Insider threats, is one of the most challenging threats in cyberspace, usually responsible for causing significant loss to organizations.The topic of insider threats has long been studied and many detection techniques were proposed to deal with insider threats. This paper focuses on using different anomaly detection algorithms- Locality Outlier Factor Algorithm and Isolation forest Algorithm and does a comparative analysis between their performance. A hybrid model incorporating advantages of both LOF Algorithm and IF Algorithm is proposed in this paper which gives better performance than the individual models for detecting insider threats. The hybrid model was able to achieve whooping 99.99\% accuracy while detecting insider threats.