Simple chatbots are easier to build than ever before. If you have straightforward needs, like guiding users to a specific section of your site, gaining user feedback, or offering support, tools like Landbot make it easy to get up and running in minutes.Â
However, if you have more complex needs, like lenders predicting borrowers credit approval or analyzing user sentiment, then itâs a lot harder. Indeed, Landbot raised $2.2 million âfor its on-message âanti-AIâ chatbot.â Thereâs typically no (or very little) AI functionality in the simple, drag-and-drop chatbot builders, like Landbot, Joonbot, Wotnot, and so on.
If you want to build an Artificial Intelligence (AI) powered chatbot, then youâre faced with automation options like Googleâs DialogFlow or SAP Conversational AI, which are immensely complex and take months and lots of time reading the FAQ to figure out.
In short, there are two ends of the spectrum, without much middleground: Simple, no-code bot builders without AI functionality, and extremely complex AI-powered bot builders.
With Akkio, you can reach a nice middleground, and create feature-rich AI-powered chatbots with simple bot builders. In this hands-on guide, weâll make an AI-powered chatbot in Landbot in minutes, which will score applicants by likelihood of repayment automatically approve or reject extending credit during the application process.
First, sign up for Akkio. Then, hit âCreate New Flowâ to get started making an AI flow.
Then, click âTable,â as weâll be working with this tabular dataset of loan approval information from Kaggle. Note: Each business will want to customize their credit approval model for their business process - to account for their unique customers, credit scores, interest rates, etc.
After uploading the Kaggle dataset, youâll get an overview of the table, as seen below.Â
Now that the data is uploaded, hit âAdd Stepâ and then âPredict.â
We then simply select the column we want to predict, which is âLoan_Status.â Optionally, you can click on the âIgnoreâ tab and select âLoan_IDâ and âGender,â as these fields arenât desired for our model. Then, click âCreate Predictive Model.â
And we did it! Weâve now built an AI model that accurately predicts loan approval.
Our final step in Akkio is to deploy it, so just hit âAdd Stepâ and then âAPI.â Youâll then see a screen like the below, and you can simply click âDeployâ to finish.Â
Now, letâs sign up for Landbot, which weâll use for the chatbot functionality.
Then, click the âloan applicationâ template to get started.
This chatbot template walks users through a loan (or credit) application process, with an option to send that information over to a loan provider for manual review. We can use the Akkio AI model we just created to automate the loan approval process, and immediately give users an answer, saving huge amounts of time and money.
We want to edit the Landbot to ask users for the same information used in our predictive model. That data was:Â
Hereâs what the Landbot looks like with those questions. I simply replaced the existing blocks with questions asking that information. As you can see, it still all fits on one screen! Make sure to save each input as a variable in Landbot, so we can pass these variables to the Akkio model.
Next, we add a custom webhook, and under âsend params?â, we enter the data from Akkioâs API deployment.
The last âkeyâ field, named âdata,â is where we input the data to feed the Akkio model. The webhook should then look something like this:
Now, we can take the output of our webhook, and give it to the user.
By hitting âpreviewâ in the top-right of Landbot, we can test it out, and voila, it works! Users answer a series of questions, which gets sent to the AI model we made and deployed in Akkio, and a prediction is returned to the user.Â
In a matter of minutes, you can build an AI-powered, automated credit approval chatbot. This is revolutionary, because itâs an example of something much bigger: You can build and deploy AI models to predict practically anything in a number of minutes.
In late 2020, an AI startup called Zest raised $15 million for its AI-based credit underwriting software, for a total of over $230 million. In 2019, a similar AI lending startup called Upstart raised $50 million.
In short, AI-based credit lending is incredibly valuable, and it used to be incredibly difficult. Today, you can use no-code AI to accomplish wildly difficult tasks in a fraction of the time.
In this guide, weâve explored using Akkio, a no-code AI tool, and Landbot, a no-code chatbot builder, to build an automated credit approval bot for financial services.
However, the same principles and steps apply to a wide-range of use-cases. If you visit Kaggle Datasets, youâll find thousands of ways to apply no-code AI. For instance, you could use AI to predict corporate bankruptcy, credit card fraud, Kickstarter project success, customer churn, or any of a million other metrics. As long as you have sufficient, high-quality historical data, you can make a predictive model.
This guide can be used by any company that issues credit to make their processes more efficient, by improving customer service while cutting costs.
That said, this guide can be so much more. Consultants and agencies can use this guide to increase their repertoire of services, and offer AI-powered chatbots to their clients.
Entrepreneurs can use this guide to build an AI startup faster and cheaper than ever before, and intrapreneurs can use this guide to implement AI in their organizations, even without having any technical expertise.