Creating Effective Risk Models Using Machine Intelligence

Article written by Ayasdi

There is a growing need for financial institutions to have sound models in place that can accurately measure and control risk, proactively detect and prevent fraud, and effectively evaluate capital reserve adequacy. Model failure can be catastrophic to a firm’s financial condition.

The constraints imposed by annual regulatory reviews and huge financial losses as a result of decisions based off of inaccurate models are leading financial institutions to reassess how they develop, validate, and update the models they use to assess credit, market, and operational risk.

Accurately assessing a bank’s risk exposure requires a deep understanding of the complex and dynamic interplay of a large number of variables and the ability to continuously incorporate these findings into its models. Conventional analytics solutions are overwhelmed by data complexity, and have reached their practical limits. There is a need for a new approach. Read more.