Integrating Machine Learning into Financial Systems to Improve Risk Management and Economic Stability
DOI:
https://doi.org/10.60087/jklst.vol4.n4.013Keywords:
Machine Learning, Financial Risk Management, Fraud Detection, Economic Stability, Financial SystemsAbstract
Background: Machine learning systems now operate in financial environments which have transformed all risk management operations and fraud detection systems and economic stability maintenance methods. Financial institutions now use intelligent algorithms to strengthen their decision processes which produces better predictions and lowers their operational risks. Methods: The study used a cross-sectional quantitative design to study machine learning affects the development of financial systems. The study collected primary data through a structured questionnaire which 215 professionals from banking and insurance and regulatory sectors answered using a five-point Likert scale. Machine learning implementation together with risk management system performance and fraud detection capabilities and their impact on economic stability. Descriptive statistical methods to analyze their data by calculating frequency distributions and percentage values and average scores. Results: Machine learning delivers improved financial risk prediction results through its ability to reach 84.7% accuracy while it simultaneously helps organizations cut down their operational financial losses by 79.5%. The fraud detection system achieved its highest performance level when it successfully detected suspicious transactions within 86.0% of cases. The research findings reveal that 81.4% of participants experienced a decrease in cyber financial fraud incidents while 83.7% of them reported better financial security which led to economic stability. Conclusion: Machine learning serves as an essential tool which researchers discovered to enhance financial system security through its ability to improve risk assessment and detect fraudulent activities and maintain economic stability.
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