Examining the Impact of Artificial Intelligence on the Evaluation of Banking Risk

Swankie, George Daniel Brown and Broby, Daniel (2019) Examining the Impact of Artificial Intelligence on the Evaluation of Banking Risk. Preprint / Working Paper. University of Strathclyde, Glasgow.

[thumbnail of Swankie-Broby-2019-Examining-the-impact-of-artificial-intelligence-on-the-evaluation-of-banking-risk]
Preview
 Text. Filename: Swankie_Broby_2019_Examining_the_impact_of_artificial_intelligence_on_the_evaluation_of_banking_risk.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (1MB)| Preview

Abstract

This paper examines the relationship between Artificial Intelligence (AI) and banking risk management. The global financial crisis highlighted their importance and now banks are subject to more stringent regulation regarding their capital adequacy. Meanwhile, advances in technology are driving changes in the way banks operate. AI is at the core of this and has the potential to revolutionise financial services. It is comprised of several techniques that allow computers to mimic human behavior and analyse vast quantities of data in seconds. These techniques include machine learning, deep learning, speech recognition, natural language processing and visual recognition. We investigate the extent to which each of these techniques can be implemented in the context of financial services. In this respect, we look at credit, operational, liquidity and reputational risk, all of which can have a negative impact on the earnings of an organisation. AI has the potential to help mitigate these risks in banks and address some of the highlighted management issues. We conclude that the application of AI can add significant economic value to banking operations.

ORCID iDs

Swankie, George Daniel Brown and Broby, Daniel ORCID logoORCID: https://orcid.org/0000-0001-5482-0766;
CORE (COnnecting REpositories)