An Intelligent Chatbot Implementation for Employee Exit Auto-Clearance using Deep Learning

The exit clearance process is a mandatory requirement for an employee exiting their employer organization. This process ensures that all organization property has been returned, facilitates the issuance of the final paycheck, and guarantees that the employee leaves formally. This process typically entails filling and submitting an exit clearance form. This form is usually rubber-stamped or appended with a signature for each area of responsibility in the clearance process, this ascertains completion. Findings from the review of literature revealed that the exit clearance process utilizes physical forms, leading to tasks such as printing, filing, and extensive record keeping. Conversely, some organizations have employed the use of automated means, however, these are still predominantly human reliant, resulting in delays, inconsistencies and redundancies. The aim of this study was to develop an intelligent chatbot implementation for employee exit auto clearance using Deep Learning. A chatbot is a software tool that is driven by Artificial Intelligence (AI). It serves to facilitate communication and interaction between humans and computers in a more natural way. In addition to the various benefits of a chatbot, it also enhances the user experience and offers greater availability, making it suitable for mundane tasks that can become monotonous when handled by humans. This research applied the qualitative design to investigate the various ways and approaches that constitute the exit clearance process and their challenges, in formal organizations, within Nairobi, Kenya. The proposed deep learning chatbot model was developed using 2 hidden layers and trained on 2,000 epochs. The training data dictionary was categorized as tags, patterns and responses. The model achieved a 99.91 % accuracy in correctly associating input pattern data points to their corresponding response output data points. In cases of unclear input patterns, the model responded appropriately. The model was able to successfully make API calls to the web service, where digital signatures are appended, and finalize the exit clearance process with a complete and signed clearance form.