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Comparison Procedure Of Predicting The Time To Default In Behavioural Scoring

机译：行为评分中预测违约时间的比较程序

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The paper deals with the problem of predicting the time to default in credit behavioural scoring. This area opens a possibility of including a dynamic component in behavioural scoring modelling which enables making decisions related to limit, collection and recovery strategies, retention and attrition, as well as providing an insight into the profitability, pricing or term structure of the loan. In this paper, we compare survival analysis and neural networks in terms of modelling and results. The neural network architecture is designed such that its output is comparable to the survival analysis output. Six neural network models were created, one for each period of default. A radial basis neural network algorithm was used to test all six models. The survival model used a Cox modelling procedure. Further, different performance measures of all models were discussed since even in highly accurate scoring models, misclassification patterns appear. A systematic comparison '3 + 2 + 2' procedure is suggested to find the most effective model for a bank. Additionally, the survival analysis model is compared to neural network models according to the relative importance of different variables in predicting the time to default. Although different models can have very similar performance measures they may consist of different variables. The dataset used for the research was collected from a Croatian bank and credit customers were observed during a 12-month period. The paper emphasizes the importance of conducting a detailed comparison procedure while selecting the best model that satisfies the users' interest.
机译：本文涉及预测信用行为评分违约时间的问题。该领域为在行为评分模型中包含动态成分提供了可能性，该行为成分使得能够做出与限额，回收和回收策略，保留和损耗有关的决策，并提供对贷款的盈利能力，定价或期限结构的洞察力。在本文中，我们在建模和结果方面比较了生存分析和神经网络。设计神经网络体系结构，使其输出与生存分析输出相当。创建了六个神经网络模型，每个默认时间段一个。径向基神经网络算法用于测试所有六个模型。生存模型使用Cox建模程序。此外，讨论了所有模型的不同性能指标，因为即使在高度准确的评分模型中，也会出现分类错误的情况。建议进行系统的“ 3 + 2 + 2”比较，以找到最有效的银行模型。此外，根据预测默认时间时不同变量的相对重要性，将生存分析模型与神经网络模型进行比较。尽管不同的模型可以具有非常相似的性能指标，但它们可能包含不同的变量。用于研究的数据集是从一家克罗地亚银行收集的，在12个月的时间内观察到了信贷客户。本文强调了在选择满足用户兴趣的最佳模型时进行详细比较程序的重要性。

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