Adaptive Credit Risk Modeling with Machine Learning and Financial Mathematics - Sarjono, Haryadi; Soeratin, Boyke Setiawan; Rahmawati (Future Science, 2025)
This study examines the integration of financial mathematics and artificial intelligence (AI), focusing on credit risk analysis within the financial industry. Traditionally, financial mathematics has played a central role in modeling and assessing credit risk. However, recent technological advancements have enabled a more synergistic relationship between financial mathematics and AI, particularly through the use of machine learning. This integration enhances risk assessment by leveraging classification algorithms to uncover complex patterns in historical data patterns that are often difficult to detect using conventional methods. Through a case study in the financial sector, this research explores the application of classification algorithms to better understand customer behavior, evaluate credit history, and predict the likelihood of future loan repayment. In the realm of machine learning, classification techniques are essential for processing large volumes of data and generating accurate risk predictions. By combining the analytical rigor of financial mathematics with the predictive power of machine learning, this study aims to develop a robust and adaptive model for credit risk analysis one capable of responding to dynamic market and economic conditions, and improving the overall decision-making process in credit assessment.