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Artificial Intelligence for Acquiring Card Payment System

 
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Hi Noah,

How Pragmatic AI book will help to build Artificial Intelligence for Acquiring Card Payment System. This is with respect to Payment Systems in Financial Transactions. Can we build a prediction system which will tell us which type of transactions will give us more rewards points.

Best Regards,
Ninad Kulkarni
 
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What is or does a "Acquiring Card Payment System"? What does it have to do with reward points?
 
Ninad Kulkarni
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Tim Moores wrote:What is or does a "Acquiring Card Payment System"? What does it have to do with reward points?


Acquirer passes Merchant transaction to issuer bank who issue card to card holder. Reward point gets calculated on amount of transaction done on merchant terminal for specific scheme or program which can be loyalty program.
 
Ninad Kulkarni
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Tim Moores wrote:What is or does a "Acquiring Card Payment System"? What does it have to do with reward points?


Acquirer passes Merchant transaction to issuer bank who issue card to card holder. Reward point gets calculated on amount of transaction done on merchant terminal for specific scheme or program which can be loyalty program.
 
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In the example shown there are a couple of "pragmatic" approaches that could tried first, ie. in the spirit of using the highest level tools first. I would approach the problem like this:

Step 1:  Spin up an AWS Sagemaker Instance
Step 2:  Create a Jupyter Notebook
Step 3:  Clean up the data and do some clustering and see if there are some clusters that create types of transactions and plot (with on axis being transaction size)
Step 4:  Add the labels to the original data set and predict which cluster something could be assigned to and deploy via Sagemaker (to either production or other team members via about 1 line of code)

Off the top of my head a workflow like this could be a rapid way to approach the problem
 
Ninad Kulkarni
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Noah Gift wrote:In the example shown there are a couple of "pragmatic" approaches that could tried first, ie. in the spirit of using the highest level tools first. I would approach the problem like this:

Step 1:  Spin up an AWS Sagemaker Instance
Step 2:  Create a Jupyter Notebook
Step 3:  Clean up the data and do some clustering and see if there are some clusters that create types of transactions and plot (with on axis being transaction size)
Step 4:  Add the labels to the original data set and predict which cluster something could be assigned to and deploy via Sagemaker (to either production or other team members via about 1 line of code)

Off the top of my head a workflow like this could be a rapid way to approach the problem

Thanks a lot Noah
 
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