Robotic Process Automation (RPA) Scripting Model Using Machine Learning (ML) for Enterprise Data Validation and Integration

This research presents an automated data processing model based on RPA Scripting, designed to enhance efficiency in extracting, validating, and integrating information from various web platforms. The automated workflow begins with the use of a tool that simulates human interaction on web platforms to obtain data automatically and reliably. The data is then organized and cleaned using processing techniques that prepare it for analysis. As a key component of the model, Machine Learning algorithms have been incorporated to detect errors, identify unusual patterns, and classify records, thereby improving data quality before storage. Finally, the processed data is loaded into a database and visualized through a dynamic dashboard that supports decision-making via reports and indicators. In conclusion, integrating Machine Learning algorithms within an RPA Scripting model not only optimizes the execution of automated tasks but also equips the model with intelligence to anticipate errors and adapt to changes in the data. This enables the development of a more robust, reliable, and adaptive automated process, aligned with current requirements for real-time analysis and decision-making.