Accurate prediction of charge-off rates has been one of the major challenging tasks in the credit card industry. Credit policy helps an institution develop strategies within the planned asset quality range that are consistent with the institution’s profitability goals. Usually, strategic business analysis is incorporated by credit card issuers to develop credit policy and guidelines with legal and regulatory constraints. A higher charge-off rate exhibits a higher risk to a company. The charge-off rate for a given bank or issuer is calculated by dividing the dollar amount of charge-offs by average outstanding balances on credit cards issued by the firm. Thus the account is written-off as bad debt.
This is usually a final action since it is an indication to lenders that the consumer will never pay off their account. Typically, a bad credit card debt will be marked as charged-off after six months of non-payment, and it is withdrawn as an asset from the lender’s accounts. Consumers must issue payments by the due date, and failure to do so will result in putting the consumer’s account into delinquency or default. The term charge-off means an outstanding credit card debt, which is written off as bad debt. Similar to any industry, the goal in the consumer credit industry is to maximize profits by measuring and controlling risk and avoiding exposure to default (also known as charge-off), as much as possible. 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. 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 features that were selected by each of these models covered all three sectors of the economy. 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. We develop two versions of the forecasting expert system, which utilize different approaches to select between the lags added to each indicator. The state of the art machine learning models are used to develop the proposed expert system framework.
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. In this paper, we propose an expert system for loss forecasting in the credit card industry using macroeconomic indicators. Different macroeconomic conditions affect individuals’ behavior in paying down their debts. Hence, managing the charge-off rates is a vital task for the profitability of the credit card industry. banks consists of credit card portfolios. A major part of the balance sheets of the largest U.S.
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