Here is my article along with Shanker Ramamurthy that was published last Thursday in the American Banker, discussing IBM's Watson super computer system's potential applications in the financial services industry - helping to advance industry thinking on how in the era of "big data" only advanced non-linear analytics can make sense of structured and unstructured data flows to transform it into valuable insights.
VIEWPOINT: New Computer, New Modeling Possibilities
By Shanker Ramamurthy and Constantin Gurdgiev
February 10 , 2011 - p8
Next Monday a new IBM computer system called Watson will battle two quiz-show champions in a game of Jeopardy! There is more at stake here than winning a game. The potential applications of this technology to transform the operations of industries such as health care, government and finance are enormous.
In the financial services industry, integrated risk management is an everyday struggle. Financial practitioners and supervisory and regulatory authorities must make split-second decisions using information coming from all sides: the Internet to corporate and call center channels.
The challenge is to efficiently process diverse data streams and pick out relevant data insights to apply to strategic business and regulatory decisions.
In the banking industry today, data "fuzziness" abounds. Uncertainty exists about the quality of data, assumptions and models that are being used to make judgments. This, of course, clouds the true picture of risk and biases our decision-making, often in econometrically undetectable ways.
Most banks today run risk models on a discrete and disaggregated basis while relying on often subjective assumptions. High-performance computing advances, represented by Watson's capabilities, can rectify this - by providing visibility into concentrations of risks and risk-related activities, as they happen. Simultaneously, it deploys nonlinear analytics in selecting both the statistically and operationally important scenarios.
The beauty of a nonlinear computer that "learns" is that it can analyze a complex set of implied possible scenarios and give answers to the broadest set of questions. This potentially can lead to the emergence of analytical systems that not only report on probabilistically likely events but also identify latent "Black Swan" events and even sense deeper levels of uncertainty.
For example, a legislative decision altering a specific set of financial strategies can have no impact on traditional linear models because the outcomes can be weighted by an extremely low assigned or assumed probability. But in a nonlinear world, such an outcome can still be testable as part of the selection list for reporting. More importantly, it can be made recognizable by the analytic system and, therefore, objectively reportable.
A system like Watson has the potential to get answers to incredibly difficult questions about strategic decisions, risks and market changes that can otherwise be elusive.
For example, it has the ability to create an interactive risk-pricing system using a menu of models that evolve as the system learns, detecting structural breaks in data before analysts can spot them and build them into existing programs.
Of even more significance, Watson will be able to deliver scenario analysis based not just on either event probability or expected loss/gain but also on more complex company objectives.
This can involve analyzing corporate strategy inputs, including non-quantifiable questions, alongside fully quantifiable inputs. Imagine asking a computer "How do I increase my loan book profit margin by 10%?" or "What actions can I take to strengthen my capital reserves, with minimum impact to my asset base?"
At a much deeper level, the nonlinear learning capabilities that Watson pioneers can lead to the creation of systems that are able not only to handle traditional risks and their interactions but also to evolve into systems capable of transforming deep uncertainty into explicit models. Though still some years away, this could mean an artificial intelligence able to sense Donald Rumsfeld's famous "unknown unknowns," converting them into specific models suitable for risk analysis and getting meaningful, actionable responses.
The real-time, decision-making capability that is so sought after in the financial industry will be a crucial, competitive differentiator.
As risk intensifies within interconnected global markets, the complexity and exploding volumes of data will only rise.
Shanker Ramamurthy is the general manager of banking and financial markets at IBM Corp. Dr Constantin Gurdgiev is the head of macroeconomics in the Center for Economic Analysis at the IBM Institute for Business Value.
I am by no means a technophobe, but I am rather skeptical of such advancements in terms of competitive advantages. Why?
ReplyDeleteHigh Frequency trading would be one example. Sniper, Guerilla, dark pools, etc are the terms used to describe functions and programs that operate here, to me it clearly displays the true nature of such operations, it is warfare of a special kind. Nano seconds latencies are of concern to the people developing such trading platforms, in this world it matters if you can gain a few hundred nano seconds.
So, of course, it is all about speed here, but is it progress? Debatable. Watson and other projects are designed to outperform human capabilities in decision making and risk analysis, but can something like common sense be programmed? Perhaps. However, we have seen how HFT was and still is abused to gain what I would call unfair profits.
Our social skills have not evolved in equal terms compared to our technological possibilities, and herein lies a great risk itself. Global financial systems are on the brink of a total collapse, despite all the technology we have available.
Despite all our advances, the future remains unpredictable, and this is a good thing.
I find that whole subject so very interesting, hence I am tempted to add something. If I am not mistaken, Watson has currently something like 200 million pages of pure knowledge available, steadily growing I suppose.
ReplyDeleteKnowledge on it's own merit is a useless commodity, only by utilizing the knowledge we can turn it into wisdom, and the latter is the key differentiator to the way a human can operate, utilizing knowledge and learning from experience.
Watson already is programmed to 'learn from his errors', but per se it is a giant search engine who's search results are determined by statistical factors that are preprogrammed.
Creativity and Intuition, to name but a few are a long way from being part of any artificial Intelligence, and perhaps, it is a wrong assumption that we will be able to implement such qualities in a machine as we know it today. Perhaps, we need to accept that Life itself is the only force that enables such qualities. Self awareness, creativity, intuition, last but not least ethics are qualities that can not exist in a purely statistical world.
However, on a slightly ironic note, even a early Version of artificial Intelligence such as Watson would have undoubtedly called for a Egypt Style Revolution in Ireland as the only Solution once he was made aware what decision DOF and other officials imposed on the coming generations. (grins)
GIGO
ReplyDeleteI learnt that 40 years ago!