No-Code AI to Predict Bankruptcy

Use Cases

Now you can build machine learning models without writing a line of code. Learn how to apply no-code AI to predict corporate bankruptcy.

Predicting company bankruptcy can give early warning to investors, allowing them to better understand and manage risk. Publicly-traded companies declaring bankruptcy is a regular occurence, putting people's savings at risk. For instance, Hertz went bankrupt in 2020, causing the stock price to drop well over 90%. We'll build an AI model to predict bankruptcies ahead of time.

However, a valuable predictive tool is about more than just building AI models. We need to implement those models and predictions in the real-world. What good is a model if it’s not used? In this guide, we’ll explore how to deploy our model via a no-code automation tool called Zapier.

1. Building a bankruptcy prediction model

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

After log-in, you’ll see a dashboard with an “Add Dataset” icon on the left. For this example, we'll upload a simple tabular dataset from Kaggle, which features 6,819 companies, with 96 descriptive columns.

Our goal is to predict a KPI called “Bankrupt?,” which is the value 0 if the company did not go bankrupt, and the value 1 if the company did go bankrupt.

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 “Bankrupt_.” In the background, a series of machine learning models compete to create the most accurate predictions for bankruptcy.

And we’re done! Now, we can also see how various attributes impact the bankruptcy KPI. The main drivers discovered by the automated machine learning models were Realized Sales Gross Profit Growth Rate and revenue per person.

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

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. Essentially, we can use this to make predictions of any new company data.

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 “Bankrupt”).

For instance, since my training data had columns like ROA(A) and Operating Gross Margin, 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 “cff97990–7385–11eb-9a76-af3704ed1fd7.” 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 bankruptcy prediction model, and we’ll get the bankruptcy 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 bankruptcy. 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 bankruptcy is above a threshold that we set. I set up a filter that gets activated when the bankruptcy probability is higher than .7.

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


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 bankruptcy and give early warning to investors.

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