AI-Driven Predictive Analytics for Enhancing Automotive Safety in Financial Risk Assessments in Cloud Data
G. V. Radhakrishnan, R. Varalakshmi, Namrata Kapoor Kohli, Sarvagya Jha, S. Sruthi, Suraj Singh · 2025
Prediction technologies based on AI drive crucial automotive safety and financial risk assessment which results in minimizing losses as well as increasing road safety. A Hybrid AI-Econometrics Model which merges machine learning algorithms with econometric systems serves as the main proposal to estimate and quantify economic consequences linked to automotive accidents. TensorFlow performs deep learning operations within the framework alongside Statsmodels to analyze telematics data, insurance claims data and macroeconomic data for determining risks of accidents and their connected financial costs. The model uses deep learning algorithms to find patterns in accidents before calculating financial risk through GARCH (Generalized Autoregressive Conditional Heteroskedasticity) and Vector Autoregression The model provides both accurate predictions and understandable results which make it acceptable for various users including insurers and governmental agencies and automotive industries. The research shows better assessment capabilities as the model enhances protective driving environments