Revolutionizing Attrition Risk Forecasting: Leveraging of AI-based Machine Learning Model and Distance Metrics for a Proactive Business Model

The impact of technology on human resources practices is becoming increasingly tangible nowadays. Many HR problems are being solved with the use of technology. It has got an unpredictable dimension to it. This paper addresses the gap in prediction and explains in detail how employee attrition could be forecast early on using a Machine Learning algorithm with a unique audit engine architecture model. Multinational conglomerates with hundreds of thousands of employees will find such an application useful. There is a set of features coupled with employee data that is decisive in determining whether a particular employee will stay or resign. These features are identified, quantified, and analyzed with different distance metrics to arrive at a model that would predict whether an employee will stay with the company in the framework of an Internal Auditing Engine, the relevant instances of which are powered by the KNN algorithm. Our analysis shows that the Manhattan distance metric gives a higher accuracy value.