This systematic review explores the incorporation of Environmental, Social, and Governance (ESG) factors within financial risk prediction models, with a particular focus on Machine Learning (ML), Natural Language Processing (NLP), and Large Language Models (LLM). Adhering to the Preferred...

The field of MLOps (Machine Learning Operations), which focuses on effectively managing and operationalizing ML workflows, has grown because of the advancements in machine learning (ML). The goal of this study is to examine and contrast the difficulties encountered in the implementation of MLOps...

One primary objective of the early warning system literature is to construct more accurate financial vulnerability prediction models and investigate the mechanisms and key factors that differentiate healthy from vulnerable financial states. Despite the importance of identifying and predicting...

Exhaustive hyperparameter search techniques such as grid search and random search are widely used in predictive analytics but become computationally expensive and often suboptimal in high-dimensional or nonlinear settings. This challenge is particularly clear in tuning Cycle Reservoir with Jumps...