A few months after I was thrown head-first into the weird, wonky world of structured finance as a data viz/data experience designer for dv01, I was tasked with designing and building a “securitization explorer” that would enable our users to easily peruse deals in the U.S. Consumer Unsecured debt space.

I knew only of the existence notorious of Mortgage Backed Securities, courtesy of the thousands of articles and books produced by journalists post financial crisis — and that was the extent of my knowledge of securitizations at the time.

I was lucky to be working with Frank Deutschmann, an industry veteran and then-head of product at dv01, who was all too willing to spend the hours needed to explain to me in very plain terms what I needed to know about securitizations.

My version of “Feynmanning”

I came across this excellent Richard’s Feynman quote on Slate Star Codex, in which he explains how his brain works:

I had a scheme, which I still use today when somebody is explaining something that I’m trying to understand: I keep making up examples. For instance, the mathematicians would come in with terrific theorem, and they’re all excited. As they’re telling me the conditions of the theorem, I construct something which fits all the conditions. You know, you have a set (one ball) -disjoint (two balls). Then the balls turn colors, grow hairs, or whatever, in my head as they put more conditions on. Finally they state the theorem, which is some dumb thing about the ball which isn’t true for my hairy green ball thing, so I say, “False!”

As SSC’s Scott Alexander noted, Feynman “was also good at using his non-mathematical intuitions to back up his mathematical genius.”

This technique of building a mental model to debunk a bad hypothesis or confirm a good one is often referred to as “Feynmanning”. My thought process for when I’m designing a new data visualization form is very similar.

When I am presented with information about how something works, I start with the simplest representation of the core concept and improvise add-ons as complexities emerge. As new information presents itself, I modify my viz to fit the new criteria. I test it to see if it fulfills all requirements as I go — does it still work in all possible realities within the limits of the criteria presented?

Rinse and repeat, until I have a visualization that fits all criteria and as many realities (i.e. real data, ideally) as I am able to test it against.

What is a securitization?

(Feel free to skip ahead to the next section if you are already familiar with this structured finance concept, this is a brief intro for non-finance folks.)

FiMarkets defines securitizations rather succinctly:

Securitization is a financial arrangement that consists of issuing securities that are backed by a pool of assets, in most cases debt. The underlying assets are “transformed” into securities, hence the expression “securitization.”

On one side of every securitization lie the assets, on the other side are the securities — the liabilities.

The assets are consumer debt — online personal loans, auto loans, mortgages.

When investors buy a part of a securitization they are essentially laying claim to some of money that debtors are due to repay in principal and interest. These claims on the money to be repaid form the liabilities i.e. securities side of a securitization.

Assets ALWAYS support liabilities: this is the fundamental accounting equation that allows one to judge just how risky a securitization is.

Very simply:

If assets > liabilities = good.

If assets < liabilities = bad.