## 6 Analysis of Results and Discussion
### Discussion  
The implementation of FRIDA in automating the credit application process has yielded substantial improvements across several key performance indicators, demonstrating significant gains in efficiency, accuracy, and scalability. However, these outcomes must be contextualized within the organizational environment to fully appreciate their broader implications. The automation process reduced the average processing time per application by 83%, from 120 min in the manual process to just 20 min post-automation. This marked improvement underscores the efficiency gains facilitated by Robotic Process Automation (RPA). By automating routine tasks, the system can process applications more rapidly, significantly enhancing operational capacity and customer satisfaction. Nevertheless, more complex cases may still require additional processing time, suggesting opportunities for further refinement of the automation framework. The error rate was reduced by 81.25%, falling from 8% to 1.5%, reflecting the accuracy improvements achieved through automation. By minimizing human errors in data entry, validation, and compliance checks, we reduced the system's reduced, thereby decreasing  
Figure 2: A comparison of percentage improvements in key variables after the implementation of FRIDA. network, enhancing data integrity, and improving overall process quality. Continuous monitoring, however, remains essential to mitigate the risk of new errors emerging as the system evolves. An 80% reduction in manual steps highlights the framework's capacity to automate the majority of tasks, enabling employees to focus on more strategic activities. However, the remaining 20% of manual tasks, particularly those involving complex decision-making or exceptions, indicate areas where further automation could enhance efficiency. Exploring machine learning and AI-driven processes may offer solutions to address these residual tasks. The compliance rate increased by 6.5%, rising from 92% to 98%, demonstrating the system's ability to consistently apply regulatory requirements and reduce non-compliance risks. Automation ensures that predefined rules are uniformly enforced, improving adherence to regulatory standards. Future work could focus on enhancing the system's ability to adapt to evolving regulations and introducing more sophisticated compliance checks. Scalability improved significantly following the framework's implementation, enabling the system to handle higher application volumes without the constraints of manual processes. This scalability is particularly critical for organizations experiencing growth or fluctuating demand. However, challenges may arise during periods of high application volume or during integration with other systems. Future efforts should aim to enhance the system's adaptability to such variations. The integration of Requirements Engineering (RE) and DevOps practices in the development of RPA systems has demonstrated clear benefits in terms of operational efficiency and user experience. Combining these practices provides a structured approach to identifying requirements early in the development lifecycle (through RE) while enabling continuous updates and rapid deployment cycles (through DevOps). This dual approach ensures that RPA systems align closely with user needs, resulting in functional and user-friendly solutions. However, deploying DevOps in process automation environments presents specific challenges. These include the need for synchronization between development and operations teams, particularly in organizations with traditional cultures, and the need to manage dependencies between legacy systems and new automation solutions. The scalability of CI/CD pipelines in high-volume application scenarios may also require infrastructure adjustments and resource reallocation. These limitations underscore the importance of a flexible and adaptive approach, allowing for continuous refinement of the framework to address real-world complexities.