Abstract

Enhancing Recovery Rate Prediction through Explainable AI

In the finance sector, accurate prediction of recovery rates is crucial for risk management and financial planning. Traditionally, this task has posed significant challenges due to the complexity and variability of financial data. Recent advancements in artificial intelligence (AI) and data science offer promising solutions by enhancing both the accuracy and explainability of predictive models. This study presents a series of quantitative tools designed to improve the prediction of recovery rates. Our approach emphasizes the importance of explainability and interpretability in AI models, ensuring that financial analysts and stakeholders can understand the reasoning behind predictions. By integrating explainable AI techniques, we aim to provide more transparent and reliable financial forecasts. This presentation will detail the methodologies employed, the enhancements achieved in prediction accuracy, and the practical implications of our results in the financial industry.