect actual implementation and application from the real world. The results may differ when different sectors are compared. Future studies should use survey questionnaires, examine the ethical and organizational issues, and evaluate the late effects of the predictive analytical system on the aspect of finance.
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