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Operationalize your Financial Risk Models

DATA SHEET

Operationalize your Financial Risk Models

Quickly and easily import multiple types of risk models

The Provenir decisioning Platform and risk analytics is unique in its ability to quickly and easily import multiple types of risk models so that they can be operationalized in automated decisioning processes.

Because the integration is “model agnostic”, simple and complex models and scorecards developed in third-party tools such as SAS, R and Excel or exported using PMML or MathML can be imported, validated and mapped via easy-to-use wizards.

With Provenir, there is no need for programming, enabling models to be imported and operationalized in minutes. This exclusive capability extends the value of your investment in industry-standard modeling tools while ensuring that automated risk decisioning processes developed in Provenir are always using the most accurate and up-to-date risk models.

Here’s a quick summary that shows just how easy it is to operationalize an R model.

Simply click Add to select a model from your system files and import it into Provenir. Provenir automatically validates the model’s code and extracts the fields for mapping.

Open the model to visually configure the input and output mapping by dragging lines between the model’s and Provenir’s data fields. This creates Execution Profiles.

Input mapping specifies the data from Provenir that will be used by the model to execute the analysis.
Output mapping specifies the data to be returned to Provenir once the model has executed.
Provenir also provides options for managing the Execution Profile including how a returned value should be handled as well as using more than one Execution Profile with a model.

Provenir dynamically maps between the R model and its decisioning process. You can also map constant or fixed values between the model and Provenir.

Finally, check in your new model object and you are ready to test it. You can test it directly from the model object itself or place it in a business logic process and test it. Provenir provides visual feedback to show you exactly what happened during the test.

With these few simple steps, your R model has been operationalized in an automated Provenir risk decisioning process.

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CRO’s Guide to Machine Learning

WHITEPAPER

CRO’s Guide to Machine Learning

Speak ‘Machine Learning’

As Chief Risk Officer, your job is not to know the ins and outs of Machine Learning. Your job is to responsibly manage risk.

However, according to McKinsey, Machine Learning “improves the accuracy of risk models by identifying complex, nonlinear patterns in large data sets” and it is one of the top trends in risk management1.  So, you should probably understand the basics.

This paper guides you through the technical foundation of Machine Learning — not why you should leverage it’s power, but how it works. You’ll gather key talking points to kickstart ML initiatives in your organization.

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Provenir Unveils Amazon Machine Learning Adapter

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Provenir Unveils Amazon Machine Learning Adapter

New Provenir Adapter adds Machine Learning to the risk analytics and decisioning toolkit for faster credit and risk decisions

New Jersey, US – 20 March 2017Provenir, provider of risk analytics and decisioning solutions, today unveiled the new Provenir Adapter for Amazon Machine Learning for financial institutions seeking to add machine learning to their toolkit without the time and resources typically associated with development. The latest in Provenir’s suite of adapters automatically feeds the predictive score returned by the Amazon Machine Learning model into the risk decisioning process. The Provenir Platform then automates that process, instantly executing a pass, fail or refer result from a risk score.

Provenir Adapter Makes Machine Learning More Accessible to Lenders
“While Machine Learning has massive implications in financial services, few organisations are able to dedicate full time resources to the development of homegrown machine learning capabilities” said Larry Smith, CEO and Founder, Provenir. “The Provenir Adapter for Amazon Machine Learning makes machine learning accessible to those companies who are focused on their core businesses by eliminating the need for a dedicated expert.”

Inexperienced Developer Deploys ML Model in Less Than 20 Minutes
Smith shared an example. “With no experience in machine learning, one developer on our team was able to build and deploy a machine learning model in less than 20 minutes, operationalizing that model in the Provenir Platform in just hours. Historically, that would have taken a team of people a month to achieve.”

Visually Configurable, Real-Time, Risk Analytics
To meet customer expectations of speed, efficiency and cost, financial service providers need risk management solutions that are visual and intuitive, don’t require coding expertise or development skills to update, and are flexible enough to adapt in changing situations. This integration pairs Provenir’s visually configurable risk platform with Amazon Machine Learning for real-time, user-friendly, risk analytics and decisioning.

About Provenir
Provenir makes risk analytics faster and simpler for financial institutions. The Provenir risk analytics and decisioning platform is a powerful orchestration hub that can listen to any channel, integrate with any data service and operationalize any analytic model. Helping clients process more applications with greater efficiency and increase sales conversions with instant, real-time risk decisioning, Provenir serves clients across a broad range of financial verticals including consumer, cards, payments, ecommerce and auto financing. Provenir is headquartered in Parsippany, New Jersey with EMEA operations based in England and APAC operations in Singapore. For more information please visit www.provenir.com.  

The Ultimate Guide to Decision Engines

What is a decision engine and how does it help your business processes?

Learn More


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Continue reading