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More Reward than Risk Using AI for Wealth Management

Feb 07, 2019

In recent years, the financial and banking industries have become increasingly reliant on artificial intelligence (AI) solutions to manage and optimize their operations. Particularly interesting applications of these tools can be found in investment banking. For example, modern investing algorithms exist that can select and manage the contents of a client’s portfolio with limited human oversight. These tools allow brokers to access and utilize a near infinite amount of market data with both speed and efficiency, and they are already making a huge impact in the industry. While there are clear benefits of using AI in this context, no solution is perfect.The banking industry is incredibly well-established and there will inevitably be growing pains associated with adopting new technology in such an environment. There are inherent risks and rewards when leveraging these applications.

Let’s begin by examining the risks. When considering the use of AI in investment banking, these potential risks must be kept in mind:

  • Algorithm Shortcomings: The stock market is an unpredictable entity that is driven by an exponential number of variables. As such, no solution will be able to perfectly anticipate the performance of a given portfolio.
  • Defect Management: Bugs can allow many poor decisions to be executed in a relatively short amount of time. This can be detrimental when handling large sums of money for investors.
  • "Black Box” Solutions: By its very nature, AI solutions are something of a “black box”.The complexity of the algorithms used to power them often make it difficult for human investors to understand the reasoning behind a machine’s decisions.

In a relatively short amount of time, AI has begun to fundamentally change the way we invest our money. In most cases, these changes are positive and allow investors to steward their clients’ finances with efficiency and excellence. Some key advantages of using AI solutions in this field include:

  • Lower Management Costs: AI can perform historically complex tasks with increased speed and accuracy. This drives down management costs for clients, as human investors will take a more supervisory role in investing.
  • More Diversified Portfolios: Machine learning programs can analyze a wide range of data across disparate industries. This facilitates the creation of more diverse portfolios for potential clients containing both traditional (i.e., stocks and bonds), as well as non-traditional investments (i.e. foreign and cryptocurrencies).

  • Impartial Decision Making: The use of investment algorithms removes the inherent biases introduced from human emotion and flawed reasoning. This mitigates the risk of poor decision-making.
  • Pattern Identification: AI can identify complex patterns and market indicators that would be difficult for a human to detect.

While it is always important to measure risk, the benefits of adopting AI solutions in this industry tend to outweigh the costs. Machine learning is an incredibly useful tool in analytics, when implemented with the proper care and foresight. AI applications in investment banking show extraordinary promise and will likely play a key role in ensuring the financial security of future generations.