Have you ever tried augmented reality glasses? They let you see reality, and at the same time show you additional data that’s relevant for that reality. In a nutshell, this is what data augmentation does to your credit risk models.

Data augmentation and credit risk management

We’ve written about the various ways banks are leveraging AI before. Machine learning is making credit risk modelling more accurate and allows you to draw the right conclusions from heaps of data.

Data augmentation will assist you to create the most accurate ML models. It is the process of adding more layers of (usually) external data and including it in your machine learning training set.

Many banks still base their models on a limited data set. They look at their customers using data that originated from the application requests and from data warehouse databases. Sometimes they add only FICO or other agency’s scores. This is highly limiting and hurts the models’ accuracy. The data is not utilized to the full extent in predictive analytics. Sometimes, it’s due to orthodox modeling. Other times, it’s a lack of ability to deliver complex models based on different transformations of the external data to production.

We’ve discussed the benefits of using an end-to end credit risk management platform before. The ability to test and easily get models to production saves time and improves your ability to test models. This is proving to be even more true, due to the growing need for more data points. This additional data will help you capture the changes your customers undergo. A new data point that the platform indicates has high predictive value can change the decisions you will make, and your bottom line.

The benefits of data augmentation:

New features and data sets that enable up/cross sell

A good platform will let you play with additional data points, easily test new models and send them to production. It will also recommend new features. Banks should think about their customers’ journeys and life cycles in order to match customers needs and preferences with their product and offerings.

Reduce risk and default rates

Banks that would extract the permissible regulated compliant data and interconnect it with their own data, would be able to reduce their risk. The more robust data sets will allow them to have minimal impact on their rejected rate.

Making sense of the multitude of data in your organization

Let’s assume you are already using a wide variety of data sources. You have your internal data sources, and combine them with external data sources for a fuller picture of your customers. Ask yourself: Are you really leveraging all this data? Is your credit risk management platform able to utilize this vast amount of data and create better predictions? Ensure your platform can augment the data so you have a multi-dimensional perception of your customers.

Not using external data sources yet? Read our thoughts on the importance of using them, and the reasons you should seek out a platform that can easily integrate them into your model.

In the post-COVID world, risk becomes a major problem for all. Banks, aiming above all to keep uncertainty to a minimum will need to rely much more on advanced modeling methods fueled by more data than before. Don’t stay behind – embrace ML and make the most out of your models.

The EyeOnRisk platform is an ML -based credit risk focused, end to end platform for smarter, faster risk model management. It is specifically designed to boost credit risk management processes and is built with the bank’s processes and compliance needs in mind.

The platform encapsulates the model data flow. This lets you transfer models from research to production at an unparalleled speed. At the same time, this encapsulation allows for easier external data enrichment. Close monitoring of the data sources feeding the model while tracking data skews and suggests continual improvements.

Contact us to learn more