Cloud Clarity vs. Shadow Banking

CAN THE BURGEONING INTEGRATED FABRICS OF WEB 3.0 HELP GENERATE A BROAD, SUPER-RESILIENT TRANSPARENCY THAT SAVES INT’L BANKING SYSTEMS?

The United States is facing what some experts are calling an “economic perfect storm“, with historical economists worrying about symptoms and reactions “not seen since the Great Depression”. Resources (natural and financial) are increasingly scarce, strained by tight credit markets and by competition from major emerging economies (China and India), and food prices are soaring.

One of the most serious aspects of the current crisis is tied to the widening deficit in the credibility of major financial institutions. The New York Times, for instance, is reporting:

The Federal Reserve not only [has taken] action unprecedented since the Great Depression — by lending money directly to major investment banks — but also has put taxpayers on the hook for billions of dollars in questionable trades these same bankers made when the good times were rolling.

“Bear Stearns has made it obvious that things have gone too far,” says Mr. Gross, who plans to use some of his cash to bargain-shop. “The investment community has morphed into something beyond banks and something beyond regulation. We call it the shadow banking system.”

The lesson may be that we have a problem of endemic manipulations, permitted by current regulatory and technological standards. What if it were possible to build into international banking institutions —including but not limited to the manner and volume of transactions engaged in by investment banks— a transparency-insuring mechanism based on the dispersed computing power of an integrated web-based “cloud” matrix?

While a primary function of Web 3.0 must be the innovative enhancement of privacy safeguards, it should become increasingly possible to create broad-spectrum data-screening aggregator applications that allow for the creation of a banking process safeguard fabric. Multiple unique and even competing software analysis platforms could work on the same expansive datasets to help prevent dangerous overdependence on excessively volatile market trends.