Machine Learning with Certainty: A Requirement for Intelligent Process Automation

Many dull but non-trivial tasks in the financial industry are performed by human staff, but could be automated by intelligent systems that observe staff and learn the correct processes. Such systems present unique design challenges, because errors are so costly they are simply unacceptable (referring a case to humans for manual processing would be better than processing it incorrectly). In this paper the system is cast as a classifier which learns to trigger an action under the right conditions. Several design constraints for the classifier are proposed, including interpretability and the absolute need to avoid any false positives. A gap is identified in current literature with respect to these constraints. We present "SureTree" - a simple meta-learning algorithm for training decision trees with minimal risk of false positives - and compare its performance to several conventional alternatives.