Scoring Model

Scoring models, used across diverse fields from finance to healthcare, aim to assign numerical scores reflecting the likelihood of an event or outcome. Current research emphasizes improving model accuracy and fairness, addressing issues like sampling bias and the need for interpretability, with techniques ranging from Bayesian frameworks and regularized boosting algorithms (like XGBoost) to deep learning architectures (like LSTMs and transformers). These advancements are crucial for enhancing decision-making in various applications, improving predictive performance, and mitigating potential biases in high-stakes scenarios.