Home credit default risk is a significant concern for financial institutions. To mitigate this risk, many institutions are turning to machine learning solutions to analyze historical data and identify patterns that may indicate a high risk of default.

These solutions take into account a variety of factors such as credit history, income, employment status, and demographic information to create a risk score for each borrower. By analyzing this data, the solution can identify potential red flags such as a history of late payments or a high level of outstanding debt. Additionally, by incorporating external data sources such as public records, social media, and news articles, the solution can also identify external factors that may impact a borrower's ability to repay their loan.

Implementing this solution allows financial institutions to identify high-risk borrowers early and take appropriate action to mitigate their risk. This can include adjusting loan terms, requiring additional collateral, or denying the loan application altogether. By proactively identifying and addressing potential issues, financial institutions can reduce their exposure to credit default risk and improve their overall portfolio performance. Additionally, this solution can also be used for the customer segmentation, which can help the institutions to adjust their products and services to the different customers' needs and increase the customer satisfaction.