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.
Highlights
This is an opinion paper based on a literature review where we explore the advancements of big data and Artificial Intelligence in the banking industry and how these tools and capabilities could be used to address the “too big to fail” institutions
Big data and Artificial intelligence (AI) entered the financial institutions as a response to competitive pressure, in itself a driver for advances and research in this field
A number of potential applications where supervision and resolution authorities would work on big data structures by employing AI algorithms have been presented, jointly contributing to the various pillars of an enhanced financial supervision: decreasing probability of default, anticipation of potential defaults, and limitation of losses given default though swift and wellinformed decision
Summary
The author questions if the advancements made in big data and artificial intelligence fields, widely adopted in the financial industry by market participants, could be adopted by authorities to address the too big to fail institutions and limit the bail-out even further (or eliminate them). We will propose tools based on Big Data and Artificial Intelligence that could be deployed by supervision and resolution authorities.
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