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The objective was to leverage machine learning to improve credit risk assessment for our client and help their lenders make informed decisions, by alerting them to potential risks. Our data science team developed and deployed a loan default model to assist loan processors in assessing the probability of loan defaults. By analyzing the data entered by loan applicants, our model provides a reliable and efficient scoring mechanism that enables our customer to accurately evaluate creditworthiness. The deployment of our loan default model via API has proven to be a valuable solution for financial institutions in assessing creditworthiness and managing credit risk effectively. By leveraging machine learning techniques and integrating the model into their existing loan application systems, lenders can now score the probability of loan defaults accurately. This case study highlights the importance of data science in improving credit risk assessment and showcases Mutually Human’s commitment to delivering innovative solutions that drive positive outcomes for our clients.
To build the loan default model, we gathered loan application data, credit history, financial details, and other relevant information. We then took the following actions:
including cleaning, normalization, and feature engineering, to prepare the data for analysis.
Using a machine learning algorithm, we trained the model on a labeled dataset, evaluating its performance through several accuracy metrics including accuracy, precision, recall, and F1-score.
For our loan default model, we selected a gradient boosting algorithm due to its ability to handle complex relationships and provide accurate predictions. Feature selection and importance assessment were conducted to identify the most influential factors for loan default predictions. Through rigorous training and validation, the model achieved a high level of performance, demonstrating its ability to effectively distinguish between borrowers likely to default and those who are not.
The loan default model was deployed via API, providing our customer a seamless interface to score the probability of loan defaults in real-time. The API allows for efficient data input and preprocessing, ensuring that loan applicant data is handled securely and accurately. By leveraging the model’s predictions, our client is able to make more informed lending decisions, reducing credit risk and improving loan portfolio management. The implementation of the API resulted in increased efficiency, accuracy, and consistency in credit risk assessment processes.
For our loan default model, we selected a gradient boosting algorithm due to its ability to handle complex relationships and provide accurate predictions. Feature selection and importance assessment were conducted to identify the most influential factors for loan default predictions. Through rigorous training and validation, the model achieved a high level of performance, demonstrating its ability to effectively distinguish between borrowers likely to default and those who are not.
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