A major part of the balance sheets of the largest U.S. banks consists of credit card portfolios. Hence, managing the charge-off rates is a vital task for the profitability of the credit card industry. Different macroeconomic conditions affect individuals’ behavior in paying down their debts. In this paper, we propose an expert system for loss forecasting in the credit card industry using macroeconomic indicators. We select the indicators based on a thorough review of the literature and experts’ opinions covering all aspects of the economy, consumer, business, and government sectors. The state of the art machine learning models are used to develop the proposed expert system framework.

We develop two versions of the forecasting expert system, which utilize different approaches to select between the lags added to each indicator. Among 19 macroeconomic indicators that were used as the input, six were used in the model with optimal lags, and seven indicators were selected by the model using all lags. The features that were selected by each of these models covered all three sectors of the economy. Using the charge-off data for the top 100 US banks ranked by assets from the first quarter of 1985 to the second quarter of 2019, we achieve mean squared error values of 1.15E−03 and 1.04E−03 using the model with optimal lags and the model with all lags, respectively. The proposed expert system gives a holistic view of the economy to the practitioners in the credit card industry and helps them to see the impact of different macroeconomic conditions on their future loss.

美国最大的银行资产负债表的主要部分包括信用卡投资组合。因此，管理冲销率对于信用卡行业的盈利能力至关重要。不同的宏观经济条件会影响个人偿还债务的行为。在本文中，我们提出了一种使用宏观经济指标进行信用卡行业损失预测的专家系统。我们根据对文献的全面回顾以及涵盖经济，消费者，商业和政府部门各个方面的专家意见来选择指标。最先进的机器学习模型用于开发建议的专家系统框架。

我们开发了两个版本的预测专家系统，它们使用不同的方法在添加到每个指标的滞后之间进行选择。在用作输入的19个宏观经济指标中，模型中有6个具有最佳滞后，而所有滞后均由模型选择了7个。这些模型所选择的功能涵盖了经济的所有三个领域。使用从1985年第一季度到2019年第二季度按资产排名的美国前100家银行的冲销数据，使用具有最佳滞后的模型，我们获得1.15E-03和1.04E-03的均方误差值和所有滞后的模型。