In 2008, Warren Buffett famously floated the idea of a million-dollar bet against hedge fund managers. He would invest a million dollars in Vanguard’s 500 index fund, while the hedge fund managers would invest however they saw fit. Whoever ended up with higher returns over ten years would donate the gains to charity.
Many hedge fund managers like to boast about their returns, but only a single manager, Ted Seides, was willing to bet against Warren Buffett.
It’s been almost a decade since the bet was made, and the winning choice couldn’t be clearer. The fund is up 82 percent. The Seides funds are only up 22 percent.
Given that hedge funds managers have traditionally charged fees equal to 2 percent of assets under management, plus 20% of profits over a prescribed benchmark, while index funds like Vanguard only charge fees equal to roughly 0.05%, the contrast between the two options are even starker.
The situation is even clearer when we look at the industry more broadly. In fact, the overall data shows that 92% to 95% of actively managed funds were outperformed by their industry benchmarks over 15 years.
Consumers are quickly realizing the gains they can make by switching to index funds. This realization has been evidenced by the dramatic gains seen by Vanguard, which has more than doubled their assets under management since 2010 to more than $4 trillion. What’s more, a writer at Bloomberg estimates that Vanguard has saved investors $175 billion in fees since 1974. That’s pretty amazing.
New companies that promote automated investing such as and have sprung up in conjunction with the success of Vanguard, further indicating that automation is here to stay.
The bad news is that during this same time period, more than 10,000 jobs have been cut at the top 10 banks, largely due to the fact that these banks don’t have need for so many active fund managers.
Automated Retail Banking
What does all of this mean for retail banking?
To start, we can be certain that retail banking will only become more automated, not less so. You can see it happening already. Years ago, the work of a loan officer consisted entirely on manually researching a consumer’s history and deciding based on word of mouth and sound judgment whether or not that person deserved a loan. Now there are complex algorithms that can determine credit.
These algorithms are set to become more precise as digital companies expand the amount of data available to analyze. Companies like look at stuff like education,earning potential, and savings habits to make more accurate assessments about who deserves a loan. They can then usually offer a better price than most consumers can get from traditional lenders.
Right now Earnest focuses on refinancing student loans and offering personal loans, but it isn’t hard to imagine that as they refine their algorithms with more and more data they will have the upper hand on traditional financial institutions that aren’t leading with strong analytics capabilities.
is breaking similar ground on the small business loan front by perfecting SBA loan algorithms. Once again, they’re able to use their data to find new ways to define what it means to be creditworthy, and they’re making the loan approval process far faster as a result.
In an , Lendio CEO Brock Blake said, “I do believe that as alternative lending algorithms become smarter, the underwriting will get faster — to the point that you’ll be able to provide some info from your mobile phone and qualify for a substantial business loan within hours.” As companies like Lendio continue to expand their reach, they will acquire more data and in the process benefit from the data flywheel effect.
Another example on this front is , which is to include non-banking data such as Amazon interactions, social media communication, and sensors in phones such as GPS and accelerometers. By combining data from a wide range of disparate areas, TD Bank is paving the way to discover completely new ways to determine creditworthiness and provide financial help to their users. Again, this will enable TD to take what used to be a time-consuming and resource-intensive process and make it more valuable.
The Implications for Retail Banking
Just like automation in investment banking, we can see that automation in retail banking is generally good for the consumer. When it comes to retail banking we can hope that automation will continue to lower the price of loans and target the most creditworthy consumers with precision. This will ultimately be highly valuable to the overall economy.
At the same time, automation is bad news when it comes to the number of jobs needed at financial institutions. After all, when algorithms run the show, there’s less need for humans to be involved at every step.
Loan officers whose talents are primarily in relationship building will have to supplement those talents with knowledge about data sets to stay ahead of the competition in their field. If they refuse to do this they might find that lenders no longer have the same need for them that they once did. It’s a hard reality, but we’ve seen this play out in other industries to the same effect.
Ultimately, automation brings tremendous benefits for us all in the long term and hurts certain sectors in the short term. It’s how creative destruction works. We can be sure it will be no different in retail banking.