Can Financial Services Firms Balance the Opportunities and Risks of Artificial Intelligence?

As fintech disrupters continue to put pressure on traditional financial services firms, artificial intelligence (AI) is emerging as a key technology for keeping pace in an evolving industry.

However, implementing machine learning and AI into a complex infrastructure of legacy technology is easier said than done.

In order for traditional financial services firms to keep pace with digital natives, it’s critical that they balance the opportunities and risks associated with AI in the industry.

Opportunities for Artificial Intelligence in Financial Services

We’ve previously touched upon the ways in which machine learning and AI will impact customer experience in digital financial services, but the potential opportunities and use cases are much broader.

The value of AI lies in scalability and speed for financial services firms. Whether it’s identifying corrupt practices, making investment decisions, processing customer data, assessment of risk in portfolios, or any other process, applying AI algorithms can add a new level of depth and quality to financial services operations.

This is why the majority of respondents in a 2016 Baker & McKenzie survey felt that AI and machine learning integration will accelerate competition in the financial services industry. While the potential use cases for AI are seemingly endless, the short-term focus for these survey respondents fall into 3 key categories:

  • Credit provisioning
  • Asset management
  • Trading and hedge funds

Despite the optimism for artificial intelligence in financial services, the Baker & McKenzie survey shed light on an interesting theme—that the use of artificial intelligence is a bit of a double-edged sword alongside compliance risks of artificial intelligence.

Financial Services Firms Calling for Increased Regulation to mitigate the risks of Artificial Intelligence

“Financial institutions have been fined billions of dollars because of illegality and compliances breaches by traders. A logical response by banks is to automate as much decision-making as possible, hence the number of banks enthusiastically embracing AI and automation. But while conduct risk may be reduced, the unknown risks inherent in aspects of AI have not been eliminated.”—Arun Srivastava, Head of Financial Services Regulation at Baker & McKenzie

The more we scale financial services operations with algorithms, the greater risk of non-compliance if those algorithms aren’t perfectly formed. With the industry facing more and more regulations in the wake of the financial crisis, it’s no surprise that 60% of survey respondents say more regulation is needed to offset the risks of AI and machine learning.

Even though financial services firms have been bombarded with new regulations in recent years, only 16% of survey respondents felt that they were being over-regulated. Compliance has its challenges, but there is a real desire for regulators to step in and create structure around business practices and new technologies, including the use of artificial intelligence and machine learning.

Unfortunately, 75% of financial services leaders feel that regulators are behind the curve when it comes to understanding and keeping pace with machine learning and artificial intelligence. Not only that, but nearly half of firms aren’t prepared internally for the corporate liability, data protection and privacy risks that come with artificial intelligence opportunities.

Artificial intelligence might garner excitement for its ability to scale and speed up digital processes, but regulators and financial services firms alike must learn to balance the opportunities and risks of artificial intelligence before diving into AI and machine learning for their firms.

Customer Experience Analytics—A Good Starting Point for AI in Financial Service

When you’re just starting out with artificial intelligence and machine learning, you want to integrate it into an operation without introducing too much risk to the organization. This is why implementing a customer experience analytics solution is better than starting by overhauling something like your trading processes.

With the right customer experience analytics solution, you can identify critical points of friction in your digital services while ensuring compliance with data privacy regulations. Getting started with AI in customer experience analytics will prove its value as you learn how it can be implemented throughout your organization.

If you want to learn more about the digital pressures facing financial services institutions (including artificial intelligence and machine learning), download our free ebook, Uncovering Hidden Revenue from Online Customers for Financial Services.