Exposing Impediments to Insurance Claims Processing

(a) Situation faced: Processing injury-compensation claims, such as compulsory third party (CTP) claims, is complex, as it involves negotiations among multiple parties (e.g., claimants, insurers, law firms, health providers). Queensland’s CTP program is regulated by the Motor Accident Insurance Commission (MAIC). The Nominal Defendant, an arm of MAIC, determines liability for claims when the vehicle “at fault” is unregistered or unidentified and manages such claims from injured persons. While the relevant legislation mandates milestones for claims processing, the Nominal Defendant sees significant behavioral and performance variations in CTP claims processing, affecting the costs and durations of claims. The reasons for these variations are poorly understood. (b) Action taken: The BPM initiative took a process-mining approach that focused on the process identification, discovery, and analysis phases of the BPM Lifecycle. We undertook automated process discovery and comparative performance analysis with the aim of identifying where claims processing across cohorts of interest to the Nominal Defendant differed. In parallel, we conducted a context analysis with the aim of identifying the context factors that affect claim duration and cost. The personal injury literature and interviews with representative Nominal Defendant staff informed our selection of data attributes. (c) Results achieved: Process models were developed to facilitate comparative visualization of processes. The Nominal Defendant was particularly interested in differences in the processes for specific cohorts of claims: (i) overall claims, (ii) claims involving unregistered vehicles versus unidentified vehicles, and (iii) direct claims versus legally represented claims. The model facilitated identification of aspects of claims processing where there were significant differences between cohorts. Data mining/feature selection techniques identified a set of process-related context factors affecting claim duration and cost. Models utilizing these context factors were able to distinguish between cases with short and long durations with 68% accuracy and between low-cost and high-cost claims with 83% accuracy. (d) Lessons learned: This multi-faceted process-mining study presented many challenges and opportunities for refining our process-mining methodology and toolset. Data-related challenges arose because of the replacement of claims-management software during the study. Legislative changes, changes to key personnel, and the semi-structured nature of CTP claims-processing introduced issues related to concept drift. Each of these issues affected process discovery, but close collaboration with the stakeholders proved valuable in addressing these issues. Novel visualization techniques were developed to support delivery of insights gained through comparative analysis that will guide process improvement. Consideration of context considerably broadens the scope of process mining and facilitates reasoning about process specifics.