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Guest post from Andrew Aziz, Director, Financial Engineering, Research and On-Cloud Solution, IBM Risk Analytics
While glued to CNN over the past week my attention was captured by Prudential's recent "Age Stickers" ad campaign, which highlights the risks associated with managing personal wealth for retirement. What intrigues me about this ad is that it illustrates a remarkable advancement in the sophistication of how companies are dialoguing with individual investors.
Check out the commercial here:
Beyond just the fact that the concept of risk is being depicted in a clever and easily interpretable manner, it is equally noteworthy that viewers of this advertisement are being exposed to the sophisticated concept of probability distributions. Prudential, in this ad, has demonstrated an effective ability to articulate a complex concept and make it understandable so that the average investor gains insight.
One of the compelling aspects of IBM's Smarter Analytics approach is the description of the transformation process from "Data --> Insights --> Outcomes." What resonates most with me in this description is the significance of the "Insights" step. There is a deliberate reason why the process is not described as merely a transformation from "Data --> Outcomes." If it were, then all we'd be concerned with is developing the best transformation algorithms and the associated technology to implement them. The focus would be limited to simply defining these quantitative or qualitative relationships, and then producing the tools that mechanically transform one to the other... data to outcomes.
The notion of "Insight", however, defines a crucial intermediate step. The Wikipedia definition of insight is " the understanding of a specific cause and effect in a specific context." The word understanding implies a human dimension - an interpretative dimension. The inference then is that outcomes are determined by individuals, and that individuals, therefore, must gain some understanding of the meaning behind the vast amount of data (structured and unstructured) before they can undertake a set of actions that generate outcomes. It is these insights that provide a framework for comparison and enable humans to make judgements as to the appropriate actions that will increase the likelihood of achieving their desired outcomes.
However, insights can only be achieved when there is a convergence between the form of information that the data has been transformed into and the ability for humans to digest this information ... to create meaning out of it. Data transformed into something that isn't relevant and interpretable by humans is nothing more than.... just more data. It is only when this information can be understood and meaning attached to it, that it can be called an insight. This is why we call it "smarter" analytics. We have seen an evolution in the ways that analytics has been able to transform data into relevant information but, equally important, we have seen an evolution in the ways that humans are able to interpret this information.
In no area do these observations apply more aptly than in the area of financial risk analytics. For over twenty years, financial institutions have invested billions of dollars in developing methodologies and technologies to produce risk information - information required by regulators, but also used in running their businesses more effectively. This has long been the domain of quantitative professionals and academics.... financial engineers and actuaries with deep domain knowledge in applied mathematics... the so called "armies of PhDs" or the "Rocket Scientists" of Wall Street.
In the risk analytics context, the transformation process from data to information begins with market data and then, by applying techniques that lean heavily on stochastic calculus, probability theory and statistics, produces measures such as Value-at Risk (VaR) and Expected Shortfall ......measures typically interpreted and actioned on by the very same financial risk professionals...... a tiny sliver of the population at large. (It is another topic of conversation, of course, as to how effective these insights have proven to be in determining appropriate outcomes).
What is clear today, though, is that financial risk management is pertinent to a much broader population than just financial institutions and their risk teams. Every individual has exposure to financial risk and should be concerned about it, most notably as they manage their own personal wealth through time. A challenge, however, is that the type of information that might provide meaning to a quantitative risk professional may not necessary provide any insight to the average investor. A measure such as VaR cannot provide any insight if the investor cannot attach any meaning to it. In order for risk information to effect desired outcomes, it is therefore critical that risk analytics tools produce the appropriate information for the appropriate context.
Fortunately, financial planners and on-line retirement tools have evolved significantly in the way that they now incorporate the concepts of risk into their analyses, borrowing principles from sophisticated risk management, but articulating them in a manner more digestible to their client base... sometimes using analogies from other more familiar disciplines such as weather forecasting. At the same time, and not unrelated to these efforts, there is growing evidence that the average investor is becoming more savvy around their understanding of risk. Where once it was assumed that investors only understood the concept of ex-post performance, investors now appear increasingly comfortable with the insights provided by measures that incorporate probabilities of future outcomes, and will take actions that can effect those probabilities.
A convergence is therefore occurring and the Prudential advertising campaign is a great example of this. Despite how impressively the concept of risk has been articulated to the audience, there would be no insight gained, and hence no convergence, unless equally important, the viewers of the advertisement were receptive to interpreting information by visualizing a portfolio distribution.
It is only when average investors gain better insight into risk, that they can become better equipped to take actions that will increase the likelihood of desirable outcomes. And if this is the case then, clever example aside, it becomes irrelevant to the average investor whether the information underlying the probability distribution is produced by "age stickers" or by much more advanced actuarial techniques.
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