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What Is the US Banks’ AI Strategy?

Artificial intelligence and machine learning saw a significant spike of attention in the past few years – whether it’s through partnerships, acquisitions, or in-house developments. The largest financial institutions in the US have been involved in one way or another in bringing artificial intelligence into operations and customer-facing functions.

A recent study of 34 major banks across several geographies (US, EU, Singapore, Africa, Australia, India) by MEDICI Team found that 27 out of these 34 banks have implemented AI in their front-office functions in form of a chatbot, virtual assistant, and digital advisor. Some of the most prominent banks in this space across regions are Bank of America, OCBC, ABN Amro, YES BANK, etc. While front-office applications have certainly seen a higher intensity, scope, and adoption, the AI strategy in the US banking industry, in reality, is far more diverse.

As for the US, all major banks have been found to be experimenting with artificial intelligence in one of the four sets of applications: front-office uses; back-office uses; trading and portfolio management in financial markets; and uses of AI and machine learning by financial institutions for regulatory compliance (RegTech) or by public authorities for supervision (SupTech).

Let’s look at particular use cases of artificial intelligence that are currently being explored by the largest financial institutions in the US.

Automation

RPA is expected to have a $6.7 trillion global economic impact, a plan that will result in a 40-45% growth of global spending on technology. In addition, the estimated global market potential of RPA stands at $8.75 billion by 2024. Automation is one of the more explored areas of AI/ML application.

Barclays Bank, for example, has implemented RPA across various processes which include but are not limited to – fraud detection, risk ...

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