Development of credit scoring models is important for financial institutions to identify defaulters and nondefaulters when making credit granting decisions. In recent years, artificial intelligence (AI) techniques have shown successful performance in credit scoring. Support Vector Machines and metaheuristic approaches have constantly received attention from researchers in establishing new credit models. In this paper, two AI techniques are reviewed with detailed discussions on credit scoring models built from both methods since 1997 to 2018. The main discussions are based on two main aspects which are model type with issues addressed and assessment procedures. Then, together with the compilation of past experiments results on common datasets, hybrid modelling is the state-of-the-art approach for both methods. Some possible research gaps for future research are identified.

中文翻译：

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信用评分：支持向量机和元启发式方法的回顾

信用评分模型的开发对于金融机构在做出授信决定时识别违约者和非违约者非常重要。近年来，人工智能（AI）技术已在信用评分方面取得了成功的表现。支持向量机和元启发式方法在建立新的信用模型方面一直受到研究人员的关注。本文回顾了1997年至2018年以来使用这两种方法构建的信用评分模型的两种AI技术，并进行了详细讨论。主要讨论基于两个主要方面，即模型类型，要解决的问题和评估程序。然后，结合过去在公共数据集上的实验结果，混合建模是这两种方法的最新方法。