The impact of COVID-19 on artificial intelligence in banking.

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Date: Apr. 15, 2021
Publisher: Bruegel
Document Type: Article
Length: 1,206 words
Lexile Measure: 1270L

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COVID-19 has not dampened the appetite of European banks for machine learning and data science, but may in the short term have limited their artificial-intelligence investment capacity.

Before COVID-19, banks were keen adopters of artificial intelligence (AI), including machine learning and other advanced data-science techniques. After the technology sector, the financial services sector was the biggest spender on AI services in 2018. Thanks to such massive investment, AI now powers a wide range of tasks. Machine-learning systems trade, detect fraud, engage with customers and help banks comply with regulatory requirements.

On the face of it, the pandemic should reinforce banks' adoption of AI. Accelerated digitalisation generates new data processing needs, while ultra-low interest rates and weakened revenues call for cost savings. But the crisis weakens the business case for AI in at least two respects. Machine-learning models trained on historical data are less useful when the present looks nothing like the past - 2019 data is little help in predicting whether Spanish hotels will survive 2021. Weak profitability may also drain banks' R&D budgets and executive patience to invest in fundamental transformation. Rather than speeding up AI adoption, could the pandemic therefore impede the banking sector's use of, and spend on, AI?

COVID-19 and the banking business case for AI

Early evidence suggests that banks' interest in adopting machine learning and data science has continued during the COVID-19 crisis, and may have increased. Half of banks polled in a summer 2020 Bank of England survey said the COVID-19 crisis has made machine learning and data science more important for the future[1] . Over a third reported an increase in the number of their planned use-cases, with the business areas directly affected by the pandemic, such as customer engagement, expected to grow the most. One explanation for this is that machine learning...

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