Tag Archives for artificial intelligence

New Benchmine Measure to Compare Investment Returns of Employer 401(k) Plans

Benchmine.com, provided by OnlyBoth, launched in late November a free, open-to-all website for comparing the performance of 55,000+ employer 401(k) plans, offering many novel analytic capabilities to users.

The federal data source (Department of Labor EBSA) reports many core measures of 401(k) plans, leaving it to users of the data to introduce derived measures that better enable broad performance comparison across plans of different sizes. This led to our incorporating total administrative expense ratio, defined as total administrative expenses (Line 2i(5)) divided by total assets (Line 1f), times 100. Note: all data sources referenced here are from Schedule H.

The Benchmine 401(k) engines needed a measure of plan-year investment returns that likewise enables fair comparison. The federal data reports on total income and net income during a plan year, but these include employer and participant contributions and rollovers, which tend to skew the returns on investment. Also, plans sometimes transfer investment assets out of, or into, the plan, which also complicates fair comparison.

After consulting 401(k) industry experts, we decided on a new measure yield on beginning-of-plan-year total assets (yield for short), defined as net earnings on investments (the sum of the 10 column (b) entries from section 2b minus investment advisory and management fees from Line 2i(3)) divided by total assets at the beginning of the plan year (Line 1f(a)), times 100. By itself, this doesn’t deal with the complications of mid-year asset transfers (section 2l), so we added a qualifying criterion that a plan’s asset transfers (incoming + outgoing) be less than 1% of its total assets at the beginning of the plan year. This prerequisite disqualifies about 5% of the 55,788 401(k) plans at Benchmine, which then get a value of N/A for their yield.

These three CY 2021 examples of employer 401(k) plans (names omitted here), from different total-assets brackets, stand out on their joint yield and administrative expenses:

  • Only PLAN (within the $10M-$50M bracket) has both such a high total administrative expense ratio (1.716%) and such a low yield (11.70%).
  • In California with its 209 ($250M-$1B) plans, only PLAN has both such a high yield (18.31%) and such a low total administrative expense ratio (0.002%).
  • PLAN has the highest total administrative expense ratio (0.360%) among the 196 ($100M-$250M) plans that have 1,000 to 4,999 total participants and have at least a 16.66% yield.

In conclusion, Benchmine is now equipped with good measures for both administrative expenses and investment returns, all in the service of enabling fair comparison, heightening performance transparency, helping to drive improvement, and empowering participant choices.

Raul Valdes-Perez

Enter the Benchmarking Engine!

OnlyBoth was founded in March 2014 based on technology that answered a new question about data, never before posed computationally: What’s unusual or exceptional about a given entity, compared to all its peers?  The technology’s origins were in research carried out at Carnegie Mellon University in the late 90s, sponsored by the National Science Foundation under a research grant to one of OnlyBoth’s co-founders.  The technology was set aside for 12+ years while the co-founders worked together at Vivisimo, which was also founded on technology first developed at Carnegie Mellon.  After IBM’s acquisition of Vivisimo, Lessa and Valdes-Perez got together again to commercialize OnlyBoth’s founding technology.

But first there was a puzzle to solve.  The original work was a classic example of curiosity-driven research, in which the researcher often asks “Can this be done?” after first getting an idea of a novel “this”.  The story in this case is told here.  If the answer is “Yes, it can be done and here’s how.” then the next puzzle is how to convert this into an innovation that serves a need or creates an opportunity.

For the last year, we at OnlyBoth have been trying to identify how this technology best meets a human need or enables new accomplishments.  There was no single aha! moment, but instead a gradual realization that the underlying technology fit the goals of benchmarking in the business world.

To understand benchmarking’s goals, we had to understand the questions that benchmarking seeks to answer.  After much reading and thinking, we settled on these core benchmarking questions, which we have rephrased for brevity:

  1. How are we doing?
  2. Where could we improve?
  3. What’s best in class? (peers may remain anonymous)

It turns out that OnlyBoth’s core technology is uniquely suited to answering these questions.  But that’s only half the battle.  The other half is: “Does benchmarking need improvement?”  Our research revealed to us that it clearly does. Although benchmarking has laudable goals, it has a spotty reputation (e.g., see this Harvard Business Review article) because of multiple flaws, partly due to a lack of automation, and partly due to other circumstances that could be cured by moving to a more-promising playing field.

Our next post will examines these flaws and how software automation, based on artificial intelligence and algorithm design, removes them. Read here for a preview.

In view of this breakthrough, which matches a novel, unique technology with a business practice sorely in need of software automation, as of today we are introducing the novel concept, backed by mature technology, of a Benchmarking Engine and demonstrating its application openly to public data on all 4,813 U.S. hospitals as made available at the Hospital Compare website at Medicare.gov.

Going forward, our mission at OnlyBoth is now this:  Universal betterment through automated benchmarking.

Raul Valdes-Perez