How to Integrate Risk Models into Risk Decisioning Processes without Wasting Time and Money

February 9, 2016

Author: Anjali Joglekar

Companies invest lots of time and money developing risk models to figure out which customers are the best bets for loans and credit. Operationalizing these models, developed in tools like Excel, SAS, Python, and R, in risk decisioning processes often turns out to be really hard. This is especially true with complex models built in R. Lots of risk decisioning ‘solutions’ demand that you manually translate the R model (or any other model that you are using) into code that it can understand. You need high-priced programming resources and lots of time to connect the R model to the risk decisioning process.

It’s much more efficient to use a risk decisioning solution which is model-agnostic – in other words, a solution that doesn’t care how the model is constructed. The Provenir Risk Decisioning Platform is a great example of this model-agnostic approach. With this, models developed in a variety of tools can easily be imported, mapped and validated using simple wizards. Provenir automatically generates a list of the data fields; all users have to do is pick the data Provenir needs to send to the model, as well as the data the model should send back to Provenir to drive the decisioning. This entire process takes just a few minutes, which means you not only gain an effective way to maximize the value of your models, but can also instantly adapt risk decisioning processes whenever a model changes.  And it sure saves you a lot of time and money.

Operationalize Risk Models in minutes.

[if lte IE 8]
[if lte IE 8]
[if lte IE 8]
[if lte IE 8]