An effective machine learning implementation means that artificial intelligence has tremendous potential to help and automate financial threat assessment for commercial firms and credit agencies. The scope of this study is to build a predictive framework to help the credit bureau by modelling/assessing the credit card delinquency risk. Machine learning enables risk assessment by predicting deception in large imbalanced data by classifying the transaction as normal or fraudster. In case of fraud transaction, an alert can be sent to the related financial organization that can suspend the release of payment for particular transaction. Of all the machine learning models such as RUSBoost, decision tree, logistic regression, multilayer perceptron, K-nearest neighbor, random forest, and support vector machine, the overall predictive performance of customized RUSBoost is the most impressive. The evaluation metrics used in the experimentation are sensitivity, specificity, precision, F scores, and area under receiver operating characteristic and precision recall curves. Datasets used for training and testing of the models have been taken from kaggle.com.

中文翻译：

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使用人工智能促进信用卡不平衡数据日志中的用户授权

有效的机器学习实现意味着人工智能具有巨大的潜力，可以帮助商业公司和信贷机构帮助自动进行金融威胁评估。本研究的范围是建立一个预测框架，以通过建模/评估信用卡违约风险来帮助信贷局。机器学习通过将交易分类为正常交易者或欺诈者来预测大型不平衡数据中的欺骗，从而实现风险评估。在发生欺诈交易的情况下，可以将警报发送到相关的金融组织，该组织可以中止针对特定交易的付款释放。在所有机器学习模型中，例如RUSBoost，决策树，逻辑回归，多层感知器，K-最近邻，随机森林和支持向量机，定制RUSBoost的总体预测性能最令人印象深刻。实验中使用的评估指标是灵敏度，特异性，精确度，F分数以及接收器工作特性和精确召回曲线下的面积。用于训练和测试模型的数据集来自kaggle.com。