Quantifying Insurance Agency Channel Dynamics Using Premium Sales Big Data and External Factors

In insurance business, product sales can be realized over a variety of channels such as independent agencies, or bank branches. In 2017, 55% of premium production was generated over insurance agencies in Turkey making independent agency evaluation prominent in the domain. Unfortunately lacking attention from the scientific community, agency evaluation problem is usually tackled in the industry by utilizing internal business dynamics data. To incorporate the external facts to the agency evaluation process, we propose a computational approach to model behavior traits reflecting insurance agency channel dynamics based on not only premium sales big data but also external facts. We demonstrate how we translate these behavior traits into useful features, namely, utilization, response, and governance, so that each agency can be positioned in a space whose dimensions are determined by these features allowing easy visual detection of segments. Utilization model suggests that each agency has a potential based on its location, determined by several local socioeconomic factors, and it explains the capability of converting potential to profit. To compute utilization scores, we adapt point-of-interest data as a parameter to the segmentation model, a novel approach not only in the insurance business but also in the literature. The response model suggests that a responsive agency must follow overall profit trends of the company. Finally, the governance model explains agency/company cooperation in terms of premium production. All together, we propose a segmentation-based agency evaluation model providing understanding of insurance agency behavior that could be explained and formulated along these three dimensions. Based on the findings from a year-long case study and a proceeding implementation period of our models on an actual analytic system of the insurance company donating the data, we reflect on the performance and usability of our behavioral models that were fit on premium sales big data comprising 127 million transactions. Our results suggest that (1) our approach is quite efficient in extracting features from production logs, (2) behavioral models are quite intuitive resulting in straightforward application steps, and (3) the adoption of behavior models in agency segmentation and evaluation processes is an improvement over commonplace approaches in which premium production is used as the sole metric.