Predicting credit defaults is crucial from a business perspective as it enables banks to effectively manage risk, reduce costs, allocate resources efficiently, gain a competitive advantage, and comply with regulatory requirements.
By using machine learning techniques, banks can assess the creditworthiness of borrowers and identify those with a high probability of default, allowing them to make informed lending decisions, protect their loan portfolios, and maintain financial stability.
This credit default model uses Random Forest Regressor to predict defaults with 76% accuracy and 73% recall