  A major part of the balance sheets of the largest US 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.
