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.

中文翻译：

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使用保费销售大数据和外部因素量化保险代理渠道动态

在保险业务中，产品销售可以通过独立机构、银行分行等多种渠道实现。2017 年，土耳其保险机构产生了 55% 的保费，使得独立机构评估在该领域显得尤为突出。不幸的是，由于缺乏科学界的关注，机构评估问题通常在行业中通过利用内部业务动态数据来解决。为了将外部事实纳入代理评估过程，我们提出了一种计算方法来模拟反映保险代理渠道动态的行为特征，不仅基于保费销售大数据，还基于外部事实。我们展示了如何将这些行为特征转化为有用的特征，即利用、响应和治理，利用模型表明，每个机构都有一个基于其位置的潜力，由几个当地的社会经济因素决定，它解释了将潜力转化为利润的能力。为了计算利用率分数，我们将兴趣点数据作为参数应用于分割模型，这是一种不仅在保险业务中而且在文献中的新方法。该响应模型表明，一个负责任的机构必须按照公司的整体盈利趋势。最后，治理模型在优质生产方面解释了代理/公司合作。总之，我们提出了一个基于细分的代理评估模型，提供对保险代理行为的理解，可以沿着这三个维度进行解释和制定。根据为期一年的案例研究结果以及我们的模型在捐赠数据的保险公司的实际分析系统上的实施期，我们反思了适用于大型保费销售的行为模型的性能和可用性。数据包括 1.27 亿笔交易。我们的结果表明（1）我们的方法在从生产日志中提取特征方面非常有效，（2）行为模型非常直观，导致应用程序步骤简单，