The fraud detection system in banking organisation relies on data-driven approach to identify the fraudulent transactions. In real time, detection of each and every fraudulent transaction becomes a challenging task as financial institutions need aggressive jobs running on the log data to perform a data mining task. This paper introduces a novel model for credit card fraud detection which combines ensemble learning techniques such as boosting and bagging. Our model incorporates the key characteristics of both the techniques by building a hybrid model of bagging and boosting ensemble classifiers. Experimentation on Brazilian bank data and UCSD-FICO data with our model shows sturdiness over the state-of-the-art ones in detecting the unseen fraudulent transactions because the problem of data imbalance was handled by a hybrid strategy. The proposed method outperformed by a margin of 43.35–68.53, 0.695–11.67, 43.34–68.52, 42.57–67.75, 3.5–13.06, 24.58–34.35%, respectively, in terms of true positive rate, false positive rate, true negative rate, false negative rate, detection rate, accuracy and area under the curve from the state-of-the-art-techniques, with a Matthews correlation co-efficient of 1.00. At the same time, the current approach gives an improvement in the range of 0.6–24.74, 0.8–24.80, 10–17.00% in terms of false positive rate, true negative rate and Matthews correlation co-efficient respectively from the state-of-the-art techniques with detection rate of 0.6650 and accuracy of 99.18%, respectively.

银行组织中的欺诈检测系统依赖于数据驱动的方法来识别欺诈交易。由于金融机构需要在日志数据上运行积极的作业来执行数据挖掘任务，因此实时检测每笔欺诈交易成为一项具有挑战性的任务。本文介绍了一种信用卡欺诈检测的新模型，该模型结合了集成学习技术，如 boosting 和 bagging。我们的模型通过构建装袋和提升集成分类器的混合模型，结合了这两种技术的关键特征。使用我们的模型对巴西银行数据和 UCSD-FICO 数据进行的实验表明，在检测看不见的欺诈交易方面比最先进的数据更坚固，因为数据不平衡问题是通过混合策略处理的。所提出的方法在真阳性率、真阴性率、假阳性率方面分别以 43.35-68.53、0.695-11.67、43.34-68.52、42.57-67.75、3.5-13.06、24.58-34.35%假阴性率、检测率、准确性和曲线下面积来自最先进的技术，马修斯相关系数为 1.00。同时，当前的方法在假阳性率、真阴性率和 Matthews 相关系数方面分别从 state-of- 提高了 0.6-24.74、0.8-24.80、10-17.00%检测率分别为 0.6650 和 99.18% 的最先进技术。假阳性率、真阴性率、假阴性率、检测率、准确度和曲线下面积来自最先进的技术，马修斯相关系数为 1.00。同时，当前的方法在假阳性率、真阴性率和 Matthews 相关系数方面分别从 state-of- 提高了 0.6-24.74、0.8-24.80、10-17.00%检测率分别为 0.6650 和 99.18% 的最先进技术。假阳性率、真阴性率、假阴性率、检测率、准确度和曲线下面积来自最先进的技术，马修斯相关系数为 1.00。同时，当前的方法在假阳性率、真阴性率和 Matthews 相关系数方面分别从 state-of- 提高了 0.6-24.74、0.8-24.80、10-17.00%检测率分别为 0.6650 和 99.18% 的最先进技术。