I Just Don't Trust You: How the Tech Community Can Reinvent Risk Ratings

“How many of you believe that Sharpe ratio is bull**? How many of you use portfolio theory, optimal portfolio, and all that cr*p?…If someone starts talking about Sharpe, VaR, and all that, throw them out of your office.” –Nassim Taleb

Financial technology – something we all thought was complete – has been upended. Fundamental assumptions have been exposed as faulty. And now we have the opportunity to recreate our finance industry from the bottom up. We have a choice: a path of openness and information sharing, or more opacity and secrecy.

We will show you the potential of an open approach to assessing corporate credit risk by supplying corporate financial information that can be openly analyzed, avoiding conflicts that have been exposed in the current market.

Historically, the financial industry has valued opacity and closed solutions, with credit rating agencies functioning in a government-regulated oligopoly, enjoying immense protection from competitors. These firms haven’t been obligated to disclose their methods, creating a powerful incentive to alter the ratings based on favoritism, coercion, and bribery, and offering few means of convincing customers otherwise.

As a result of these market failures the state-of-the-art in credit-rating generation is woefully archaic and in need of retooling; however, the difficulty in assembling the data and the infrastructure needed to experiment with these scores prevents simple experimentation by developers.

With our latest project, FreeRisk, we aggregate accurate, accredited risk data, enabling users to generate crowd-sourced algorithms to analyze credit risk and allowing anyone to view the results of these algorithms. FreeRisk aggregates both all standardized XBRL data and public-domain financial data, as well as user-generated content incorporating unstructured data released in financial reports like footnotes, critical to accurate risk assessment. This system allows credit evaluators to focus exclusively on creating and applying risk analytics, instead of working through the complex data management tasks traditionally required to solve these problems or relying on black-box credit ratings.

Systems like FreeRisk can become a catalyst for innovation in financial technology, and by enforcing the same open source principles that generated so much new value in software, we can be assured that this knowledge will be shared, audit-able, and clear. More data, freer data, and easier data solutions will enable the rapid experimentation needed to create the accurate risk assessment we’ve been missing.