Design and simulation of financial service risk system based on improved genetic algorithm

This study presents the design and simulation of a financial services risk control system based on an improved genetic algorithm (GA). The system aims to effectively manage and control the risks associated with financial services such as investment portfolios, loans and insurance. The improved genetic algorithm employs several improvements to provide more accurate and efficient risk control. Firstly, a new fitting function is introduced which takes into account multiple risk factors and their associated weights. This allows the system to prioritise and allocate resources based on the importance of each risk factor. In addition, the improved selection operator enhances the genetic algorithm to increase population diversity and prevent premature convergence. This ensures that the algorithm explores a wider range of solutions and avoids falling into local optima. In addition, a local search operator is incorporated into the genetic algorithm to refine the solutions obtained from the genetic operator. This local search operator helps to improve the quality of the solution by making small adjustments to the individuals in the population. In order to evaluate the performance of the proposed system, we have prepared simulation code using Python language to perform extensive simulations on real-world financial data. The results show that the improved GA-based system outperforms traditional risk control methods in terms of accuracy and efficiency. In conclusion, this study presents a new approach to financial services risk control using improved GA. The proposed system provides a more comprehensive and efficient method for managing financial services risk, which contributes to the stability and sustainability of the industry.