Transfer Learning for Enhancing Predictive Models in Financial Risk Management

Abstract

Financial risk management operates as a mandatory decision-making tool for banking sectors and insurance companies and investment organizations. Predictive systems based on traditional models create weak output because these systems need to deal with limited datasets while also having restricted management constraints. The research examines Domain Adaptation within Transfer Learning because it functions as an operational method for developing predictive financial risk assessment models. Deep learning models despite their pre-trainings enable adaptation to particular risk prediction solutions which results in improved model flexibility and strengthened stability. The features selection combined with hyperparameter tuning and model architecture decision process becomes optimized through the use of AutoML. Transfer learning technology serves financial risk management effectively by optimizing the accuracy rates and reducing the processes durations.