Interpretable Machine Learning Models for Financial Risk Assessment

Interpretable machine learning models in financial risk assessment are valuable tools for analysts and decision-makers. These models provide a better understanding of the factors that lead to specific outcomes through transparency in the decision-making process. In the complex financial world, where uncertainty and unpredictable risks exist, the interpretability of models helps analysts not only achieve more accurate predictions but also develop better strategies for managing existing risks. Furthermore, these models contribute to building trust among stakeholders, as they explain data-driven decisions with greater clarity. In this context, techniques such as feature importance and sensitivity analysis allow analysts to examine the impact of each variable on the final outcomes. Given the importance of risk assessment in financial markets, utilizing interpretable machine learning models can be an effective step towards improving decision-making processes and reducing financial risks.

کلیدواژه ها:

Machine learning models ، Interpretable ، Financial risk assessment ، Analysts