Big data and AI: a potential solution to end the conundrum of too big to fail financial institutions?

Abstract

Abstract The “too big to fail” institutions are a widespread concern, especially in the financial world. Their failure can create severe economic downturns and social turmoil. In past bank failures, governments intervened with public funds to save such institutions from collapse to avoid economic downturns. Since, measures have been put in place to prevent bank failures and limit the utilisation of public funds. However, failures cannot be prevented and risks of affecting the economy are always present in the case of too big to fail institutions. This article explores the possibilities offered by recent advancements in the fields of Big Data and Artificial Intelligence, widely implemented by the financial institutions themselves, as tools to be used by authorities in ending the too big to fail conundrum. The adequate implementation of these technological capabilities will contribute to the areas already targeted by governments – reducing the probability of failure and providing tools to limit negative externalities and spillover effects – and will also introduce a new capability that could address the too big to fail matter. Since financial institutions are, in their essence, data hubs, now in a digitalised format, the possibilities to automate tasks and provide insight for decisions should address the issue. The actual transfer of assets and liabilities to institutions that can carry on the activity, currently need years to be handle:. Big Data and Artificial Intelligence technologies could make such operations a matter of hours or days.