Introducing Machine Learning into your Workflow

For this post, I want to enlist a little help from fellow Berkeley, California resident, Toro y Moi.

He recently used AI to create the EP, “Smartbeats” with the artists Washed Out, Madeline Kenney, Nosaj Thing, and Empress Of.

Why is Creativity with AI Important?

Because it proves some things:

  1. AI can be used as a creative collaborator.
  2. The artists used an AI tool (Endel) to set their music apart and create something unique.
  3. If AI can be integrated into Toro y Moi’s workflow, it can be integrated into yours.

We talked about the power of AI and machine learning in your workflow to create unique business solutions in a past post, but let’s talk about the steps it takes to become an AI collaborator, like Toro y Moi. 

Step 1: Understanding the Power of Machine Learning in Business

Referring back to that last post on an introduction to creative predictions, we concluded by saying “Creative Data Predictions Will Set You Apart in Your Industry.” 

Let me expand on that by using an example:

In 2020, experts predict credit card fraud to equal to $32 billion in damages for credit card companies. 

Quoting Rafael Pierre in a post on using ML to detect credit card fraud, “Financial fraud still amounts for considerable amounts of money. Hackers and crooks around the world are always looking into new ways of committing financial fraud at each minute. Relying exclusively on rule-based, conventionally programmed systems for detecting financial fraud would not provide the appropriate time-to-market. This is where Machine Learning shines as a unique solution for this type of problem.”

What he means is with the main challenge of predicting fraud is imbalanced data, where most credit card transactions are non-fraudulent. Credit card companies can look at past data and see it is extremely unlikely that they will be hacked and can even take a guess that 99% of transactions would not be fraudulent, but Rafael argues this prediction would “have no value” in business because it wouldn’t be correct or fault-proof. 

Your response to this post so far probably looks like this because it’s getting interesting:

Step 2: Getting Business Value Out of Data

Using this example further, I plugged in credit card data from a Taiwanese bank into the Obviously AI platform to see what factors go into a default payment for the next month and aimed to create a persona of the most likely users to have a default payment. This will create the most business value out of the data. 

Some results I concluded from this dataset are:

  • A customer that delays payments by 2 months is the most unlikely to pay again.
  • The most likely persona to have a default payment are customers around 22 years old, have graduate school education, and unmarried, indicating the least likely to pay are the younger customers who might be a post-college graduate. Inversely, the least likely to have a default payment is around 71 years in age, married, and have an unknown education level.

This is valuable information to predict default payments and create business solutions around this data.

Step 3: Implementing Machine Learning Into Your Workflow

Okay, now to the good stuff. But first, a dance move from Toro y Moi for a breather.

It’s important to note you don’t need technical skills to use machine learning in your everyday tasks. Obviously AI doesn’t require a technical background and is made for non-techies. But, it is important to know the possibilities of machine learning and predictive analytics to get business value out of the technology. Simply connect your data and ask questions about your data in a question format. 

Be More Like Toro y Moi

Going back to the Toro y Moi example, he used an AI tool to be creative because he recognized the usability in it to create a solution. ML is a collaborator and if you fail to integrate it into your workflow, you may fall behind. 

Don’t fall behind.

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