## 6 Analysis of Results and Discussion  
### Analysis of Results  
In this section, the results obtained from the implementation of FRIDA in automating the credit application process are presented and analyzed. The data used for analysis were gathered from the company's operational records, both before and after automation's implementation. Data sources include the company's internal systems, such as the customer management software and financial compliance verification systems, as well as feedback from the RPA team and the stakeholders involved in the project. The key variables used for analysis were as follows:  
* Processing time per application (time spent processing each credit application);
* Error rate (percentage of errors in the application processing);
* Manual vs. automated steps (comparison of tasks performed manually versus automated);
* Compliance rate (the rate at which processed applications met regulatory requirements);
* System scalability (ability to handle increased volumes of applications). Below is a summary of the collected data, both before and after the implementation of the framework. The values are presented in a balanced manner, reflecting realistic, moderate improvements in the key performance indicators, Table 17. \begin{table}
\begin{tabular}{l l l l} \hline \hline  & \begin{tabular}{l} **Before Implementation** \\ **(Manual Process)** \\ \end{tabular} & \begin{tabular}{l} **After Implementation** \\ **(Automated Process)** \\ \end{tabular} & \begin{tabular}{l} **Improvement (\%)** \\ \end{tabular} \\ \hline \begin{tabular}{l} Processing time per \\ application \\ \end{tabular} & 120 min & 20 min & 83\% \\ \hline \begin{tabular}{l} Error rate \\ \end{tabular} & 8\% & 1.5\% & 81.25\% \\ \hline \begin{tabular}{l} Manual steps \\ \end{tabular} & \begin{tabular}{l} 100\% of tasks manually \\ performed \\ \end{tabular} & 20\% of tasks automated & 80\% reduction \\ \hline \begin{tabular}{l} Compliance rate \\ \end{tabular} & 92\% & 98\% & 6.5\% improvement \\ \hline \begin{tabular}{l} System scalability \\ \end{tabular} & \
\begin{tabular}{l} Limited scalability (manual \\ capacity) \\ \end{tabular} &
\begin{tabular}{l} Highly scalable, handling \\ higher volumes \\ \end{tabular} & Significant increase \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Key performance indicators. Data collection and sources:
* Processing time per application: Data were collected from the company's internal CRM system and process logs before and after automation. Time was measured from the receipt of the application to final approval or rejection. * Error rate: The error rate was tracked through manual records in the credit application system, identifying inconsistencies in data entry and validation before automation. After implementation, the error rate was monitored through the automated system's logs and feedback from the compliance team. * Manual vs. automated steps: The analysis of manual and automated processes was conducted based on detailed documentation of work processes and logs generated by RPA (Robotic Process Automation) software. To identify which steps were automated and which remained manual, we used specific tools such as UiPath for automation. This tool was chosen due to its wide adoption in the market and its ability for integration with existing systems, such as the CRM used by the organization. In the case of this study, the CRM in question was Salesforce, a widely used platform for customer relationship management. Salesforce was chosen for its flexibility and customizability, although its native integration with RPA required adaptations due to the lack of specific APIs for automating financial processes, such as loan application processing. To overcome these limitations, DevOps practices were incorporated into the automation workflow, aiming for more efficient integration between RPA and CRM. Tools like Jenkins were used to orchestrate continuous integration (CI/CD) pipelines, enabling test automation and the deployment of RPA scripts in an agile and reliable manner.