A Perspective of Why Financial Crises Happen, and How to Predict Them Before They Happen

The rise in the complexity and globalization of financial services has contributed to stronger linkages between various institutions. While this has led to smoother credit allocation and greater risk diversification among individual institutions, it has also increased the potential for disruptions spreading across banks and across borders as exemplified by the recent European banking crisis.  A financial contagion can take the form of shocks to haircuts, which trigger liquidity hoarding at some institutions that propagate across the interbank network. We live in a fascinating data-driven world full of challenges and opportunities – the world of Big Data. This is a world of unmatched availability of data to drive better decision making on a more timely basis but it is also a world where perceptions and panic can spread much faster than at earlier times.

There is a growing body of work studying the role of bank behavior in driving contagion as well as application of network techniques to better understand the probability and spread of contagion.  In these studies, the financial links between individual institutions are often represented as stable, embedded and long-term connections based on random graphs. However, one financial institution can be quite different from another in equity value and liabilities; the capacity of resisting risk impact is quite different. As a result, heterogeneous capacity of nodes is an important feature of real interbank networks versus a random network model. Also, the sequencing of transactions between institutions over time provides valuable insight into understanding the pattern of contagion spread across the network. Finally, the ability to analyze the behavior of interbank network in the context of other data in the media, development of events in regional politics, and impact of financial policy decisions in various countries can provide unforeseen insights.

New generation of advanced analytics, such as visual analysis, can be used to evaluate the resilience of the financial system to liquidity shocks affecting subset of banks. Advanced visual analysis applications can be used to explore the resilience of the financial system to liquidity shocks affecting a subset of banks under different network configurations, degree of connectivity between financial institutions, haircut assumptions, and balance sheet characteristics.  These tools combine advanced graph visualization techniques together with social and dynamic network analysis techniques to provide a comprehensive visual tool for analysts and policy makers.