# Exploring the Impact of AI-Based Cyber Security Financial Sector Management
## 4 Experiments and Evaluation
One strategy for identifying and reducing the risk associated with data storage and transportation is data security. Banks have to protect their customers' financial information against unauthorized access. If cybersecurity is poor, clients can lose necessary time, personal information, and savings. Customers may become uneasy or fearful, and companies may lose the public's faith in keeping their money and personal information safe. The proposed technique has strengthened data protection based on the data values determined from Equation (7). Figure 8 shows that the CS-FSM method provides better data protection than other methods. Protecting sensitive information is a crucial part of using artificial intelligence for security purposes in the banking industry. Financial institutions  
Figure 6: Comparison of data privacy. Figure 7: Interpretation of scalability. must ensure that they have comprehensive data protection mechanisms in place to secure sensitive data from cyberattacks, comply with data protection legislation, and guarantee the transparency and explication of their AI-based cyber security systems. An attack avoidance strategy eliminates challenges, activities, and exposures that could harm a company and its resources. Cybersecurity seeks to minimize the negative consequences and financial costs of potentially harmful situations, whereas avoidance aims to avoid them altogether. The CS-FSM approach, as shown in Figure 9, is more precise than the alternatives. These values are provided by Equations (8) and (9), demonstrating a significant increase in attack avoidance utilizing the researcher's proposed technique. Due to data breaches, financial institutions can be tough to convince, which is a significant problem for banks. A shaky protection system can lead to data exposures, quickly leading to customers going elsewhere to spend their cash. The CS-FSM approach enhances the data privacy ratio (18.3%), scalability ratio (17.2%), risk reduction ratio (13.2%), data protection ratio (16.2%), and attack avoidance ratio (11.2%). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Customers**} & \multicolumn{6}{c}{**Various Techniques**} \\ \cline{2-9}  & **SVM** & **CPS-IoT** & **PCA** & **DM** & **LDA** & **CATRAM** & **CS-FSM** \\ \hline
10 & 78.5 & 82.1 & 84.8 & 80.9 & 90.5 & 86.3 & 93.2 \\ \hline
20 & 76.2 & 80.2 & 85.6 & 82.3 & 89.3 & 90.1 & 95.4 \\ \hline
30 & 80.4 & 81.7 & 89.2 & 78.6 & 88.4 & 91.6 & 96.1 \\ \hline
40 & 83.4 & 82.9 & 88.7 & 81.4 & 87.6 & 88.4 & 97.4 \\ \hline
50 & 72.5 & 79.6 & 82.3 & 80.5 & 85.4 & 89.3 & 94.6 \\ \hline
60 & 74.6 & 72.4 & 80.8 & 79.8 & 82.5 & 87.8 & 92.4 \\ \hline
70 & 78.2 & 76.3 & 82.5 & 82.7 & 86.6 & 85.6 & 93.5 \\ \hline
80 & 82.6 & 77.5 & 81.2 & 86.9 & 89.4 & 83.4 & 96.7 \\ \hline
90 & 85.4 & 85.6 & 86.7 & 83.6 & 79.6 & 91.7 & 95.4 \\ \hline
100 & 79.5 & 87.2 & 89.6 & 85.4 & 88.6 & 89.8 & 98.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of risk reduction ratio. Figure 8: Assessment of data protection. The purpose of this section is to discuss an experiment that focused on a suggested CS-FSM method for cyber security utilizing AI and EES. The research's code was written in Python, and the results were examined in Matlab 2018a. Data privacy, scalability, risk reduction, data protection, and attack avoidance are a few of the metrics used to gauge the success of the suggested approach. Linear discriminant analysis, principal component analysis, and the Support Vector Machine are some of the current methods used to compare the results. To ensure the protection of the financial sector's data, the experiment assessed cyber security data. The results of the study demonstrated that the CS-FSM method excels over competing approaches in terms of data confidentiality, scalability, risk mitigation, data security, and attack avoidance. The risk reduction ratio for various models is compared in Table 2, and it can be seen that the suggested CS-FSM method results in a higher risk reduction rate.