Explain Like I’m Five: The Non-Nerdy Guide to Machine Learning in Order to Cash

When you search for directions on Google Maps, along with the list of possible routes to reach your destination it also suggests you the fastest possible route. Try doing the same in offline mode and the result is a list of possible routes but no recommendation on which one you should take. The Machine Learning algorithm of Google can make decisions that simplify your life but only when provided with real time traffic DATA collected via internet.


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2019-03-20
10:00 AM - 11:00 AM EST

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When you search for directions on Google Maps, along with the list of possible routes to reach your destination it also suggests you the fastest possible route. Try doing the same in offline mode and the result is a list of possible routes but no recommendation on which one you should take. The Machine Learning algorithm of Google can make decisions that simplify your life but only when provided with real time traffic DATA collected via internet.

Can Machine Learning (ML) in Order to Cash (OTC) also make such useful predictions when equipped with the right data?

After all, there is no dearth of data on customer behaviour, payment patterns and ordering history, etc. Could ML start making sense of all this information to make useful predictions such as likely delay in payments, validity of customer claims, missing remittance values or increased customer risk? The answer is YES.