Data integrity
Algorithms are only as good as their inputs, and sometimes a human is needed to identify and explain why an outcome is wrong, that is, to validate the data.
Indeed, accurate and relevant data inputs are essential, so a major challenge is to find clean data and institute consistent channels to access it. Too often data quality is compromised and inadequate, which should prompt vendor suppliers to gain an edge over competitors if they can provide reliable sources and feeds. Moreover, cybersecurity and lax controls are a perennial problem.
Many banks believe that they can differentiate themselves with clients by retaining a proprietary data management capability.
Besides, outsourcing data quality carries inherent risks because it implies extending complete trust to an external party and surrendering control over a vital part the automated system.
Machines and humans
On a practical level, it seems clear that humans at brokerages are still needed to manage risk, supervise systems, oversee the connection between clients and automated processes, and interpret regulation – lawyers will always be in demand somewhere.
Rarely do machines just speak to machines. Instead, automation simplifies, streamlines and codifies the trade order cycle, which helps sales-traders provide a better service for their clients.
In some cases, automated processes are introduced for traditional mainstream trades while high value transactions are performed manually. In addition, the development of more sophisticated algos and the introduction of artificial intelligence (AI) technology means that the dichotomy is actually reversing: humans handle the basic transactions and machines manage the exotic, high-value trades.
Although many banks have been working on aspects of AI, such as predictive analysis for several years, there is now more clarity about its wider potential and more structure about its employment. It is being used to identify pattern variations and implementation shortfalls, examine alternative scenarios and process more variables.
Machines learn from history rather than in real-time, but access to extensive contemporaneous data sources, including news and social media, mean that the history is fresh and relevant. They are more dynamic than previously, when they were rule-based and static.
Perhaps at some point humans can’t compete with machines. Automation is taking over time-consuming operational processes such as client on-boarding, and algorithms are already trading news as well as implementing portfolio strategies – which suggest that the investment manager’s role might become obsolete too.
Besides, if your job can be eliminated by a computer, then how long would you actually want to do that job? And new technology will continue to require complementary skill sets from people. Trading experience, networks and market savvy might diminish as job requirements, but quant expertise will be highly valued and, ultimately, essential.
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