Home Credit Default Risk

Improve the accuracy of your credit risk assessments, make informed lending decisions, and reduce the risk of default on home loans

This turnkey enterprise AI solution helps financial institutions to mitigate the risk of default on home credit loans. By identifying patterns that indicate a high risk of default, this solution provides financial institutions with a comprehensive view of the credit risk and allows making data-driven decisions. Contact us today to learn more about how this solution can help your institution to improve portfolio performance and reduce credit risk.

Key Metrics

Increase in Loan Approval Rate by


Decrease in Risk of Default by


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.


  • Collect and analyze historical data on borrower creditworthiness, including credit history, income, employment, and debt information.
  • Utilize advanced machine learning techniques such as decision trees, random forests, and neural networks to build a predictive model for credit default risk.
  • Continuously update the model with real-time data on borrower behavior and financial performance, and incorporate external data sources such as economic indicators and market conditions.
  • Leverage the trained ML model to predict the likelihood of default for each borrower and make informed lending decisions.
  • Monitor and evaluate the performance of the model, and use the data to identify and address any biases or inaccuracies in the predictions.
  • Use the insights to develop customized loan and repayment plans for borrowers, and to identify opportunities for financial education and credit counseling.

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* Values are approximates arrived at based on earlier experience and/or existing literature. Contact us to find out how you can measure the ROI on this solution for your business