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Assessment of life insurance applications: an approach integrating neuro-symbolic rule-based with case-based reasoning

机译：评估人寿保险的应用：一种基于神经符号规则与案例推理相结合的方法

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摘要

Assessment of applications for life insurance is an important task in the insurance sector that concerns estimation of potential risks underlying an application, if accepted. This task is accomplished by specialized personnel of insurance companies. Because of recent financial crises, this task is more demanding, and intelligent computer-based methods could be employed to assist. In this paper, we present an intelligent approach to assessment of life insurance applications, which is based on an integration of neurule-based with case-based reasoning. Neurules are a type of neuro-symbolic rules that combine a symbolic (production rules) and a connectionist (adaline unit) representation. A characteristic of neurules is that in contrast to other hybrid neuro-symbolic approaches, they retain the naturalness and modularity of symbolic rules. Neurules are produced from available symbolic rules that represent general knowledge, which however do not completely cover the domain. We use health condition, age, gender, annual income, profession, insurance type and primary life insurance benefit as assessment parameters used in rule conditions. The integration of neurules and cases employs different types of indices for the cases according to different roles they play in neurule-based reasoning. This results in its accuracy improvement. Experimental results demonstrate the effectiveness of the approach.
机译：人寿保险申请的评估是保险部门的一项重要任务，涉及评估潜在的潜在风险（如果被接受）。该任务由保险公司的专业人员完成。由于最近的金融危机，这项任务的要求更高，可以使用基于智能计算机的方法来提供帮助。在本文中，我们提出了一种智能的方法来评估人寿保险的应用程序，该方法基于基于神经元的推理与基于案例的推理的集成。神经元是一种神经符号规则，它结合了符号（生产规则）和连接主义者（ADA单位）表示形式。神经元的一个特点是，与其他混合神经符号方法相比，它们保留了符号规则的自然性和模块化。神经元是从代表常识的可用符号规则中产生的，但是这些规则并未完全涵盖领域。我们使用健康状况，年龄，性别，年收入，职业，保险类型和基本人寿保险福利作为规则条件中使用的评估参数。根据案例在案例中基于神经元推理的不同作用，案例和案例的整合采用不同类型的索引。这导致其精度提高。实验结果证明了该方法的有效性。

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