There is an absolute need for financial institutions to deploy a fast and effective process to put models in place that can accurately measure and control risk, proactively detect and prevent fraud, and effectively evaluate capital reserve adequacy. Failure to adopt an effective process for risk modeling is not just costly, it can also be catastrophic to a firm’s financial condition and can lead to serious penalties.
Increasingly, time is the enemy. Whereas it used to be acceptable to update risk models on an annual basis, regulatory regimes are now pushing banks towards monthly updates, and business owners need models to be continuously updated to reflect current conditions. But with current processes, most banks measure the time to develop, validate and roll out models in months or quarters. Banks need a new approach that will compress the time to build a model from thousands of hours down to tens of minutes.
Costs are also a serious concern. For many banks, the current process of creating a single model is manual and iterative and costs hundreds of thousands to millions of dollars. There simply aren’t enough data scientists in the world to keep up, and even the largest banks can’t afford to spend a million dollars or more each time a risk model needs to be created or updated. An industrialized, linear and predictable process that drives costs down to just hundreds of dollars per model is required.
Collaboration between business people and analysts is critical. Many banks suffer from siloed processes, where business people and analysts have no formalized way to work together collaboratively through the modeling process. The resulting “throw it over the wall” approach leads to time consuming and wasteful iteration by analysts, and a lack of understanding of or buy-in for the resulting models by line of business and/or regulators. What is needed is a fast and interactive process where business people and analysts work together side by side, quickly developing and validating risk models together.
Ayasdi lets you deploy the power of artificial intelligence to transform your risk management processes.
Most approaches to risk model development are dependent on scarce and expensive modeling resources, and the process can be extremely time-consuming. Typically, quantitative analysts use a guess-and-check method to identify the variables for inclusion in risk models. However, with a growing universe of variables to consider, systematically exploring all the data to produce the most predictive and useful model is becoming extremely challenging. It requires extensive iteration with the business to arrive at statistically valid models that are easy to defend. With Ayasdi Machine Intelligence, you start with all of the available variables. The software draws on innovations in topological data analysis (TDA) and machine learning to analyze thousands of variables simultaneously. It quickly surfaces combinations of features that are highly correlated with risk. It lets business owners easily weigh in on the variables for inclusion in the models speeding the development of defensible risk models. For example, a major bank used Ayasdi to shorten the process of developing of their models for CCAR from three months to two weeks.
Unlike conventional machine learning techniques, Ayasdi’s machine intelligence software was designed from the ground up to help rapidly analyze highly complex data sets and uncover combinations of factors that drive more accurate risk models. Current approaches using spreadsheets, statistical tools, and standard machine learning techniques can result in overfit models as they attempt to describe all of the underlying data. Ayasdi’s software automatically surfaces groups of statistically valid variables that are highly correlated with risk that can then be incorporated into models.
The use of “black box” approaches like spreadsheets and conventional machine learning techniques typically leave little room and time for business managers to weigh in on the logic behind the choice of variables used in risk model construction. The business managers are not in a position to confidently defend the models to regulators or feel comfortable that these models reliably capture the complex relationships between a bank’s specific business activities and its risk exposures. Ayasdi supports a risk model development process that combines machine intelligence with business intuition to deliver simple risk models that encode business logic. The software automatically surfaces statistically ranked variables that are highly correlated with risk that the business managers can evaluate and approve for inclusion in the model construction process. It automatically whittles down hundreds of combinations of models to a select group of statistically valid and empirically sound candidate models that the business managers can then select from and confidently include in their regulatory filings or use to forecast risk.
With increasing business complexity, it is both challenging and expensive to build risk models across the organization. Each line of business within a bank requires specific and unique risk models. Ayasdi Machine Intelligence can serve as the foundation for rapidly building accurate, sophisticated risk models without extensive manual iterations.
Ayasdi Machine Intelligence significantly reduces the expensive iterations required to produce accurate risk models. Unlike “black box” machine learning techniques, Ayasdi Machine Intelligence provides business managers with the opportunity to weigh in on the choice of variables and the risk models selected. The Ayasdi approach results in simple models that incorporate business logic that the business managers can confidently defend.
Ayasdi Machine Intelligence equips quantitative analysts and risk modelers with a systematic way of exploring all the data to produce accurate risk models. It reduces the possibility of producing overfit models.
IT infrastructure teams benefit by using the Ayasdi Machine Intelligence platform as the foundation for developing and deploying hundreds of intelligent applications across the bank.
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