Creating rules to block bad emails works. However, it’s time consuming and requires continuous improvement. Rules must be created to block bad emails. Others, to let good emails through. And once they’re created, they have to be updated, corrected, and generally cared for. Rules link to other rules, exponentially increasing complexity.
Our AI journey officially began in 2008. Our team decided to develop automated measures to help classify and block emails. The first engine we used was called OSBF and used Bayesian inference to classify emails. This was replaced by a second engine named OSCAR, which was instead designed to classify messages using Logistic Regression. These engines could be “fed” emails, told whether they should be considered good or bad, and learn how to accurately classify future emails.
- Problem: The volume of bad emails to block only keeps increasing
- Context: The number of emails sent keeps increasing. The different types of emails keep increasing.
- Solution: Automation (first OSBF, then OSCAR)