Combatting Fraud and Cyber Crime with Python

Combatting Fraud and Cyber Crime with Python

Why am I writing this article? Combatting Fraud and Cyber Crime with Python

Why am I writing this article?

My passion has always been in fraud analytics. I just think it’s really interesting how you use that data and what writing some clever code can mean for a company’s bottom line.

As a data engineer, I’m constantly surrounded by data, in some point of the spectrum. Could be the quality of the data — are the data types correct, are they optimally stored; could be the manipulation of data — analysis and modeling; could be presentation of the data — report/dashboard building; could be the delivery of the data — is it in the right location, data warehouse, database types of thing; whatever it may be, I’ve worked on it. But using data to find fraud — it’s like using data to build a puzzle. It’s awesome.

So, What is Fraud?

Or at least, what do people mean when they say “we’re looking for a fraud or risk analyst”? Depending on your industry, it usually means digital fraud; things like CNP (card not present) fraud, account takeover — basically extracting cash off of a stolen credit card. There are bigger definitions, but you didn’t come here for a history lesson. You came here for the code, am I right?

How do they do it?

Not going to get into this too much either — but there are deep corners of the internet where you wouldn’t want to even visit unless you were completely hidden. And by hidden I mean using a vpn, an encrypted browser and it would be best not to do it on your personal device.

Ok, Let’s Get into it

#first thing's first
import pandas as pd
import itertools
import plotly.express as px

python pandas

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