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 nonquantifiable 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.
