Credit risk Monitoring and evaluation

In this project, I collected and analyzed data related to borrowers' credit histories, financial health, and economic conditions. Using advanced statistical methods and machine learning algorithms, I developed models to predict the likelihood of default and other credit-related risks.

The project involved creating a real-time monitoring system that continuously evaluates the creditworthiness of borrowers. Key metrics, such as credit scores, debt-to-income ratios, and payment histories, were tracked and visualized through interactive dashboards. This system enables stakeholders to make informed decisions and implement timely interventions to minimize risk.

The outcome of this project is a robust framework that enhances the accuracy and reliability of credit risk assessments, ensuring better financial management and reduced losses.