# Exploring the Impact of AI-Based Cyber Security Financial Sector Management
## 4 Experiments and Evaluation  
Experimental results presented in this section are based on the CS-FSM method, and its classification performance is compared with that of other algorithms that provide similar results. This experiment was conducted on an Intel Core i7 computer with 2.84 GHz and 64 GB of RAM running Windows 8 64-bit. The Python 3 emulator was loaded, and the testing network's susceptibility information was examined using the security program. The network structure data were gathered with the ArcGIS toolkit. The researcher used Python to develop the code for the project. During the course of the trial, the investigation analyzed roughly 250 thousand sets of attack and defense strategies. Matlab 2018a was employed for data analysis and visualization. Various parameters have been used to evaluate the suggested CS-FSM model, such as data privacy ratio, scalability ratio, risk reduction ratio, data protection ratio, and attack avoidance ratio. It analyzes cyber security data with artificial intelligence, and the Enhanced Encryption Standard (EES) secures and decompresses data to keep financial sector information safe. The following dataset is taken from the link [39]. Data privacy refers to individuals' ability to control their data collection and use. Data security and privacy go hand in hand because protecting customer details and secret materials is the first step in preserving digital information and personal data confidentiality. The results are compared to the latest techniques such as Support Vector Machine (SVM), Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA). Personal Identifiable Information is a severe danger to data privacy. In an innovative contemporary society, dealing with millions, if not billions, of collected data can be a hardship because of the information's quality and amount. Figure 6 shows that the financial sector's data privacy ratio is mathematically confirmed using Equation (2). The innovative CS-FSM method to financial sector cyber security enhances privacy, scalability, risk reduction, data protection, and attack avoidance. EES and KNN are two examples of AI algorithms that can provide a more comprehensive view of cyber security than traditional methods. The proposed method represents a significant improvement over the status standard of cyber security measures in the banking industry. The results in Figure 6 validate the novel nature and practicality of the proposed approach enhancing cyber security in the banking industry. Preparing the groundwork to enable and support future performance and being able to grow without being inhibited is the goal. Planning, funding, and having the right infrastructure (including people, procedures, tools, and equipment) are essential. This refers to the ability of an organization or company strategy to grow without being constrained by its structure or finances when confronted with higher productivity. In a software organization, scalable pricing is valuable for increasing income. It allows one to take a larger share of the money that customers are ready to pay without alienating smaller customers who cannot afford high pricing. The finance system's scalability ratio is theoretically verified using Equation (2) in Figure 7.  
Financial risk refers to a company's ability to handle indebtedness and strain, whereas economic exposure refers to the firm generating enough income to cover operating costs. Financial risk refers to a group's capacity to manage credit and fulfill financial obligations. Volatility in stock prices and bond rates, currency fluctuations, and other variables contribute to this risk type. The simulation results of the proposed CS-FSM system are shown in Table 2. The suggested CS-FSM systems simulation results and the growth rate in risk reduction are compared to existing models using Equation (5). Data retention is all about protecting critical IT assets with a set of regulations and procedures. Throughout this project, papers, charts, identification, and connectivity were all taken care of.