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A machine learning approach for predicting bank credit worthiness

机译：预测银行信贷值得的机器学习方法

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Machine learning is an emerging technique for building analytic models for machines to "learn" from data and be able to do predictive analysis. The ability of machines to "learn" and do predictive analysis is very important in this era of big data and it has a wide range of application areas. For instance, banks and financial institutions are sometimes faced with the challenge of what risk factors to consider when advancing credit/loans to customers. For several features/attributes of the customers are normally taken into consideration, but most of these features have little predictive effect on the credit worthiness or otherwise of the customer. Furthermore, a robust and effective automated bank credit risk score that can aid in the prediction of customer credit worthiness very accurately is still a major challenge facing many banks. In this paper, we examine a real bank credit data and conduct several machine learning algorithms on the data for comparative analysis and to choose which algorithms are the best fit for learning bank credit data. The algorithms gave over 80% accuracy in prediction. Furthermore, the most important features that determine whether a customer will default or otherwise in paying his/her credit the next month are extracted from a total of 23 features. We then applied these most important features on some selected machine learning algorithms and compare their predictive accuracy with the other algorithms that used all the 23 features. The results show no significant di erence, signifying that these features can accurately determine the credit worthiness of the customers. Finally, we formulate a predictive model using the most important features to predict the credit worthiness of a given customer.
机译：机器学习是一种用于建立机器的分析模型的新兴技术，从数据“学习”并能够做预测分析。机器将“学习”和预测性分析的能力在这一时代非常重要，它具有广泛的应用领域。例如，银行和金融机构有时会面临在向客户推进信用/贷款时考虑的危险因素的挑战。对于客户的若干特征/属性，通常考虑到客户，但大多数这些功能对信贷值得的预测效果几乎没有预测到客户。此外，强大而有效的自动化银行信用风险评分，可以非常准确地预测客户信贷资源，仍然是许多银行面临的主要挑战。在本文中，我们检查了真正的银行信用数据，并在对比较分析的数据上进行多种机器学习算法，并选择哪些算法是学习银行信用数据的最佳算法。算法在预测中提供了超过80％的精度。此外，最重要的功能可以默认客户默认或以其他方式在下个月支付他/她的信用时从总共23个功能中提取。然后，我们在一些选定的机器学习算法上应用了这些最重要的功能，并将它们的预测精度与使用所有23个功能的其他算法进行比较。结果显示没有显着的DI erence，表示这些功能可以准确地确定客户的信誉。最后，我们使用最重要的功能制定预测模型，以预测给定客户的信誉值得。

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