An Artificial Intelligence Approach to Regulating Systemic Risk

We apply an artificial intelligence approach to simulate the impact of financial market regulations on systemic risk-a topic vigorously discussed since the financial crash of 2007-09. Experts often disagree on the efficacy of these regulations to avert another market collapse, such as the collateralization of interbank (counterparty) derivatives trades to mitigate systemic risk. A limiting factor is the availability of proprietary bank trading data. Even if this hurdle could be overcome, however, analyses would still be hampered by segmented financial markets where banks trade under different regulatory systems. We therefore adapt a simulation technology, combining advances in graph theoretic models and machine learning to randomly generate entire financial systems derived from realistic distributions of bank trading data. We then compute counterparty credit risk under various scenarios to evaluate and predict the impact of financial regulations at all levels-from a single trade to individual banks to systemic risk. We find that under various stress testing scenarios collateralization reduces the costs of resolving a financial system, yet it does not change the distribution of those costs and can have adverse effects on individual participants in extreme situations. Moreover, the concentration of credit risk does not necessarily correlate monotonically with systemic risk. While the analysis focuses on counterparty credit risk, the method generalizes to other risks and metrics in a straightforward manner.