ENetRM-SQS: Elastic Net Regression Model in Secure Query Systems for Predictive Financial Risk Analysis

The world depends strongly on dependable predictive financial risk analysis because it upholds the stability and operational efficiency of worldwide financial systems.Traditional risk assessment systems experience difficulties when operating with extensive complex datasets and maintaining secure data safeguards because these problems produce breaches and wrong conclusions.The proposed ENetRM-SQS framework addresses data protection challenges by implementing Elastic Net Regression Model with current data security capabilities in its framework structure.ENetRM-SQS operates better than single-focus statistical models because it delivers optimized performance from both security aspects and predictive accuracy standards.ENetRM-SQS establishes superior performance than traditional Logistic Regression (LR), K-Nearest Neighbors (KNN) and Decision Trees (DT) models by reaching a 0.985 accuracy level.The model's performance is validated through analysis of FinancialRiskData and CreditRisk datasets by processing various demographic, financial and behavioral attributes.These financial databases prove that the model operates efficiently within changing financial settings throughout multiple periods of time.ENetRM-SQS should be strategically implemented in financial risk management to revolutionize risk assessment capabilities through an advanced security framework.This research establishes the main innovation which unites robust security measures with advanced regression techniques to define advanced standards for financial risk assessment accuracy along with data integrity.