Innovative Insurance and Profit Modeling: Integrating ARIMA and LSTM for Risk Assessment and Profit Maximization Strategies - Yuan, Meng; Zou, Zihao; Chang, Shuhao (Transactions on Economics, Business and Management Research, 2024)
This research presents an innovative approach to insurance and profit modeling by integrating ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short-Term Memory) techniques for risk assessment and profit maximization in the insurance industry. The study initially establishes sub-models focusing on income modes and loss modes, utilizing historical data and risk factors to calculate potential profits and losses. Through the development of a profit model based on nonlinear programming, the study aims to optimize costs associated with natural disaster claims while maximizing profitability. Leveraging machine learning techniques, particularly LSTM, the research refines time series models to predict and mitigate risks more accurately. The findings emphasize the importance of geographical variations and timely underwriting decisions for insurance companies to ensure business stability and sustainable growth amidst evolving risk landscapes.