Enterprise Wide Architectures for Artificial Intelligence
The European Banking Authority (EBA) has conducted a series of meetings over the past few months to explore the state of the art of Artificial Intelligence (AI) adoption in the banking sector and identify the best regulatory approach for validation processes. These conversations bring to the surface important aspects regarding transparency of the algorithms, robustness of the processes, security of the applications, ethics of the decision-making. I am not new to discussing similar issues with regulators, given my professional risk management background to validate first internal models in the late 1990s. I was therefore pleased to join the debate and contribute with my experience and the IBM point of view.
The banking industry experienced a period of “quantitative exuberance” in the 1990s and early 2000s. Financial innovation was almost a daily routine fostering a fierce competition (not always sane) among investment banks and distribution networks (similarly, Fintech innovation is popping up every where). Risk management departments were established and a new profession was created for highly qualified individuals (similar to data science today). Basel I capital accords (1988) motivated banking boards to embed quantitive risk analysis (economic capital estimates) into economic decision-making and commit to significant investments into middle office and front office transformation to qualify for the capital savings granted by internal models (similarly, PSD2 and MiFID II are inviting financial institutions to modernise their banking infrastructure).
From an implementation point of view, financial institutions relied on two different practices:
The approaches devoted to specialised algorithms tended to identify economic value of risk management in the development of sophisticated quantitative methods, separating the analysis of profitability from the aggregated understanding of interdependency of risk factors. Instead, the integrated approaches were relying upon the idea of embedding advanced quantitative methods inside Enterprise Wide Risk Management Architectures with the scope to guarantee a joint analysis of all risk factors and to support a coherent capital allocation. The race to the algorithms (typically dominated by front offices) generated an exciting intellectual ecosystem from the point of view of mathematical research and highly specialized solutions, but it did not favour transparent and robust mechanisms capable of interacting with a business context evolving fast on interdependent markets. The financial crisis was inevitable.
Ultimately, banks adopting cartesian deterministic approaches could not find strategic value in their advanced yet isolated quantitative methods, which are always imperfect representations of the real world.
