Machine Learning in Financial Risk Management: Techniques for Predicting Early Payment and Default Risks
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Abstract
Artificial intelligence and, commonly, its subfield of machine learning (ML) has dramatically impacted financial risk management by improving the elicitation and flexibility of risk forecasts, especially concerning early payment and default risk. That is why it has become possible to speak about the existing traditional risk assessment models that no longer apply in a modern financial context, as they are oriented on historical data and are to be implemented with the help of relatively rigid frameworks. On the other hand, ML provides real-time prediction services, which leverage big datasets and learning algorithms like the logistic regression models, the random forest, and neural nets to develop proper risk profiling. The significant uses of the JHL method are for early payment prediction, default risk identification and credit scoring, which is flexible. There are benefits accrued to its use, such as increased predictive accuracy and real-time risk assessment, where it adopts a cheaper model to arrive at the results. However, its disadvantages include data privacy, security, and interpretability drawbacks. The future of ML in financial risk management trends will include the eventual use of technologies such as blockchain and AI to enhance decentralized, efficient, and secure risk management systems. As ML progresses, it is predicted that this technology will increase the efficiency, effectiveness, and individuality of risk management processes in the financial industry.
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