# Golden Copy: Inevitably Incorrect ... Or Not Necessarily?

## When using data about risk to make predictions, or report accurately to regulators, how can one avoid being wrong?

Inspired by culture writer Chuck Klosterman's musings on "But What If We're Wrong?", some thoughts about how to form hypotheses about risk to better manage data

Pop culture writer Chuck Klosterman has an excellent new book out called But What If We're Wrong? in which he writes about the likelihood of conventional wisdom on topics such as music, sports and politics eventually changing to the exact opposite of what it is now. Klosterman also imagines that in the future only one or two artists in a musical genre will come to represent that entire genre—just as John Philip Sousa's work came to symbolize all marching music.

But that's just an intriguing aside. The part of Klosterman's book that is more relevant to financial industry data operations concerns the author's approach to scientific understanding. The best hypothesis to use for any kind of scientific idea, Klosterman writes, is "one that reflexively accepts its potential wrongness to begin with."

I can't imagine that regulators would be happy if firms reported their risk with the caveat that the regulators should take the figures with a grain of salt, but the industry has seen plenty of predictions over the past 10 or 20 years that turned out to be wildly inaccurate—the dotcom and housing bubbles are just two that come to mind.

Inside Reference Data's most recent in-depth look at risk compliance issues was a feature about the Fundamental Review of the Trading Book (FRTB). As we continue to cover risk data management compliance, it's good to keep in mind that any compliance plan—and there are numerous regulations addressing this issue, not just FRTB—ought to have a contingency plan for incorrect assumptions or errors being found in reporting.

If you accept Klosterman's criteria for a scientific or financial risk hypothesis, you have some newer tools for developing measures to weed out incorrect theories or assessments. These include improved linkages between data sources, and more adoption of identifiers and assignment of ever more of those identifiers.

So, when implementing those tools, the challenge for the industry is to use them to strengthen a risk assessment and stamp out what can turn out to be wrong.

#### Waters Wavelength

###### Waters Wavelength Podcast Episode 113: IBM's Lund on Blockchain's Evolution

Jesse Lund talks about real uses for DLT in the capital markets, lessons learned while rolling out IBM's blockchain platform, and what’s ahead for 2018, and into 2019.