Operationalize Models in Hours, not Weeks
Provenir empowers your team to quickly and easily import multiple types of risk models so that they can be natively operationalized in automated decisioning processes. Simple and complex models and scorecards developed with third-party tools such as Python, SAS, R and Excel or exported using PMML or MathML can be imported, validated and mapped via easy-to-use wizards.
We can have a change deployed in minutes or days instead of weeks or months.
With Provenir, there is no need for programming, enabling models to be imported and operationalized quickly. Simply upload a model from your system files and Provenir automatically extracts the fields for mapping.
See how simple it is to operationalize risk models in Provenir with this insider how-to. Download the Risk Models Guide here.
What challenges do you have deploying risk models?
Testing Your Models
Test your models directly from the model object itself or place it in a business logic process and test it without deploying to a separate test environment. Provenir provides visual feedback to show you exactly what happened during the test.
Credit Risk Modeling with Provenir
Python Risk Modeling
With advances in analytics and deep learning, it’s no wonder that Python is quickly becoming attractive in forward-thinking risk organizations. Provenir natively operationalizes Python models with support for your favorite libraries, all you need is your .py file.
Provenir data scientist talks Python for credit risk. Read the full story here.
Credit Scoring in R
Logistic Regression and Decision Trees are very popular methods as non-linear techniques are increasingly applied to credit risk data. Provenir makes deploying these, and all of your R models simple.
Risk Management with SAS
Forget converting you SAS models for decisioning, that’s just an extra step. With Provenir, you’re importing your SAS models and mapping values directly into the decisioning workflow. Simple.
Credit Scoring Models in .xls (Excel)
Excel models are exciting; weeks or months of coding to operationalize them are not. With Provenir, you can simply upload your .xls file and map the values to your decisioning workflow.
Credit Scorecard Models
For a scorecard to be effective it must be easy to use, and we’re big fans of keeping things simple. While the industry has come a long way from paper scorecards, we support the use of scorecards in your decisioning process. No coding required.
Amazon Machine Learning Models
To make the leap into machine learning and AI accessible, Provenir offers a tight integration with Amazon Machine Learning. You train your model in Amazon, then drag and drop it into your Provenir decisioning workflow. The future is here.