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Seeing the full credit risk picture using external data sources

//Seeing the full credit risk picture using external data sources

BeeEye Blog

Seeing the full credit risk picture using external data sources

Seeing the full credit risk picture using external data sources

There is an old parable about a group of blind men who have never seen an elephant before. When they first encounter one, each one feels a different part of it, like the trunk or the ears. Each one in turn describes the beast in a completely different way. There are several lessons that can be learned from this fable. What I take from it is to always remember to see the bigger picture.  To always ask “what am I missing?” and include all the data points.

This is especially true when it comes to credit risk modelling, and the use of varied data sources. If you only rely on internal data sources, you’re definitely not seeing the full picture. The result? You are either exposing yourself to more risk than intended, or are leaving money on the table. 

Using the right sources, the right way

You must have full control on which data points and data sources are in use, as the GDPR and similar regulations require it. In a platform where all external data controllers are at the hands of the modeling team, unique value can be uncovered while keeping the models’ transparency. This way the stakeholders that monitor data governance have full visibility into the data sources used. 

Examples of sources that are especially beneficial are: Applicants’ email quality, data derived from a 3rd party partner retailer API and governmental API with local addresses data. By adding these to the mix, you’ll improve risk scoring and customer knowledge. 

So, why isn’t everyone using alternative/external data sources to complete the picture for them? Well, first and foremost, because many credit risk modelling platforms don’t lend themselves to an easy integration with these sources. Another reason is that when using additional data sources, modelers must also have a detailed model explainability report. This is important so decision makers can assess both the exposure and the lift impact new data points add. Then they can use them with discretion to achieve the optimal data mix.

The key features to look for

Here’s what you’ll gain from working with a platform that can automatically integrate external data sources to your workflow: 

  • Automatic integrations will speed up your ability to see the full picture.
  • A central platform that manages all of your external sources’ APIs will save you money as several users on your team will be able to use the same source without paying for it separately.
  • A no- hassle process around the API calling code: The platform will do the work for you.
  • Easily test the powerful impact of various data sources with the ability to easily include or exclude the various data points.

When you’re using a platform that is specifically built around your processes and needs, everything makes more sense. Processes that are key to your workflow are automated, no coding is required, and you can focus on what you do best: manage your credit risk models. 

Check out the EyeOnRisk platform – a highly automated, built for credit risk modelling platform that allows for an easy integration of external data sources. 

 

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