AI is coming to your financial institution, to make trading algorithms more reliable, lower fees and personalize financial products.
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New technologies have enabled tremendous evolution in the finance industry, especially over the past decade. Thanks to machine learning and artificial intelligence (AI), investors and consumers are getting access to more innovative tools, new types of financial products and a new potential for growth.
So, what kind of impact is AI having on banking and Wall Street, and how might the resulting impact on entrepreneurs evolve in 2019?
1. More accessible technologies for consumers
Some of the best AI developments have been reserved for private banks, professional investors, venture capital firms and other major organizations. But 2019 and beyond will likely bring the trend of more accessibility for consumers, too. Since 2015, we’ve seen the rise of AI-based products designed for the average consumer, like Betterment or Wealthfront. We’ve also seen the inclusion of AI-embedded into products that consumers are already using.
In the near future, this could expand to AI’s being used to create a more intuitive user experience for financial products like banking or investment apps. As the financially literate and tech-savvy younger generation becomes wealthier, banks and financial startups will need to offer more products for improving credit scores, seeing better investment returns and performing other functions.
2. Refined asset-management algorithms
There will be better asset management algorithms. Asset management companies are increasingly relying on machine learning and big data analytics to make smarter, real-time investment decisions on behalf of their investors and clients.
These algorithms can progress in several distinct ways. For example, they can incorporate more data into their decision-making tree, they can experiment with new strategies on the fly to self-improve and they can broaden their focus to consider a more diverse range of assets.
Gal Krubiner, the CEO of Pagaya Investments, recently predicted in Forbes that, “Asset management firms that combine speed, scale and accuracy of advanced technology with human creativity and nuance will find success in 2019. Those who fail to build digital skills or tap into data effectively will struggle to stay afloat.”
3. Lower fees
One of the biggest advantages of widespread AI adoption is the decreased reliance on manual labor. An algorithm or tech product that could conceivably replace a human worker could save companies tens of thousands of dollars a year, and dramatically reduce the costs and fees that consumers pay.
For many years, most jobs in the financial industry were considered irreplaceable by AI due to those jobs’ high-level critical thinking demands and complex nature. But now it’s estimated that 90,000 of the 300,000 current jobs in asset management will disappear by 2025, thanks to AI and automation.
However, AI could also enable employees in financial services to do more human-required and high-level tasks. Rather than replacing their jobs outright, AI would provide a revolutionary new way (for people)to work that could save financial professionals the time they need to focus on more important things. It could also mean much greater accessibility and profitability for average investors.
Rather than paying a broker 5 percent of their portfolio gains per year, these investors — including many entrepreneurs — might receive back 0.5 percent of their gains by relying on an asset management algorithm. Certain financial processes and services, like checking or transfers, would also get less expensive, reducing consumers’ financial burden even more.
4. Compensation for weaknesses
Trading via algorithms has been around for decades, but only through machine learning and refinement is such trading now able to compensate for some of its inherent weaknesses. For example, historically, algorithms have made the market more volatile. Many algorithms, for instance, may notice conditions for a selloff at the same time.
If they then start selling equities en masse, the broader market will take notice, and begin to deepen the selloff. This could lead to a flash crash, or something worse, if not accounted for. And this isn’t hypothetical — flash crashes occurred in May 2010, April 2013, January 2015 and October 2016; smaller crashes have happened even more frequently.
Fortunately, advancements in machine learning have allowed developers to create trading algorithms that are more diverse, and therefore less reliant on the same types of decisions. Machine learning will also allow for continual self-refinement in 2019, producing better algorithmsthat can proactively avoid such issues.
5. More personalized financial products
AI is also enabling financial institutions to create more personalized consumer products. After collecting enough data on customer spending habits, banks can recommend specific types of loans, for instance, or a different type of account to better serve customer needs. They can also customize different mortgages, auto loans and other financial products in terms of interest rates, duration, and other factors important to specific users.
In 2019, we’ll start seeing more banks and credit unions take advantage of this opportunity. Banking consumers, meanwhile, will get more dynamic recommendations for which financial products they should try; and they’ll be exposed to new types of advertising to direct their purchases. With smarter asset management, more personalized financial products and more accessibility and intuitiveness coming their way, the average consumer at all levels of the financial industry will likely benefit in ways that will deeply impact their bottom line.