A Hybrid Neural–Tree Learning Framework for Robust Risk Identification in Financial Institutions

Abstract

To provide services effectively, financial organizations are turning towards digital infrastructures, hybrid network architecture, and multi-channel banking systems. Although they help enhance the performance level of the operation, these systems simultaneously pose major risks because of high dimensionality of transactional data, distributed setups, and extreme class imbalance of fraud and loan-default data. The proposed study introduces a hybrid neural–tree learning framework for efficient identification of financial risks efficiently in various areas within the banking sector such as credit risk, marketing risk, and payment frauds. The system combines profound neural level derivation with tree-based decision learning to obtain multifaceted nonlinear patterns as well as to keep the decision limits structured and understandable. An all-encompassing preprocessing pipeline that includes data clean up, feature engineering, and imbalance handling methods are used to increase model robustness. The presented model is tested on three benchmark data, including Lending Club loan data, Portuguese banking marketing data, and an online payments fraud dataset. The experimental findings prove that the hybrid model demonstrates superior performance compared to the conventional machine learning algorithms that include, Random Forest, XGBoost, KNN, SVM, and standalone neural models. Specifically, an F1-score of 0.93 and recall of 0.96 are achieved in detecting minority fraud cases, which is more significant compared to the traditional models, which tend to consider total accuracy as the main measure of success, implying the solution has a high capacity to find minority risk cases at a balance between precision and recall. In addition, the interpretability is promoted by using the feature importance analysis which helps to understand the major risk-driving factors better. The hybrid framework is a proposal of a scalable, robust and practically deployable system to financial risk analytics to help banking institutions in enhancement of decision making, lessening financial losses, and improving operational reliability in complex hybrid network environments.