What insurance industry can learn from baseball?

The insurance industry is experiencing a time similar to the industrial revolution with the introduction of all these new technologies – from claims payment technology, ledger technology, quote comparison, customer acquisition and lead generation technology,  contract review, policy issuance, Peer to peer insurance, and on and on.

Everyone is looking to become the ‘Uber of Insurance’ – by disrupting an industry and eliminating wasteful costs from the system.

I applaud the efforts and am truly excited about what this new InsurTech revolution will bring to consumers.

But if we step back for a second, there may be a thing or two the insurance industry can learn from baseball.


If you haven’t read the book, Moneyball: The Art of Winning an Unfair Game by Michael Lewis or seen the Sony movie, Moneyball starring Brad Pitt and Jonah Hill –

  1. Where have you been?
  2. I recommend you spend some time read the book and then watch the movie.

Both are excellent.   They follow the low-budget Oakland Athletics, and their general manger, Billy Beane, and the group of amateur baseball enthusiasts and statisticians.

The story concerned a small group of undervalued professional baseball players and executives, many of whom had been rejected as unfit for the big leagues, who had turned themselves into one of the most successful franchises in Major League Baseball.

The book highlights the premise that the traditional yardsticks of success for players and teams were fatally flawed.

Beane paid attention to the statistics—with the second lowest payroll in baseball at his disposal he had to—and the book highlights his efforts.


Oakland’s Athletic A’s were the first team to embrace statistics and saber-metrics over traditional scouting tools alone.  It worked for the A’s in generating alpha prior to the adoption by other teams- the A’s outperformed their peers with a much lower expense base.

Their payroll was and continues to be one of the lowest in the league, and the productivity they obtain from their system works for this small market cap team.

However, as you can see below – if you used the traditional evaluation (Winning percentage) there would be some signs the A’s formula may need to be revisited.  The A’s winning percentage is slightly dropping- appearing to have a downward trend since 2000.

I believe once the competition embraced the technology (statistics based analysis) and similar formula, the A’s would appear to lose their competitive edge using the traditional evaluation tool (Winning percentage).

All the other large market cap teams are all using similar analysis and statistics based approaches (new tech), as well as the traditional scout approach (old tech-human based).  For what it’s worth, I believe the A’s are using both approaches as well.

However, please note they are still outperforming based upon Wins per $1 of payroll since 2000.  So this is not a critique of the A’s – just pointing out if you don’t adjust the evaluation metric, an analyst might notice what appears to be a slight downward trend.

Relevance to Insurance

Please give me some literary room to see if this analogy can be transferred to the insurance industry.  Old tech in this analogy for the insurance industry is the old school underwriters, brokers, agents, etc.  Human analysts – who like baseball scouts were created to help insurance companies find best in class risk/customers.  With decades of experience, industry knowledge, gut feels, and statistical inference tools (actuarial) this old tech has been able to perform well enough to allow the industry to achieve it’s strongest capitalization in history.

However, the introduction of Fintech or Insurtech (new tech)- has the industry media and experts calling for major disruption.  The new tech is posed to help lower costs and save the insurance company money on sourcing risks. (this sounds like it is a great idea & also necessary to lower costs to consumers.)

But is the advantage of Fintech/InsurTech only to the first movers – like the Oakland A’s analogy?  Once everyone starts using algorithms and machine learning to source risk, then what happens?  Does the company fail to generate alpha? Will the winning percentage (combined ratio) continue to fall?

Does the insurance company run the risk of becoming the Oakland As?  Appearing to only deliver a beta of the market using the old evaluation tools?

Market cap

Market capitalization or balance sheet is comparable to the payroll in baseball.  Larger companies historically could afford to take larger risks, have larger organizations (measured by EE’s), absorb losses better, attract new capital, etc.  As the system was evaluated by analysts with tools like combined ratio, ROE, ROC…

  • If the system evolves to machine algorithms and less human involvement, will companies stocks continue be  evaluated in the same fashion?
  • Will companies be appropriately rewarded by investors to generate alpha?
  • Will increases in net income be compared against any increase in risk adjusted return metrics?
  • Is it the best algorithm wins approach?
  • How will analysts even be able to evaluate proprietary algorithms and determine stock price?
  • or does the industry fall into all players become a beta of the insurance market….

If what happens in baseball is any indication, then the big market teams and higher market cap companies will eventually embrace the New tech, but will still rely on and can also afford the best scouts (i.e. underwriters) with the old technology.  Benefiting from both the old and new technologies.

Then, how will small/mid cap companies differentiate themselves if everyone is using the same tech, has a lower expense ratio, and low combine ratio – on larger balance sheets?

If everyone is seeing the benefit of expense savings on the combined ratio, how does anyone outperform?  What about those with increased volatility? Or those with poor risk selection?

Analysts need to evolve too

Hopefully analysts will shift their focus away from old metrics like Weighted Average Cost of capital, ROE and combined ratio (old evaluation tools), and focus on most efficient risk bearers.  A new return over volatility benchmark to be compared across insurers- risk adjusted return metrics are used for performance evaluation not necessarily by regulators or ratings agencies.

Combined ratio should becoming the Winning percentage of baseball  – valuable but not the sole determinate of success.

Meaning a number not to be used in isolation, if a company with a combined ratio of 94% – but produces this 6% of margin on low risk, low volatility and low expense and it is essentially perpetual- why would they not be considered the best company in the industry.  Rewarded with a higher multiple.

New metrics might include:

  • Risk adjusted revenue per employee
  • Risk adjusted revenue per $1 of expense
  • Earnings per volatility of risk

How to stay one step ahead

I think- and I’m sure Nate silver would agree, forecasters need to be evaluated and held accountable for their predictions as carefully as an algorithm will be held accountable for its success.   Based upon his book, The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t, he is a fan of holding people accountable for their forecasts or failures at forecasting.

This will be a trend that the insurance industry as well as it’s analysts will have to embrace, no longer are we in a world of acquiring scale for the sake of scale.

Stock prices, compensation, bonuses, etc. will be awarded to executive teams that can outperform the market by embracing the new and old technologies.  As all companies lower their expense ratios, and costs of capital –  Executives should be measured on returns over volatility of their portfolios (new evaluation tool) the same way baseball GM’s will be expected to perform per dollar of payroll.

About the author

Arnold Smith

Leave a Comment