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2 min read

How A.I. and Machine Learning Can Combat Fraud

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Mention the words artificial intelligence (A.I.) or machine learning around human workers, and they get noticeably uncomfortable. The fear of robots and machines taking their jobs isn’t far-fetched. After all, automation has made many positions over the years obsolete.


While machine learning is going to impact you, it won’t take your job. In fact, A.I. and machine learning can help mitigate a huge problem that’s plaguing everyone: ad fraud.  


How A.I. and Machine Learning Works 101

In its simplest terms, A.I. is a machine, software, or bot simulating an action, typically human. For example, an A.I. chatbot can simulate human language, enabling it to interact with a customer to solve a problem.

Artificial Intelligence

Source: Washington Examiner


For A.I. to make good decisions, it needs valid machine predictions as input. Here’s where machine learning comes into play. Machine learning (e.g. IBM's Watson) analyzes large chunks of data that teaches a machine how to do a task. Essentially, machine learning teaches A.I. The key difference between the two is A.I. makes decisions, machine learning makes predictions.


How Companies Are Using Them to Fight Fraud

By using A.I. and machine learning, companies can potentially strengthen their anti-fraud arsenal. Here are some ways companies are currently using A.I., machine learning, or the two together:  


To Fight Money Laundering. PayPal is boosting their security with A.I. To stay ahead of fraud, PayPal relies on real-time analysis of transactions. When a pattern is spotted, it’s turned into a “rule” that can be applied in real-time to stop any purchases that fit that profile.


With A.I. PayPal can tell the difference between a customer buying an expensive vacation package and a fraudster making a similar purchase with a stolen credit card number.  


To Safeguard Cloud Storage. With the cloud being prone to attacks, Amazon is taking steps to protect sensitive data stored in AWS. Their product Amazon Macie uses machine learning to discover, classify, and protect information stored in the cloud.

Amazon Macie

Source: Amazon Web Services


For example, if Amazon Macie spots a user from a different IP address accessing a doc, it alerts the customer as a fraud stalling tactic.


To Reduce False Positives. By using big data to identify new fraud patterns, the financial and banking sector is attempting to reduce false positives (e.g. credit card declines).


To Protect Company Logos. Trademark Vision, a computer vision and machine learning technology company in Uptown, has entered the business of visual trademark searching. It protects brands from copyright infringement.


What’s Next for A.I. and Machine Learning   

For companies looking to protect themselves and their clients from click fraud and other forms of ad fraud, A.I. is anticipated to be the big weapon in the fight against fraud this year. But to fight well, large scale machine learning needs to be incorporated, too.


Right now, machine learning can be difficult to build in-house, which is why most companies favor A.I. But as machine learning systems evolve, it’s expected they’ll require less data to “learn,” which enables them to learn even faster with smaller data sets. Hopefully, this will make the fight against fraud more efficient.


While A.I. and machine learning won’t be taking your jobs, they will be taking fraudsters’ jobs. And that’s not a bad thing.   New call-to-action