Session Analysis using Plan Recognition

TL;DR

This paper introduces a novel plan recognition approach applied to real-world customer interaction data, reducing analyst workload and improving detection of session intent and anomalies in online financial services.

Contribution

It presents the first integration of plan recognition algorithms with real-world UI data for customer session analysis, demonstrating practical benefits.

Findings

Reduces analyst workload in session evaluation

Detects malicious versus benign sessions effectively

Predicts next actions in customer interactions

Abstract

This paper presents preliminary results of our work with a major financial company, where we try to use methods of plan recognition in order to investigate the interactions of a costumer with the company's online interface. In this paper, we present the first steps of integrating a plan recognition algorithm in a real-world application for detecting and analyzing the interactions of a costumer. It uses a novel approach for plan recognition from bare-bone UI data, which reasons about the plan library at the lowest recognition level in order to define the relevancy of actions in our domain, and then uses it to perform plan recognition. We present preliminary results of inference on three different use-cases modeled by domain experts from the company, and show that this approach manages to decrease the overload of information required from an analyst to evaluate a costumer's session - whether this is a malicious or benign session, whether the intended tasks were completed, and if not - what actions are expected next.