AI has emerged as one of the current major trends that is helping banks increase output dramatically, several experts said at the Reuters Next conference in New York. Yet, applying the discovered methods of increasing efficiency by efforts of AI is not such an easy task as converting these achievements into measurable profit.
AI Drives Bank Productivity, But Profits Remain Elusive
Organizations have been using AI in several areas, such as client interfaces in the form of human-like bots and integrated into internal activities, including areas like human resources, risk management, compliance, and finance. Further, some banks are looking for an opportunity to apply AI to improve the variety of wealth management goods and services, setting a goal to bring new individual solutions to clients.
Goldman Sachs CEO David Solomon has also touched upon the use of AI in increasing code velocity across a big engineering organization at the bank. Goldman Sachs has designed its workforce with 11,000 engineers for improving the new generation capabilities of machine learning and AI, the use of which Solomon assumes would increase the productivity of engineers by 20-30%, which in his opinion would be a great advantage to the bank’s overall efficiency.
In a similar vein, BNY Mellon CEO Robin Vince talked about how his bank has introduced AI applications for improving its processes. Like many of the other large financial services companies, BNY Mellon sees AI utilization as a means to enhance the effectiveness of both the employees collecting and providing data as well as customers anxiously awaiting such information.
Still, Solomon and Vince also noted that the challenge of finding ways of measuring the monetary value of what AI delivers persists. Offering definite potential for operational enhancements, the processes for transforming those enhancements into a profitable business model remain nascent in banks.
AI Boosts Bank Efficiency, But Profitability Remains Unclear
BNY Mellon CEO Robin Vince described how thousands of the bank’s workers have been using AI to develop and launch ‘digital workers’ to help with their daily workloads. This is in line with the bank’s strategy in policy implementation of the regulation of AI in the banking industry to enhance productivity. Yet, much has been written about how AI can improve business operations and revenue, yet little solid evidence has emerged.
Although there are clear signs that AI is enhancing efficiency at most banks, there are no signs of corresponding benefits in the form of return on investment. Banks and other financial organizations are only just beginning to work out how exactly they can turn all this productivity into profits. Additional insights from its related interview with BMO Financial Group’s chief AI and data officer, Kristin Milchanowski, suggested a key point that before AI is likely to create a significant ripple effect on the banking sector, the banks in question must first set precise and custom use cases for AI in a way that can directly influence their profitability.
Milchanowski, who joined BMO in October as the head of AI, admitted the subject had been subject to a ‘hype cycle.' The speaker argued that the hope in AI has attracted a great deal of attention and funding; the real question is how to emerge from this hype effectively to produce solid financial value in banking.
It is evident that the banking sector faces a number of difficulties at the moment as organizations try out AI. In terms of applying the concepts and boosting the abilities in areas that are tightly connected with generating revenues, the businesses are still learning.
As AI finds its way into the banking systems, firms need to ensure that they consider cases that are actionable and would make sense. Lack of concrete plans in AI-related $-making, the big financial institutions may not be in the right position to finance such types of technology even if they know they hold the potential for massive change for their operations and efficiency increase.
AI Streamlines Bank Operations, But Revenue Gains Still Elusive
Kristin Milchanowski, BMO’s Chief AI and Data Officer, has highlighted the mismatch between what is expected from AI in banking and what is currently being realized. When learning about the development of AI, many had expected to see considerable revenue improvement or cost optimizations, but according to Milchanowski, it did not bring a large volume of revenue. However, its use more often than not has led to the enhancement of organizational efficiency and effectiveness in processes.
For example, at BMO, the use of AI has been most beneficial in affording equities teams the ability to create reports faster. Formerly, some activities that used to claim more than four hours of every day’s time have been done in less than an hour, so freeing more time for the analysts to engage in higher value-added activities such as innovation and strategizing. While this improvement has underpinned productivity in the undertaking, it has not as yet yielded tangible profits to the bank.
According to Milchanowski, society needs to understand how AI can solve specific problems in order to unleash its value. To this, she opined that while AI was a highly efficient system, it would only achieve its optimal value once banks identified areas in which the technology could result in incremental value add to revenues or enhance the methodology of speaking to clients.
In the future perspective, according to Milchanowski, AI has the ability to enhance trading mechanisms and can act as sources for new business. These areas could become the places where the prospects many see in the technology would be realized financially. Though a major issue is then manifested in regard to flows from AI benefits to revenues and business operations.
Currently, institutions such as BMO are still experimenting with and tweaking how AI is implemented in their operations with the prospect that as future cases and applications emerge, it will increase the ROI. Here the main issue will be determining how AI can not only increase efficiency but also produce potential for growth and making profit.
