Operationalize Models in Hours, not Weeks
When you’re pushing new products to market or digitizing risk strategies, you rely on having the right models in place to support your developing business needs.
Legacy systems and hard-coded, get to market fast solutions can make it hard for financial services organizations to deploy credit scoring models in real-time. In fact, our clients were often fighting deployment delays of weeks or even months before they implemented Provenir.
We can have a change deployed in minutes or days instead of weeks or months.
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?
Model agnostic platform for simplified deployment
The Provenir Risk Decisioning and Data Science Platform is model agnostic. So, instead of waiting for models to be recoded into a supported language, your risk team can complete credit risk modeling and deploy models in their language of choice, giving your business the power to quickly respond to changing business needs, take advantage of emerging opportunities, and rapidly mitigate new threats.
Provenir empowers your team to quickly and easily import multiple types of simple and complex models and scorecards developed with third-party tools such as Python, R, Spark, TensorFlow, SAS, and Excel, or exported using PMML or MathML. Models can be imported, validated and mapped via easy-to-use wizards and natively operationalized in automated decisioning processes.
With Provenir, there is no need for programming, enabling predictive model deployment to be completed quickly. Simply upload a model from your system files and Provenir automatically extracts the fields for mapping.
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.
Machine Learning and 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 who are exploring credit risk modeling using python. Provenir natively operationalizes Python models with support for your favorite libraries, all you need is your .py file.
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 credit risk modeling in R simple.
Spark Machine Learning
Provenir supports the integration of Spark into your business processes. Use Spark to power scalable machine learning processes that offer predictive insights, personalization opportunities and advanced problem solving.
TensorFlow offers a wide variety of options for implementing predictive analytics into your risk decisioning and business analytics processes. The Provenir Platform makes using TensorFlow models within your risk ecosystem quick and easy.
Credit Risk Management Models 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)
Credit scoring models in excel 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.