What could this quote from the "Pogo" comic strip by Walt Kelly possible have to do with analytics and risk innovation?  The battle that risk managers constantly fight is that risk analytics often brings diminishing returns.  The major components of risk analytics are data, methodology and the business problem which the analysts are attempting to solve for.  The enemy of analytic innovation is that as the usage frontiers of data and methodologies expand, the result is diminishing returns of predictive findings and solutions that can be implemented.

Risk data can be either internal or external to the organization.  Depending on where you are, the degree of regulation attached to the data may vary, but has gotten tighter over time.  The business problems are perennial and include how to efficiently reduce potential defaults and business variances.  The goal of the risk manager is to produce analytic alchemy by finding ways to weed out potential risk without taking away any economic value of the customer relationship.  Risk methodologies are a concoction of predictive algorithms from regressions to learning models to random genetic algorithms.  Some methodologies are excruciatingly deliberate while others seek to harness vast computing power to randomly stumble into an optimized solution.  Throughout the world thousands of risk analysts with advanced degrees in math and science are panning for reductions in risk through the use of extensive analytic software programming.  

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One solution to this conundrum might be execution speed.  One thing is for sure, risk defined is a variance to a desired outcome.  Risk is dynamic, ever changing.  The ability of risk managers and their analytic teams to rapidly implement models to attack a business problem adds a fourth dimension to risk analytics.  Perhaps applying the marketing strategy of "speed to the mailbox" to risk is the future of risk analytics.  Will advances in computational speed and storage abilities enable organizations to have a real time risk profile of loan portfolios?  This would require the cycle of risk data inputs to move from quarterly and monthly to daily.  Flash forward 5 years, you wake up and your mobile device lets you know that its warm out, might rain, traffic is terrible, but your morning credit score increased 10 points to 740.  Or, for the risk manager, the 3-month rolling forward outlook for losses in the portfolio increased by 10 basis points since yesterday.  In addition to the components of data, methodology and identifying the business problem being solved for, think about reducing analytic cycle time.  Hardware and software technology is here today to support these notions, especially given the evolution of cloud oriented solutions. 

For IDC Financial Insights clients, this topic is further explored within the following case study for a liquidity management solution, by Jeanne Capachin: Improving Risk Management: Implementing a New Liquidity Management Solution, Case Study Doc # FIN211798 or by following this link:  http://www.financial-insights.com/FI/getdoc.jsp?containerId=FIN211798