When was the last time you updated the credit models you use to make business credit or commercial lending decisions? If it has been a few years, you may be approving customers who are likely to default and turning away customers who are a good risk. The fact is models should be updated every year to ensure their accuracy is maintained.

Think about the economic uncertainty we find ourselves in right now and how its impact is being felt across country. The financial situations of millions of businesses have changed quickly and dramatically. But by updating your models with the most current data available, you can be reasonably assured that you are making sound and reasonable credit decisions.

Many companies today are using data-driven decisioning models that are powered by artificial intelligence (AI) within machine learning engines. These models are particularly good at predicting a customer’s future ability to repay their obligation because they use sophisticated algorithms, meaning they render much more accurate credit decisions than traditional models.

It all starts with quality data

Credit decisions have always relied on data, but this is even more true for models using machine learning. The accuracy of these models is dependent on having the necessary data to understand both present and past behavior in order to predict what will happen in the future. The more data that these models can access, the more precise the credit decision will be.

When you set out to create or revise a data-driven decisioning model, there are three things to consider:

• Accuracy — Using the right criteria (data points) is critical to getting accurate credit decisions from your model. Machine learning and AI algorithms can actually predict which criteria will help improve the model’s performance for individual customers and types of credit decisions. So, it is important to think about the different criteria that might significantly influence the accuracy of a decision.

• Quality — The quality of a credit model’s underlying data becomes even more important when the businesses that use it aim to operate faster and more efficiently. The more data that is consumed, the smarter machine learning becomes. Therefore, the quality of the data provided directly influences the accuracy of the model’s credit decisions. That is why it is important to be careful when purchasing data from third-party sources. Oftentimes it contains duplicate information, errors and missing fields of data which translate to less accurate decisions.

• Timeliness — It used to be common to see a significant gap of time between when data is collected and when it could be used. But today’s machine learning models use real-time data so you can get a current and clear assessment of a customer’s financial health and see changes in the different data points as they happen. As a result, you can more accurately predict the future credit risk of a consumer or business with a data-driven credit model than you could with a traditional credit decisioning process.

These uncertain times have created an even more pressing need for better credit decisioning and heightened risk mitigation strategies. Data-driven credit models, supported by machine learning and AI, can help you make more precise and predictive assessments to enable more accurate decisions.

To learn more about how Xactus can help you with data-driven credit modeling, contact your strategic account manager.