Predictive models performance in financial services for identifying at-risk customers

It is therefore essential in financial service where the concept of pre-emptive modeling is used to select customers that are most likely to engage in negative behaviors like loan defaulting, fraud or churn. This paper will seek to analyze different modeling techniques like Logistic regression, Decision trees, Random forests, Gradient boosting, and Neural network among others. The paper pays considerable attention to the data preprocessing step; problems such as imbalance ratio and missing values as well as important parameters such as precision, recall, and AUC-ROC to measure the efficiency of the models. Some of these problems that have been highlighted in the review include; data privacy, compliance to laws and regulations among others and model interpretability which plays an important role in the financial industry. This means that new achievements like XAI, Real-time analytics, Federated learning have contributed to the improvement of model interpretability, model size and data security. Such include technological applications to increase accuracy of Risk prediction while cutting costs, and help the financial institutions retain the trust of the customer given the ever changing regulation. By integrating these novelties and ethical issues, some more financial institutions should enhance the centrality of the presented predictions to improve the identification of the risks and customers’ appeal.