Financial services firms need robust systems and processes to maintain their predictive analytics assets.
In order to maintain quality at the point of design, company officials must monitor the performance of predictive model assets. As predictive models are used in the field, they begin to age and lose their efficacy.
Moreover, bank performance is judged based on the robustness of model practices and principles—which means it is critical to implement repeatable business processes for model development and deployment.
But how can you tell which of your models work effectively, and which are subject to considerable risk?
Businesses must employ strong practices for predictive analytics asset inventory, process management, and performance measurement. Through this process of active monitoring, product innovators will gain important insights about these predictive models that lead to innovation and re-design, then to development of new products.
This begins with the development of a strategic roadmap for the analytics model factory.
In this RedPaper, Corios President Robin Way leads an in-depth discussion around:
- Turning your predictive analytics into a thriving model factory – Learn how to close the loop: from model creation, to performance monitoring, to problem and opportunity identification and subsequent model improvement… all in a formal and disciplined way.
- The impact of individuals on model performance – Discuss the roles, skills, and teams of people in the organization who design, test, review, and monitor predictive analytics assets.
- Business processes necessary to manage predictive analytics assets – Analytically mature organizations tend to require many of the same processes. Read about the standard templates for conducting these processes to aid in productivity and repeatability.
- Best practices for model development and deployment – Explore processes and practices related to model inventories, model components, model attributes, and model change management.
- How to measure model performance – Learn the importance of actively monitoring the performance of predictive analytics assets, and the components of this process.
- Avoidance of risks related to governance, regulation & compliance – Organizations need to have well-maintained processes and systems that provide transparency and rapid access to analytics assets and their attributes. Discover the Corios-recommended practices for supporting GRC objectives.