Predicting and Preventing Default Payments With AI

Use Cases

With billions of dollars in default payments every year, a new approach to loan default prediction and prevention is needed.

Predicting default payments can help improve risk management systems and establish failure prevention mechanisms. This can help lenders select better borrowers, and help borrowers make payments.

However, to actually achieve those benefits, these predictions need to be deployed in the real world. In this guide, we’ll explore how to deploy our model via Zapier, a no-code automation tool.

1. Building a default payments prediction model

To start, simply make a Pro account on Obviously.AI — you get a 14-day free trial, with no credit card needed to sign up.

Once you log-in, you’ll see a dashboard with an “Add Dataset” icon on the left. For this example, upload a simple tabular dataset from Kaggle, which features 25 attributes for 30,000 clients.

Our goal is to predict a KPI called default.payment.next.month, which is a value indicating if the client defaulted (1) or not (0).

After clicking to upload a CSV file, you’ll need to verify the dataset, and simply check off that the first row is the column names and that all requirements are met.

Now, we select the column we want to predict, which is called “default.payment.next.month.” In the background, a series of machine learning models compete to create the most accurate predictions for default.payment.next.month.

And we’re done! Now, we can also see how various attributes impact the default payments KPI. The main drivers discovered by the automated machine learning models were PAY_0 and PAY_2, which are described as repayment statuses in two given months.

Let’s move on to building a Zapier sequence, so you can deploy predictions in the real world, and predict default payments on new data.

2. Building a Zapier Flow

Now, make a free Zapier account, if you haven’t already. We’ll start by connecting Zapier to a dataset in Google Sheets. We’ll select “New Spreadsheet Row in Google Sheets” as the trigger to activate a Zapier sequence.

After selecting that trigger, I simply located my demo Google Sheet, which I titled “New Data.”

This sheet should have the exact same column names and data types as the file I used to make the predictive model (minus the actual target column of “default.payment.next.month”).

For instance, since my training data had columns like LIMIT_BAL and EDUCATION, and so on, I use those exact columns in this Google Sheet.

Next, we’ll select “Webhook,” and then “Custom Request.” This is how we’ll integrate with Obviously AI.

To set up the Custom Request, add in these details:

The data field is the most important, which requires matching up the column names in your data with the column names used in your model. Check the documentation if you’re unsure!

To set it all up, you’ll also need your Report ID and API key. Your API key can be found in your account, which gets added as a “Header” in Zapier. To get the Report ID, head to the “Export Predictions” tab in Obviously AI, and click on the shareable report link.

The Report ID is the ID you see in that URL. In the example above, it’s the string “cff9...1fd7.” As you can see in the documentation, this gets added in the bottom of the “Data” field in Zapier.

Now, we can test our Zap, which sends data from our new data sheet to our default payments prediction model, and we’ll get the default payments probability as an output. With these predictions in Zapier, we can now do anything we want with them: Send them to a Slack channel, send an email to our team, add the prediction to our sheet, or anything else.

For now, let’s just update our sheet with the estimated default payments. This can be done with the action titled “Update Spreadsheet Row in Google Sheets.”

Then, simply add the probability created in the aforementioned webhook.

Now, let’s take it a step further. We can create a “filter” in Zapier to send an email notification when the predicted default is above a threshold that we set. I set up a filter that gets activated when the default payment probability is higher than .7.

If the default payment probability is higher than .7, we can automatically send a notification email so that we’re aware of the situation.

Alternatively, we could send financial resources to clients at risk of defaulting, in the hopes of raising financial awareness.

Summary

Zapier is a powerful way to automate practically anything in your workflow. With Obviously AI, you can integrate the power of machine learning to create AI-driven workflows, including to predict default payments and help improve risk management systems and establish failure prevention mechanisms.

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