Risks can come from various sources including uncertainty in financial markets, threats from project failures (at any phase in design, development, production, or sustainment life-cycles), legal liabilities, credit risk, accidents, natural causes and disasters, deliberate attack from an adversary, or events of uncertain or unpredictable root-cause. There are two types of events i.e. negative events can be classified as risks while positive events are classified as opportunities. Several risk management standards have been developed including the Project Management Institute, the National Institute of Standards and Technology, actuarial societies, and ISO standards.
1. Introduction
The financial crisis of 2007–2009 highlighted the importance of risk management within financial institutions. Particular attention has been given to the risk management practices and policies at the mega-sized banks at the center of the crisis in the popular press and the academic literature. Few dispute that risk management at these institutions—or the lack thereof—played a central role in shaping the subsequent economic downturn. Despite this recent focus, however, the risk management policies of individual institutions largely remain black boxes.In this paper, we examine the practice and implications of risk management at six major U.S. financial institutions, using computationally intensive “machine-learning” techniques applied to an unprecedentedly large sample of account-level credit card data. The consumer credit market is central to understanding risk management at large institutions for two reasons. First, consumer credit in the United States has grown explosively over the past three decades, totaling $3.3 trillion at the end of 2014. From the early 1980s to the Great Recession, U.S. household debt as a percentage of disposable personal income has doubled, although declining interest rates have meant that debt service ratios have grown at a lower rate. Second, algorithmic decision-making tools, including the use of scorecards based on "hard" information, have become increasingly common in consumer lending (Thomas, 2000). Given the larger amount of data, as well as the larger number of decisions compared to commercial credit lending, this new reliance on algorithmic decision-making should not be surprising. However, the implications of these tools for risk management, for individual financial institutions and their investors, and for the economy as a whole, are still unclear.
Credit card accounts are revolving credit lines, and because of this, lenders and investors have more options to actively monitor and manage them compared to other retail loans, such as mortgages. Consequently, managing credit card portfolios is a potential source of significant value to financial institutions. Better risk management could provide financial institutions with savings on the order of hundreds of millions of dollars annually. For example, lenders could cut or freeze credit lines on accounts that are likely to go into default, thereby reducing their exposure. By doing so, they potentially avoid an increase in the balances of accounts destined to default, known in the industry as “run-up.” However,
cutting these credit lines to reduce run-up also runs the risk of cutting the credit limits of accounts that will not default, thereby alienating customers and potentially forgoing profitable lending opportunities. More accurate forecasts of delinquencies and defaults reduce the likelihood of such false positives. Issuers and investors of securitized credit card debt would also benefit from such forecasts and tools. Finally, given the size of this part of the industry—$861 billion of revolving credit outstanding at the end of 2014—more accurate forecasts would improve macroprudential policy decisions, and reduce the likelihood of a systemic shock to the financial system.
With respect to this measure, we find that our models perform well. Assuming that cutting the lines of bad accounts would save a run-up of 30% of the current balance, we find that our decision tree models would save about 55% of the potential benefits relative to perfect risk management, compared to taking no action for the two-quarter horizon forecasts (this includes the costs incurred in cutting the lines of good accounts). When we extend the forecast horizon, the models do not perform as well, and the cost savings decline to about 25% and 22% at the three- and four-quarter horizons, respectively. These results vary considerably across banks. The bank with the greatest cost savings had a value added of 76%, 46%, and 35% across the forecast horizons; the bank with the smallest cost savings would only stand to gain 47%, 14%, and 9% by implementing our models across the three horizons. Of course, there are many other aspects of a bank’s overall risk management program so the quality of risk management strategy of these banks cannot be ranked solely on the basis of these results, but the results do suggest that there is substantial heterogeneity in the risk management tools and effective strategies available to banks
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