Artificial Intelligence in Capital Markets: The Next Operational Revolution

EXECUTIVE SUMMARY

Artificial Intelligence is among the hottest topics in the financial markets today, seeming to promise untold benefits to the major banks that are embracing it, and destruction to traditional asset managers and other victims who get left behind. The truth may be a little different.

Indeed, Opimas does foresee myriad benefits coming to financial players in the future from the robots and machines that think and learn and analyse mountains of unorganized data, enabling firms to anticipate and better serve customers’ needs. We estimate a 28% improvement in financial institutions’ cost-to-income ratio by 2025 as they automate routine processes currently performed by employees. In 2017, we expect financial firms to spend more than US$1.5 billion on AI-related technologies and, by 2021, US$2.8 billion, representing an increase of 75%. (see Figure 6) This does not include M&A activity and investments in start-ups.

At the same time, the full benefits of AI technologies may elude them until they train machines to think properly. Meanwhile, AI tools might well offer traditional firms new ways to prosper. We estimate that a third of the jobs lost to machines will be replaced by technology and data providers serving the industry’s new needs.

Artificial Intelligence is a term used very loosely in the financial industry to describe various technologies capable of addressing firms’ unsatisfactory operational efficiency and other needs. The various AI tools are, in fact, quite distinct and should be used to tackle different business issues: 

  • Robotic Process Automation (RPA): This technology aims to replace manual handling of automated processes for repetitive and high-volume tasks. 
  • Machine Learning (ML): This is a process on which most AI is being built. It requires using vast amounts of data to train a system and fine tune it. 
  • Deep Learning (DL): This is a specific method of machine learning that has been a game changer in data-intensive, machine-learning processes. 
  • Cognitive Analytics (CA): This approach mimics the human brain in making deductions from vast amounts of data. 

FIGURE 1. THE IMPACT OF AI ON FINANCIAL INSTITUTIONS’ PROFITABILITY

Source: Opimas Analysis
Artificial Intelligence will provide myriad benefits to financial institutions, including improved operational efficiency and cost reductions. It will also upgrade legacy IT systems, improve data and analytics, enhance client services and increase revenue generation. We expect that by 2025, the rollout of AI technology by financial institutions will decrease the cost/income ratio by over 25% (see Figure 1), notably through a reduction of overall headcount.

Worldwide, by 2025 we expect AI technologies to reduce employees in the capital markets by 230,000 people. The asset management industry will shrink most, with around 90,000 people being replaced by machines. On the other end, close to 30,000 new jobs will be created for technology and data providers who respond to the financial industry’s new requirements and demands.   

Prioritising the uses of AI will be critical, as these technologies will benefit every business operation within financial institutions. The key to AI is not in finding uses but, rather, selecting the right area in which to start, based on the firm’s positioning. That sets it off from other “trendy” technologies in search of capital markets applications—Blockchain  is a perfect example. RPA, for example, is more likely to appeal to securities players with large, back-office processing activities that can create useful, meaningful information from new unstructured data sources for data providers, hedge funds and brokers. 

Considering the tremendous potential for AI applications in the capital markets, we expect spending in the field to increase by 75% from 2017 to 2021, reaching US$2.8 billion . Cognitive analytics and machine learning solutions will take the lion’s share of this investment. While RPA will account for significant spending in the short run, we expect that it will eventually fade as opportunities for its implementation decline. 

Investments by financial firms in AI start-ups are also very likely to continue due to the limited talent pool in the space. The partnership between AI start-ups and financial institutions is a win-win: The former bring tech expertise, and the latter have business knowledge and data.

Artificial intelligence will reinforce the business model of financial institutions. Since they need access to vast amounts of data to efficiently train an AI system, banks have a clear advantage over potential new entrants because they can leverage their huge internal data sets. Once financial firms revamp their business operations using AI technologies, they will raise the barriers to entry in their market so high that it will be nearly impossible for newcomers to compete. 

Other non-financial players in the capital markets eco-system will have to adapt to the new world ahead. As with any technological revolution, AI will decrease the value of traditional services that it’s replacing and increase that of adjacent ones. Hence, BPO and IT providers will have to reconsider their business models and value propositions. Data providers such as exchanges will have to accelerate build up and/or acquire services in the unstructured data space as the value of traditional market data will quickly diminish.

Two caveats: At this point in the development of AI tools, it would be extremely risky to rely fully on them—whether to handle processes, conduct analysis, make judgments or take actions. Human supervision is still crucial as the machines learn. In addition, a key element in successfully developing AI-related technologies is access to vast amounts of data in order to train the systems. Without it, you’ve just created Artificial Stupidity. 

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